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Office densification effects on comfort, energy, and carbon lifecycle performance: An integrated and exploratory study
Journal Pre-proof Office densification effects on comfort, energy, and carbon lifecycle performance: an integrated and exploratory study E. Hoxha, C. Liardet, T. Jusselme
PII:
S2210-6707(20)30019-6
DOI:
https://doi.org/10.1016/j.scs.2020.102032
Reference:
SCS 102032
To appear in:
Sustainable Cities and Society
Received Date:
21 July 2019
Revised Date:
23 December 2019
Accepted Date:
23 December 2019
Please cite this article as: Hoxha E, Liardet C, Jusselme T, Office densification effects on comfort, energy, and carbon lifecycle performance: an integrated and exploratory study, Sustainable Cities and Society (2020), doi: https://doi.org/10.1016/j.scs.2020.102032
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier.
Office densification effects on comfort, energy, and carbon lifecycle performance: an integrated and exploratory study Hoxha, E.1, Liardet, C.1, Jusselme, T.1 1
Building2050 Research Group, Ecole Polytechnique Fédérale de Lausanne (EPFL), Fribourg,
Switzerland
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Corresponding author Endrit HOXHA (PhD, M.Sc, Dip-Ing) Ecole Polytechnique Federale de Lausanne Building 2050 Research Group smart living lab, Halle Bleue, Site de blueFACTORY Passage du Cardinal 13B CH – 1700 Fribourg
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Highlights
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e-mail : [email protected]
This research measured the environmental impacts of office densification.
Densification did not significantly impacted the occupant’s comfort.
Environmental impacts (CED, CEDnr, GWP) were reduced by almost 50%.
A new functional unit "eq-nominal people per effective presence" is introduced.
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Abstract
Generally, the environmental impacts of buildings are benchmarked per square meter as a functional unit. However, this practice prevents developing a user-centered approach in which
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performance is linked to real usage and not building size. Currently, the less a building is used, the lower the energy consumed during its use and, consequently, the impact per square meter, which does not make sense regarding both usability and environmental performance. Following user-centered design principles, the goal of the present research is to assess the environmental impact of buildings based on novel user-based functional units and to understand the environmental impact consequences of office occupation density. An experiment within the area of an academic office offered the opportunity to test the densification of working spaces
and evaluate the resulting environmental and comfort impacts. In the end, the new user-based functional units highlight a reduction of all environmental indicators by almost 50%. Also, the functional unit "eq-nominal people per effective presence" is introduced as most suitable to evaluate environmental performance according to real building usage, as a complement to square meter function units.
Keyword: Office densification; User-centered assessment; Life cycle assessment; functional
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unit.
1. Introduction Urgent action is necessary for mitigating greenhouse gases worldwide, especially in the construction sector, as it is among the main CO2-eq emitters. This sector must be carbon-neutral by 2050 to achieve the targets set by the Paris Climate Agreement (IPCC, 2014; Rockströmet et al., 2017; IPCC, 2018). Although implementing active and passive energy strategies (Lehmann, 2013; Monteiro et al., 2016; Gao et al., 2018; Saretta et al., 2019) with environmental-friendly materials has drastically reduced the impact of this sector (Jelle et al., 2010; Shoubi et al., 2015; Qoraut, 2017, Shen et al., 2018), additional solutions are necessary for achieving sustainable use of raw
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materials and energy (Jusselme et al., 2016). The life cycle assessment (LCA) method has been largely used for evaluating environmental impacts. It consists of four steps: goal and scope definition, life cycle inventory, environmental impacts assessment, and interpretation of results (ISO-14040, 2006). The first non-technical step of LCA consists of defining a functional unit, which is crucial since on it are all inventory
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data and impact evaluations normalized (Weidema et al., 2004; Hottenroth et al., 2018). Square meter of net-, gross-, or energy-floor area or per capita metric are the common functional units
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used by the scientific community and practitioners for assessing the environmental impacts of buildings (Peuportier, 2001; Lebert et al., 2012; Thiers and Peuportier, 2012; Heinonen and
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Junnila., 2014; Rashid and Yusoff, 2015; Lasvaux et al., 2017; Hoxha et al., 2017). In addition to these units, the European norm EN-15978 (2011) recommends the use of units per-building piece, -year, or -person. These units are used extensively for the evaluation of the environmental
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impacts of buildings (De Wolf et al., 2017).
Although these units are generally accepted by the scientific community and largely considered in evaluating the environmental impacts of buildings, they link the construction performance to
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the building size.
Indeed, an almost empty building might have a low environmental impact per square meter, but
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a very high impact per user. Thus, it is difficult to benchmark the performance of buildings in use if their intensity of use is not considered; i.e., their occupation rate. However, surface functional units prevent the development of a user-centered approach where performance depends on effective usage. Motivated by this knowledge gap, the first aim of this study is to introduce a novel functional unit capable of assessing the environmental impacts of buildings according to their usability in addition to the existing square meter units. This new user-centered functional unit might allow
professionals to consider the densification of building spaces as a possible strategy for minimizing the impacts of building projects. Densification of spaces has been found to be a robust solution for the minimization of environmental impacts (Resch et al., 2016). At building scale, the densification is studied through the occupancy behavior, which has a crucial influence on the operating energy of the building (Hong et al., 2016; Ekwevugbe et al., 2017; Capozzoli et al., 2017; Jafarinejad et al., 2019). Aiming for the minimization of the energy used during the operational phase of the building, Hong et al.’s (2016) study recommended the collection of good and adequate data for
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understanding the behavior of occupants. In an office building situated in Netherland, Capozzoli et al. (2017) measured the occupancy profile of employees in the different thermal zones. They proposed an optimized schedule in considering the variability of occupancy profiles. Due to this improvement, it was possible to reduce by 14% the energy required by HVAC systems that were operating with fixed schedules. With an identical scope, Peng et al. (2017) reported 7% to
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52% energy savings for cooling systems. Curto et al. (2019) investigated energy-saving measures for the ventilation system of a large shopping center. Jafarinejad et al. (2019) found
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a saving potential of around 19% for the operational energy of a building by proposing a more optimal occupancy profile. Pisello et al. (2012) and Erickson et al. (2014) also highlighted the
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necessity of real-time occupancy data in order to achieve energy-savings. Staff presence in a university building was investigated via questionnaire in a pilot study presented by Gul and Patidar (2015). Their conclusion found that only 8% were academic staff with permanent
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working places and the remaining 92% of the users were visitors. Zhao et al. (2014) also developed a data mining method to learn building occupant behavior and observed the impacts of different occupation schedules on energy consumption. The influence of user behavior in
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energy use during the operational phase of the building was also evaluated by Naylor et al. (2018). Moreover, D´Oca et al. (2018) uses an extensive literature review to show that the
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influence of the human dimensions in the energy performance of buildings is as significant as that of advanced technologies. Based on this literature review, it is evident that occupant behavior significantly influences energy consumption during the operational phase of the building. However, two aspects of occupant behavior and, more precisely, of building densification, have never been assessed. Assessed performance indicators were limited to the energy consumption during the operational phase of the building and never extended to the entire building lifecycle performance by also considering the embodied impacts. Occupant densification has always
been considered a contributor to energy consumption and never as a strategy to increase building performance. Hence, the second objective of this research is to assess building densification efficiency regarding lifecycle performance through a case study. As densification might impact user comfort, this parameter will also be monitored to ensure that environmental benefits will not be offset by user discomfort.
2. Method To address the problem presented, the current research implemented a methodology in four
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distinct steps according to the following schema (Figure 1). The first step sketched out the whole project and identified the offices where the experiment would take place and their layout for both phases. Moreover, the intended participants were identified and protocols for the evaluation of their comfort, tools for tracing their presence in offices, and equipment for measuring energy consumption were determined.
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The second phase was dedicated to the collection and analysis of the energy consumption data, comfort perception, and presence of employees in the work spaces. The third phase of the data
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collection was used for the evaluation of the environmental impacts of two offices where the experiment took place. Since it was not possible to include into the study boundary the
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environmental impacts of several building components at office scale (e.g., foundations, corridors, etc.,), in the last phase of the methodology the data collected were generalized at building scale. During this phase the environmental impacts of the buildings were assessed,
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from which conclusions were drawn.
2.1.Design of the experiment
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The objective of the present research is to introduce a novel functional unit to drive the evaluation toward user-centered design principles, and then to assess the potential benefits of
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densification strategies. However, the densification of building areas influences perceived comfort for its users, and a real occupation dataset is hard to gather. Therefore, a field experiment is judged necessary to answer our research question. According to Figure 1, the methodology is a two-phase experiment: Phase-A or pre-densification, and Phase-B or postdensification. During the pre-densification phase of the experiment the layout of offices and working conditions must not change. In the post-densification phase, the goal is to increase occupancy rates by increasing the population within the same boundaries. To do so, the layout
of offices has to change to welcome a greater number of users while maintaining their working comfort. Then, the goal is to monitor each phase in terms of comfort, density, and energy consumption. The experiment boundaries, its total duration, and specific conditions must be defined to represent the real world to scale the results at the building level. Indeed, the boundary of the study under evaluation should be accurately chosen to include details and represent the building itself. The time span of the experiment is defined based on the minimal representative time for the year and the largest time tolerable by employees to remain under observation. Considering
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both conditions, one month per phase can be considered the minimum suitable duration.
2.2.Measurements and evaluations
Over the course of the experiment, several parameters - such as user presence, comfort perception, and energy consumption - must be measured. Moreover, an inventory of materials employed in office construction and those used for furniture and appliances must be quantified.
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Finally, all these energy consumers and materials must be converted in terms of environmental impacts for both phases of the experiment.
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2.2.1. Presence measurement
One of the key points of this experiment is the ability to monitor the effective presence of
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employees within the experimentation boundaries. As the experiment must be performed over two months to be representative (one month per experiment phase), counting based on observation is not a realistic option. Verma et al. (2017) propose a numerical infrastructure
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where each experiment participant can be provided with a bracelet that broadcasts a Bluetooth signal based on a per-second time step. In each room, one data logger receives the bracelet signals to determine each employee´s location and presence duration. The signals recorded in
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data loggers were then uploaded and analyzed in a separate Excel file. During 24 hours on a per-second time step an Excel cell for each employee contained the number 1 or 0. The number
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1 highlighted the presence of the employee and the number 0 his absence.
2.2.2. Behavior perception A monitoring protocol must be set up to follow employees’ emotional responses to the densification. The self-assessment manikin technique (Bradley et al., 1994) can be used during the pre- and post-densification phase. This method is easy to implement, inexpensive, and already widely used by researchers. At the end of their workday, all employees completed a
Pleasure-Arousal-Dominance (PAD) questionnaire (Figure 2), grading their emotional states using a non-verbal pictorial technique. The grade of parameters ranged from 1 to 9. For the pleasure parameter, grade 1 meant unpleasant and grade 9 meant very pleasant. For the arousal parameter, grade 1 meant sleepy and grade 9 meant very excited. For the dominance parameter, grade 1 meant very dominant and grade 9 meant not dominant.
2.2.3. Energy consumption measurement Energy consumption must be monitored using sensors. All electrical equipment used by the participants was monitored thanks to individual Z-Wave energy meters directly plugged into
the energy consumption of electrical appliances and lights.
2.2.4. Environmental impact assessment
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each socket and their data collected through a Raspberry Pi. This allowed the measurement of
Embodied impacts of office, furniture, and appliances, as well as the environmental impacts of
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energy required for lighting, must be evaluated and considered in the boundary of the study. Five functional units are proposed to test the impact evaluation: energy reference area (ERA) per year;
number of employees evaluated according to the Swiss norm SIA-2024, (2015);
real number of employees;
full-time-equivalent employees according to their administrative contract;
and effective-full-time-equivalent employees, according to their effective presence.
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Although practitioners and researchers (Peuportier et al., 2001; Rashid and Yusoff, 2015; Lasvaux et al., 2017; Hoxha et al., 2017) generally use the first functional unit (i.e., ERA), this cannot highlight the influence of a densification strategy on the environmental impacts of
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building, as its surface will remain the same before and after densification. The second functional unit has the same issue: the number of employees per office is evaluated recognizing
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a ratio of 14 m²ERA per employee according to the SIA, which does not allow the possibility of changing the density in the assessments. In order to provide a complementary approach, we introduced three other functional units based on the real employee count. These three new units have different precision levels, from contract- to reality-based counting. First, the real number of employees is a basic count of the number of people using the building. The real number of employees refers to the people employed by the institution and are expected to work in the offices (ex: four real employees are counted even though their work contracts are respectively 100% full time; 40% part-time; 50% part-time and 60% part-time). For this reason, the second
unit, a full-time-equivalent employee, gives a better understanding of their possible presence based on their work contract. A new number of employees is considered by converting all parttime working employees to full-time. The conversion is made based on the percentage of their part-time work contracts. In this case the part-time working contracts are summed and converted to full time (ex: altogether the four employees present 100+40+50+60=250% or 2.5 full-time-equivalent employees). Employee mobility induces many absences for different reasons in the office for part or all day. This is the reason for introducing a third unit, which is highly accurate and measured with the Bluetooth bracelet as described in section 2.2.1. This unit is called effective-full-time-
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equivalent employees. Since the presence of full-time employees in the office is not 8 hours (ex: first employee stays in the office only 60% of his time; second employee 70%; third employee 50% and the last 90%), the adjusting conversion is made based on data measured from the Bluetooth bracelet. In the end, the number of effective full-time employees is calculated by multiplying the percentages of work contracts with their presence percentage; in
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the case of the example the results are (100x60)+(40x70)+(50x50)+(60x90)=167% or 1.67 effective-full-time-equivalent employees.
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Evaluating the environmental impacts of a building and its elements must follow the European norm EN-15978 (2011) and EN-15804 (2011), while assessment of furniture and appliances
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should fulfil the norm ISO-14040 (2006) since, until now, the evaluation of environmental impacts of furniture and appliance are not supported by specific norms. Including furniture and appliances in the study boundaries gives details about the evaluation of the building´s
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environmental impacts and can be found in Hoxha and Jusselme (2017). KBOB (2016) and the Ecoinvent database (Wernet et al., 2016) will be used for the assessment of the following three environmental indicators: cumulative energy demand (CED), non-renewable energy (CEDnr)
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(Bösch et al., 2007; Frischknecht et al., 2007), and global warming potential (GWP) (Intergovernmental Panel on Climate Change [IPCC], 2007). In Switzerland, these indicators
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are considered the most important for sustainable and equitable use of the world’s raw materials and energy according to the 2000-Watt Society vision (Jochem et al., 2004; SIA-D0236, 2011).
2.3.Generalization at building-scale level To evaluate the benefits of densification, the conclusions of the experiment have to be scaled to the building level. Doing so should overcome criticism about the limits of the boundaries of the field study by also considering impacts that might be not possible to monitor within the experiment’s perimeter, but also other building areas (hall, corridors, cafeterias, etc.). The
building-scale project should enlarge the concept of office densification at the building-scale level. Plans, the orientation of offices, and the surfaces of external windows and walls are criteria to consider. Energy consumption will be generalized through field measurements, and when no data is available they will be evaluated with the help of Design-Builder v. 5 dynamic simulation software (Crawley et al., 2001; Design-Builder Software v.5, 2018) following the base of the measurements during the experiment and the Swiss standard SIA 2024 (SIA-112, 2014; SIA-118, 2013; SIA-2024, 2015).
3. Case study Given the wide range of method application an interdisciplinary research team handled the
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experiment. Ecole Polytechnique Fédérale de Lausanne’s (EPFL) Building-2050 group implemented the experiment and developed the methodology for assessing environmental impacts. The Human-IST Institute of the University of Fribourg (UNI-FR) was responsible for measuring space occupancy.
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The methodology was applied within the spaces of two offices and two meeting rooms (Figure 3) situated within an academic building in Fribourg, Switzerland. This building was suitable
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for this intrusive experiment, as it is a living lab with occupants willing to participate. Also, energy meters were already available to measure the overall office consumption and its lighting
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factors.
Offices and meeting rooms were affected in the experiment since they composed the largest
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areas of academic buildings constituting the target. Used for the staff of two laboratories, each office and meeting room had a surface of 73 m2 of ERA. They had modular construction systems in which the principal materials employed were wood, concrete, and wood-wool.
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Triple-glazed windows with wood-aluminum frames were used for the openings. All building envelope elements had a thermal transmittance of 0.1 Wm-2K-1, except for the floor slab that
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was of 0.15 Wm-2K-1.
The post-densification layout was adapted from Myerson’s concept (Myerson and Bichard., 2016) of spaces for concentration and collaboration. Then, we proposed an activity-based work layout with what we called a “quiet room” and an “interactive room”. The quiet room targeted concentration and contemplation tasks without noisy activities or sources of distraction. The interactive room was designed for collaboration and communication tasks with spaces for sharing documents and the appropriate technological equipment to support remote collaboration. To increase the density, the desk-sharing concept was introduced by removing
the concept of private places since during a work day the employees may need to work in calm conditions or have discussions or meetings in loud voices. Consequently, all employees could choose and change offices during the day according to their schedules. Information about the materials and components used in offices during the pre- and post-densification phase are presented in the Supplementary Material section. The pre-densification phase of the experiment lasted three weeks, during employees in their private workplaces were supported by one laptop, one docking station, and one or two monitors. They had one private cabinet and at a minimum one metal tambour cabinet. When they were present at the office their place was always free and supported by these items.
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In the post-densification phase of the experiment, which lasted for four weeks, all employees were supported only with private cabinets in which they could put their personal equipment. In both offices, employees had to use their laptops. Unlike the pre-densification phase, all docking stations and metal tambour cabinets were removed in this phase of the experiment. Moreover, some workplaces were supported by monitors and some were not. Workplaces in the quiet room
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were also equipped with table lights. Except that loud discussions were not permitted in the quiet office no other rules were given to the employees. The interactive office was also
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supported with tables for meetings, in which employees could have short discussions, while the meeting rooms stayed unchanged during both phases of the experiment. These rooms were
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equipped only with tables, chairs, whiteboards, and projectors. These areas allowed for possible Skype discussions, long meetings with more than four employees, or private discussions. In post-densification, the number of employees working in offices almost doubled from the
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employees of the three other laboratories who were invited to work there. All differences between layouts of offices in the pre- and post-densification phases are presented in Figure 1. As highlighted, the concepts of offices were later generalized at building-scale by an archetype
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of the smart living building (Jusselme et al., 2016). Figure 4 illustrates the layout of one floor of this building. Hot water, cooling, and heating were not monitored within the experiment
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boundaries due to difficulties in implementing a separate count of these needs. Therefore, consumption differences before and after densification of these energy needs were disregarded. However, they were calculated according to SIA norms when scaling the results at the building level.
4. Results 4.1.Effective presence schedule
Figure 5 summarizes the presence schedule for both pre- and post-densification phases based on the data collected with the Bluetooth bracelets. These schedules are compared to what is proposed by Swiss norm SIA-2024 (2015), used by building designers to evaluate energy power and consumption for lighting, appliances, heating, and cooling. As shown, the results in Figure 5 highlight different densification rates between schedules. Thirteen employees worked in offices where the experiment took place. This number corresponds exactly to the number recommended by SIA-2024 (2015) norm (14 m2 energy-floor areas per employee), and the presence schedule in the pre-densification phase should be similar to that of SIA. Per contra, the real measurement of the pre-densification phase shows the occupancy rate is far
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below the schedule of design. The figure also presents the minimum and maximum presence of employees in all phases. The minimum and maximum correspond to a single day, which may not be the same for both. The important information is the maximum number of employees present in the office at the same time, which guides the number of working places the offices should have.
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There are two primary reasons for the differences between the SIA schedule and the one measured in the pre-densification phase. First, the academics (professors, researcher, PhD
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students, etc.) work in different places, maybe more than an average employee would. They
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spend time in meetings, conferences, or laboratories. Second, many researchers work part-time.
In the second phase of the experiment, the number of employees working in offices increased to 25. Based on the differences between measures of the pre- and post-densification phases with
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the presence schedule proposed by the SIA-2024 (2015), we conclude that in our case, using the standard rate of 14 m2 energy, floor area per employee would lead to substantial inaccuracies. Another interesting fact is that the real lunchtime phase is at 12:00 and not at
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13:00, as proposed by the norm. Also, some employees start work at 7:00 am, and some finish at 22:00 pm, highlighting the diversity of behaviors for each employee.
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Regarding the comparison between pre- and post-densification phases, we conclude that the number of employees increased by 92% (from 13 to 25), while the unit rate of full-timeequivalent employees increased by 73% (from 10.1 to 17.5), and the unit rate of effective fulltime-equivalent employees increased by 56% (from 3.57 to 5.56). The unit of effective fulltime-equivalent employees is calculated based on the graphics of office occupation in pre- and post-densification phases (the area under the graphic is divided by 8 hours). All the data about the number of employees, their percentage working contract, and the occupancy rate of offices with and without meeting rooms are given in the Supplementary Material section.
In the frame of this experiment, these functional unit differences lead to major variations in the calculations of the environmental impact when based on the effective presence, but are suspected to not change at all when based on the building surface or Swiss norms. However, densification will affect the building occupants’ comfort in many cases, and its consequences on working conditions must be limited and carefully evaluated.
4.2.Comfort perception Results of employee behavior in pre- and post-densification phases, evaluated with the help of the self-assessment manikin technique, are presented in Figure 6. The comparison between the
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answers received during pre-densification and post-densification are tested with the help of a ttest. This test shows the degree of differences between the two groups by comparing their average values (Garson. 2012). Based on the results obtained, we conclude there is not a significant difference (t-test is not significant at p < .01) in employee behavior between pre-
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and post-densification phases.
However, the intervals of responses for post-densification are larger than those of pre-
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densification, especially for pleasure and arousal parameters, demonstrating the highest varieties in emotional states. To identify if this comes from the densification of spaces, in Figure
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7 we present the average values of responses for the days surveyed in pre- and post-densification phases. The first conclusion drawn is that employees’ comfort was not influenced by the densification of spaces, as it is not possible to recognize a different pattern for each stage. Even
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though the densification rate was higher in the second phase of the experiment, the grades of each PAD parameter were not lower.
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Figure 8 presents more detailed results from the questionnaire for the parameters of pleasure and arousal. As noticed in the participant answers, the behavior perception was pleasant, except
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the day on which the experiment passed from pre- to post-densification, with all the logistical consequences involved. Another interesting result from the questionnaire is the employees’ responses regarding the quiet room, in which they felt calm, and in the interactive room, where they felt excited. Separating the offices into calm and interactive areas was the primary goal of the experiment. As a conclusion, we notice that the densification of offices did not induce any significant decrease in working comfort, but affected the arousal states according to the room in which the participants choose to work.
4.3.Environmental impacts Environmental impacts for CED, non-renewable energy (CEDnr), and GWP at room-scale are presented in Figure 9. Impacts are composed of four groups: building (A1-C4), equipment (A1C4), appliances and furniture, and use phase (B6). Embodied impacts from cradle-to-grave for a building´s components are included in the first group, for equipment in the second group, and for the appliances and furniture in the third. The last group considers the environmental impacts of the use phase (lighting and appliances). Based on the m2ERA/yr as the functional unit, the densification strategy of spaces increases the environmental impacts. The increment in the post-densification phase is mainly due to the
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increment of furniture with higher impacts and the number of appliances (mostly laptops). However, even if the number of computers was doubled because of densification, the energy consumed by appliances in the post-densification phase was not doubled. This is because most employees did not choose to use monitors and worked directly on their laptops. Conversely, the energy for lighting in the post-densification phase was lower. This was mainly from the quiet
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room, where employees preferred to use a table lamp instead of the ceiling light.
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Figure 10 presents the impacts evaluated on other functional units to better identify the influence of the densification of workspaces on the environmental impacts of the building. As
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stated, during the design phase of the building, the number of employees according to the Swiss norm SIA-2024 (2015) was evaluated thanks to the ratio that each employee averaged 14 m2 of energy floor area. Based on this unit, the number of employees is linked to the surface, and it
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is impossible to see any variation in the densification strategy. This is also the conclusion drawn from the results in Figure 9. For the three other units the environmental impacts of offices decreased by 90% for CED and CEDnr and 83% for GWP. These differences in percentage are
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evaluated by using the equation: 100% - (pre-densification impact / post-densification impact) x100. However, for a better evaluation of the environmental impacts of offices, the different
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working percentage of employees is converted into full-time equivalent employees. Therefore, in this unit the decrement of the indicators of CED, CEDnr, and GWP are, respectively, 71%, 70%, and 45%. Yet, the real-time presence of employees is still not considered in these units. To that end, the indicators of CED, CEDnr, and GWP, due to the real densification of office areas, were respectively reduced by 58%, 57%, and 52% on the unit effective-full-time equivalent employees.
To better understand the equations used for the calculation of results presented in Figures 9 and 10, the following example gives the details for GWP indicator for the post-densification phase of the experiment. The overall impact for the GWP indicator for both offices and meeting rooms has the value of 5.68 t CO2e/yr.
Using the energy reference area (ERA) per year as a functional unit, the value of the GWP indicator is divided by 270 m2 (147 m2 offices and 123m2 meeting rooms) and the obtained value is 21 kg CO2e/m2 per year.
Using the number of employees evaluated according to the Swiss norm SIA-2024, (2015) as a functional unit, the value of the GWP indicator is divided by 10.5 employees
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(each employee will be associated with 14 m2 of office area) and the obtained value is 0.54 t CO2e/employees according to (SIA-2024, 2015) per year.
Using the real number of employees as a functional unit, the value of GWP is divided by 25 employees and the obtained value is 0.227 t CO2e/employees per year.
Using the full-time-equivalent employees according to their administrative contract as
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a functional unit, the value of GWP is divided by 17.5 employees and the obtained value
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is 0.324 t CO2e/full-time-equivalent employees per year.
Using the effective-full-time-equivalent employees according to their effective presence
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as a functional unit, the value of GWP is divided by 5.6 employees and the obtained value is 1 t CO2e/effective-full-time-equivalent employees per year. Details for the calculation of all functional units about the number of employees, their
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percentage working contract, and presence in the pre- and post-densification phase are given in the Supplementary Material section.
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4.4.Environmental impacts at building scale Figure 11 summarizes the environmental impacts of the smart living building based on the unit
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m2ERA/yr. The figure highlights the building life-cycle impact in four groups: building (A1-C4), equipment (A1-C4), appliances and furniture, and use phase (B6). Each of these groups are further composed into building components and systems in the bottom part of the chart. Since the building is a generalization of offices as presented in the previous section, normally the results should have the same trend. Impacts in the post-densification phase are higher for CEDnr and GWP, but lower for the CED indicator. The results obtained for the indicator of CED highlight the effect of the non-heated area in the smart living building. Since normalization to the number of employees depends only on ERA, the increment of the impacts of building due
to non-heated areas does not follow the same trend as does normalization. As a result, the numerator (environmental impacts) increases with a different power than the denominator (number of employees). For the CED and CEDnr indicators, the use phase contributes, respectively, 44% and 50% in pre-densification of the experiment. the impacts are the largest contributors for the GWP indicator, with around 86% in both phases of the experiment. However, the impacts between the two phases of the experiment in almost all phases are not significantly different. In the postdensification phase the appliances and furniture have higher impacts than those in the predensification phase.
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This can be justified by the layout of the offices in the post-densification phase changing with furniture of better quality and, consequently, with higher impacts. Moreover, the number of appliances, especially laptops and monitors, is another reason for the increment of the embodied impacts of appliances, which share a larger impact compared to furniture.
Within the embodied impact of building elements (A1-C4), horizontals (slabs and roof) make
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the most substantial contribution, followed by verticals (external envelope). Embodied impacts of the ventilation system and electrical equipment represent the largest impacts in the group of
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environmental impacts of equipment (A1-C4).
lP
Figure 12 presents the environmental impacts of the smart living building on the unit of employees. Based on the number of employees evaluated according to the SIA-2024 (2015) norm, the results followed the same trend as those at room-scale, even for the CED indicator,
na
where the environmental impacts in the post-densification phase are higher. As stated previously, the results make the m2 ERA/yr. unit less appropriate for evaluating the benefits of densification. For all other proposed indicators, units highlight benefits of up to 50%.
ur
For the real-employees unit the results show a minimization of CED, CEDnr, and GWP by 93%, 92%, and 83%, respectively. For the full-time-equivalent-employees unit the decrement
Jo
of indicators of CED, CEDnr, and GWP are, respectively, 73%, 72%, and 64%. For the effective-full-time-equivalent-employees unit the minimization of indicators CED, CEDnr, and GWP is evaluated at 60%, 59%, and 52%, respectively. Each of these decreases is mainly due to the number of employees using the office that doubled.
Between the three user-centered functional units, the one-per-employee is the easiest to calculate as it does not need any tracking technology. However, it overestimated the benefits, as the real presence of employees is far below 100% effective presence. Even the second unit
full-time-equivalent employee does not reflect the effective presence of employees, as made by the last unit. Finally, the results obtained for the smart living building are similar to those obtained at roomscale by strengthening the robustness of the conclusions.
5. Discussion The results presented in this article can be useful during the use phase of the building. This statement came as a result of an experiment in the offices of an academic building; the results previously presented can only be useful for this type of building typology. Although this
ro of
statement is almost true for the results, it is not true for the methodology, which can feasibly be applied to other building typology. The importance of this type of experiment is dictated by the large differences between presence schedules proposed by SIA-2024 (2015) and those measured during the experiment. This gap shows the impossibility of the presence schedule recommended by the norm to accurately cover all office building typologies. Based on that, we
-p
recommend our presence schedule for a more accurate evaluation of energy for heating, cooling, lighting, and appliances only for the case of academic buildings, and additional
sectors, like production and industry.
re
experiments for other building typologies since the results presented are not valid for other
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The presence schedule proposed in this article was measured for one month. This time span can be considered a criticism for a robust representation of the whole year, but was the largest time employees agreed to stay under observation and participate in the experiment. Their choice is
na
fully understandable since on each day they were obliged to wear the bracelet (for tracing their position), to come from other universities to work in the office where the experiment took place (the previous offices of half the participants included in the post-densification phase of the
ur
experiment were not in the building where the experiment took place), and to reply to the questionnaire every day. According to expert judgement, the phase of one month per each phase
Jo
of the experiment was found to be robust and representative. Comparing the presence schedule measured for a whole year by Menezes et al. (2012) and Duarte et al. (2013) with those presented in this manuscript (Figure 5), we observed they are similar for the post-densification phase. Of note is that the curves presented in (Menezes et al., 2012; Duarte et al., 2013) compared with that presented in this study (Figure 5) differ from each other only in how they are distributed over 24 hours. This comparison strengthens the conclusion that the phase of one month was sufficient for measuring the rate of presence of employees in their offices.
The results of the comfort perception questionnaire are another point deserving discussion. Among several methods and techniques recommended by the scientific community for evaluating working comfort, we set up a PAD questionnaire. The limitation of this method for a detailed evaluation of working comfort should be noted. However, this technique is the most relevant for a simple, generally accepted, and rapid daily evaluation of behavior perception. Moreover, evaluations of behavior perception are not biased, since all employees answered the questionnaire without knowing the objective of the experiment, which was the densification of workspaces. The employees of one laboratory applied this concept to their office after the experiment, which deserves to be highlighted and reinforces the validity of the PAD survey.
ro of
This allows for confirmation that the results of working comfort are robust. Assessing impacts at the office room-scale presents limits in the boundary of the study evaluated and allocation issues. For this reason, the assessment was also applied at building scale. Since the results obtained at the office room and building scale were almost alike, criticism of the boundary of the study and allocation of impacts is no longer valid.
-p
We demonstrated that the functional unit energy floor area per year, which is generally used by researchers (Peuportier et al., 2001; Rashid and Yusoff, 2015; Lasvaux et al., 2017; Hoxha et
re
al., 2017) and recommended by European norm EN-15978 (2011) for evaluating the environmental impacts of the building, was insufficient. For this reason, we introduce a
lP
complementary functional unit full-time-equivalent employee, which is most suitable for the assessment of environmental impacts of a new project during the design phase. This unit is more general and allows a more robust evaluation. However, it should be accepted that it is not
na
easy to know the real number of employees and their percentage of work time during the early design phase of a building. This can be improved by statistical studies in similar buildings or by a short tracking campaign of the targeted population.
ur
Although the presented research primarily has the character of an exploratory study, several points are validated and can be used for the improvement of Swiss norm SIA-2024, (2015) and
Jo
European EN-15978 (2011) to improve the robustness of the evaluation of the results. In addition, the results presented in this manuscript are valid only for offices.
6. Conclusion Results presented in this article show the effectiveness of space densification for minimizing a building’s environmental impacts. Through a real-time experiment, we demonstrated the possibility of densification of working areas while limiting impacts on employee comfort. Separating the academic office building into calm and interactive shared spaces might enable
densification of spaces by almost doubling the number of employees. Although this separation increases the embodied impacts of the new layout due to the requirement of high-quality furniture and more electrical equipment, the overall environmental impacts of the building per employee are greatly minimized. Moreover, novel units introduced for the first time evaluated the improvement of the environmental impacts of a building. In the unit of full-time-equivalent employees due to densification of spaces, the indicators of cumulative energy demand, non-renewable energy, and global warming potential were minimized by 71%, 70%, and 58%, respectively. To strengthen the reliability of the results, this functional unit is suitable for use in the early design
ro of
phase of the building project - if the future building occupants are already known at that stage. As for the unit effective-full-time equivalent employees, the indicators of cumulative energy demand, non-renewable energy, and global warming potential were minimized by 58%, 57%, and 52%, respectively. This unit suits mostly for a better evaluation of the environmental impacts of buildings during the use phase. This unit is more suitable for increasing accuracy in
-p
evaluating the performance of the environmental impact of building stock.
Surface-based functional units are more conveniently used at the early beginning of the design
re
process, as surfaces are always easy to measure. However, they cannot demonstrate or support a densification strategy improving the building performance. Thus, this paper demonstrated the
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relevance of using user-based units to have a better benchmark of building performances in use. This exploratory study calls for future research regarding a more holistic understanding of the comfort consequences of office space densification. Also, easy-to-use user counting techniques
na
must be developed, facilitating real occupancy rate monitoring.
ur
7. Supplementary Material data
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Data about employees
Necessary data for the functional units
Office scale Pre-densifi Post-Densif
Building scale Pre-densifi Post-Densifi
270
5255
10,5
227
Energy reference area (m2) Number of employees evaluated according to the Swiss norm SIA-2024, (2015) Real number of employees Full-time-equivalent employees according to their administrative contract
13
25
234
450
10,1
17,5
182
315
Effective-full-time-equivalent employees according to their effective presence
5,56
64
100
Post-Densification
Employee 1
100%
100%
Employee 2
100%
100%
Employee 3
60%
60%
Employee 4
100%
100%
Employee 5
100%
100%
Employee 6
50%
50%
Employee 7
50%
50%
Employee 8
50%
50%
Employee 9
100%
100%
Employee 10
100%
Employee 11
80%
Employee 12
80%
Employee 13
40%
Employee 14
-
Employee 15
-
Employee 16
-
Employee 17
-
Employee 18 Employee 19 Employee 21 Employee 22
ur
na
Employee 23 Employee 25
80% 80% 40%
-p
20% 20% 40% 50%
-
100%
-
100%
-
100%
-
80%
-
80%
-
50%
-
50%
-
50%
Time.Interval
00:00-6:00
7:00
8:00
9:00
10:00
11:00
12:00
6-March
-
0,40
3,68
5,08
3,42
5,38
4,35
7-March
-
0,55
4,50
4,45
4,37
5,05
3,10
8-March
-
0,57
2,18
2,13
2,75
2,30
0,98
9-March
-
1,62
4,48
3,70
4,45
4,32
3,50
10-March
-
1,07
3,30
3,92
0,85
1,00
1,70
13-March
-
0,57
4,80
5,03
3,85
4,45
3,37
14-March
-
0,83
3,68
4,68
5,50
0,05
1,75
15-March
-
0,60
2,90
4,72
4,47
2,90
3,43
16-March
-
0,78
3,93
5,90
5,57
3,68
2,40
Jo PRESENCE DURING PREDENSIFICATION
100%
lP
Employee 20
Employee 24
ro of
Pre-Densification
re
Working time percentage
3,57
0,40
2,35
3,42
3,37
3,58
2,93
20-March
-
0,43
1,87
3,30
5,25
4,70
2,40
21-March
-
1,05
4,42
4,68
2,88
4,93
2,43
22-March
-
0,58
3,45
4,30
5,05
4,23
2,77
23-March
-
0,60
3,83
4,87
3,88
4,48
1,65
24-March
-
0,37
3,07
4,48
3,02
3,67
2,57
27-March
-
0,00
0,00
0,00
0,95
1,58
0,27
28-March
-
0,00
1,33
2,75
2,93
1,58
0,00
10-April
-
0,45
2,30
4,05
4,58
4,77
1,32
11-April
-
0,20
1,42
1,77
4,40
5,72
3,73
12-April
-
0,72
2,32
2,38
2,45
2,05
0,22
13-April
-
0,33
1,48
1,20
3,17
3,27
1,05
14-April
-
0,00
0,00
0,00
0,00
0,03
0,05
18-April
-
0,53
1,98
3,25
4,52
3,78
1,50
19-April
-
0,00
0,28
1,83
4,40
5,97
3,17
20-April
-
0,42
2,12
2,35
1,68
1,87
2,35
21-April
-
0,43
2,30
2,60
2,43
1,77
1,55
22-April
-
0,02
0,15
0,15
0,02
0,02
0,08
24-April
-
0,48
2,68
25-April
-
0,47
1,79
26-April
-
0,72
1,53
27-April
-
0,45
28-April
-
0,63
1-May
-
0,00
2-May
-
3-May 4-May
-p
ro of
-
4,02
3,52
2,02
3,35
2,63
2,20
2,31
2,70
3,26
2,67
1,90
1,97
0,98
1,95
3,03
2,04
1,95
1,61
1,40
1,72
1,48
0,70
4,03
3,55
3,42
2,73
0,37
2,37
4,67
5,10
0,85
0,15
-
0,77
2,75
4,92
5,17
5,88
1,65
-
0,42
4,68
7,45
7,62
7,58
3,53
-
0,37
3,85
5,78
5,67
6,47
2,97
-
0,42
2,42
4,12
4,08
3,03
2,52
-
1,73
4,97
5,95
5,85
6,35
2,70
-
0,60
4,98
6,28
6,52
6,72
2,22
11-May
-
0,43
3,83
5,47
4,02
5,25
3,43
12-May
-
1,15
3,78
3,88
4,60
4,78
2,17
15-May
-
0,38
3,75
5,93
7,85
8,92
1,18
16.May
-
0,75
2,23
3,27
4,87
3,58
1,32
17-May
-
0,32
1,25
3,73
5,07
5,52
2,62
18-May
-
0,50
2,70
5,52
6,20
3,12
0,70
19-May
-
0,70
2,12
2,68
4,23
2,60
1,13
22-May
-
0,00
0,45
3,08
3,72
7,22
2,55
23-May
-
0,00
0,00
2,03
5,33
5,57
1,08
24-May
-
0,20
1,97
5,93
6,42
6,07
3,22
26-May
-
0,00
0,47
2,68
2,92
3,65
1,95
8-May 9-May
lP
ur
10-May
na
5-May
re
3,37
Jo
PRESENCEDURING POST-DENSIFICATION
17-March
14:00
15:00
16:00
17:00
18:00
19:00
20:00
21-23
6-March
5,40
5,17
2,57
4,22
4,47
0,72
-
-
-
7-March
2,90
3,32
3,72
3,48
4,00
0,28
-
-
-
8-March
2,08
2,62
2,62
2,55
2,37
0,58
-
-
-
9-March
4,98
5,02
5,07
4,72
3,28
1,88
1,35
0,32
-
10-March
3,07
3,80
3,22
3,02
1,55
0,28
-
-
-
13-March
2,15
3,43
3,05
3,30
2,75
1,27
0,20
-
-
14-March
4,75
2,77
5,32
4,62
2,55
0,18
-
-
-
15-March
3,45
4,65
3,45
3,80
4,15
1,12
-
-
-
16-March
2,03
4,12
4,12
3,83
3,00
0,48
-
-
-
17-March
1,85
3,28
2,20
1,93
1,73
0,52
-
-
-
20-March
3,38
2,28
3,52
3,80
3,65
0,52
-
-
-
21-March
4,58
3,92
1,88
1,35
1,60
0,35
-
-
-
22-March
2,45
4,38
3,58
4,07
2,57
0,00
-
-
-
23-March
2,30
3,60
2,80
2,57
2,35
0,37
-
-
-
24-March
3,02
4,30
4,12
3,50
1,60
0,00
-
-
27-March
0,00
0,97
0,75
0,92
0,87
0,05
-
-
-
28-March
1,63
1,92
1,37
1,83
1,08
0,00
-
-
-
10-April
3,22
4,02
3,08
11-April
4,70
4,92
3,80
12-April
2,92
2,17
1,85
13-April
2,78
3,28
1,95
14-April
0,03
0,00
18-April
1,85
19-April
-p
3,35
3,08
0,33
-
-
-
2,32
2,40
0,32
-
-
-
2,65
1,30
0,13
0,07
-
-
1,18
1,03
0,77
-
-
-
0,00
0,00
0,00
0,00
-
-
-
2,37
1,75
1,80
0,83
0,03
-
-
-
2,65
4,25
3,57
2,98
2,20
0,03
-
-
-
20-April
2,37
2,92
1,68
1,00
0,70
0,33
-
-
-
21-April
1,10
2,00
1,72
1,30
1,05
0,32
0,27
-
-
22-April
0,95
0,90
0,60
0,23
0,08
0,32
0,68
0,68
-
24-April
0,98
2,75
2,62
1,88
1,07
0,13
-
-
-
25-April
2,08
2,22
2,53
2,78
1,52
0,18
-
-
-
26-April
2,54
2,90
1,73
2,00
1,34
0,00
-
-
-
27-April
2,76
1,96
1,40
2,40
2,48
1,43
0,28
-
-
28-April
2,29
2,57
2,37
2,68
2,64
1,52
0,20
0,03
-
1-May
0,85
3,52
3,35
4,05
3,45
1,93
0,95
0,48
-
2-May
6,47
7,72
5,63
5,03
4,95
1,22
-
-
-
3-May
3,42
5,78
5,60
6,90
5,45
2,97
1,27
0,05
-
4-May
3,08
5,33
6,90
5,45
4,93
1,53
0,68
-
-
5-May
4,17
3,72
2,60
2,45
1,67
0,22
-
-
-
8-May
3,02
2,55
2,88
3,43
2,72
0,68
-
-
-
lP
ur
PRESENCE POSTDENSIFICATION
re
-
na
ro of
13:00
Jo
PRESENCE PRE-DENSIFICATION
Time.Interval
4,00
3,83
3,05
2,97
2,78
0,40
-
-
-
10-May
4,25
7,32
7,90
6,00
3,75
1,08
-
-
-
11-May
6,33
7,25
5,97
6,20
3,70
0,12
-
-
-
12-May
3,12
5,47
5,93
6,67
3,88
1,97
0,10
-
-
15-May
3,62
8,05
9,92
7,77
2,30
1,40
-
-
-
16.May
3,72
6,28
5,18
6,98
4,52
0,80
-
-
-
17-May
4,05
5,17
6,98
6,95
3,48
0,32
-
-
-
18-May
2,20
4,72
3,67
3,42
2,78
0,57
-
-
-
19-May
2,27
4,02
2,62
2,82
2,28
0,33
-
-
-
22-May
3,35
6,90
5,05
6,42
2,77
0,82
-
-
-
23-May
2,82
4,78
4,72
4,58
1,97
0,00
-
-
-
24-May
4,45
6,10
7,20
3,92
2,92
0,72
-
-
0,02
26-May
1,40
3,47
3,27
3,67
3,40
1,52
-
-
-
ro of
9-May
Data of presence measurement
1
SIAMean Averag e 0,00
AverageMinimum 0,00
Avera ge 0,00
2
0,00
0,00
0,00
3
0,00
0,00
0,00
4
0,00
0,00
5
0,00
0,00
6
0,00
0,00
7
0,00
0,00
8
0,00
9
2,10
10
6,30
11
10,50
12
Post-Densification
AverageMinimum 0,00
Avera ge 0,00
MaximumAverage 0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,45
0,50
4,50
0,43
0,64
3,36
2,38
2,66
6,34
2,64
3,09
6,91
3,61
3,76
5,24
3,07
5,22
5,78
3,96
4,09
6,91
3,04
6,00
6,00
10,50
3,69
4,17
5,83
3,37
6,05
5,95
8,40
2,17
2,49
5,51
1,50
2,69
7,31
na
lP
re
MaximumAverage 0,00
Jo
13
Pre-Densification
ur
Time.Inte rval
-p
Presence measurement comparisons (offices and meeting rooms)
14
4,20
2,30
3,04
5,96
2,15
4,05
9,95
15
6,30
3,12
3,89
6,11
2,65
6,16
4,84
16
10,50
3,49
3,65
7,35
3,15
6,10
7,90
17
8,40
3,34
3,52
7,48
2,35
5,88
7,12
18
6,30
2,23
2,42
8,58
1,96
3,94
6,06
19
2,10
0,43
0,53
4,47
1,04
1,15
5,85
20
0,00
0,45
0,45
1,55
0,91
1,01
1,99
21
0,00
0,35
0,35
0,65
0,58
1,06
1,94
22
0,00
0,10
0,10
0,08
0,02
0,02
1,98
23
0,00
0,00
0,00
0,00
0,00
0,00
2,00
24
0,00
0,00
0,00
0,00
0,00
0,00
0,00
Presence measurement only in offices Time.Interval
Pre-densification Average Min
Max
Post-densification Average Min
Max
0,00
0,00
0,00
0,00
0,00
0,00
1
0,00
0,00
0,00
0,00
0,00
0,00
2
0,00
0,00
0,00
0,00
0,00
0,00
3
0,00
0,00
0,00
0,00
0,00
0,00
4
0,00
0,00
0,00
0,00
0,00
0,00
5
0,00
0,00
0,00
0,00
0,00
0,00
6
0,00
0,00
0,00
0,00
0,00
0,00
7
0,51
0,02
1,62
0,61
0,20
1,73
8
2,44
0,15
4,80
2,74
0,45
4,98
9
3,09
0,15
5,90
4,60
2,03
7,45
10
3,23
0,02
5,57
5,20
2,92
7,85
11
3,13
0,02
5,97
5,08
0,85
8,92
12
2,01
0,05
4,35
2,10
0,15
3,53
13
2,60
0,03
5,40
3,50
0,85
6,47
14
3,09
0,90
5,17
5,37
2,55
8,05
15
2,62
0,60
5,32
5,18
2,60
9,92
16
2,56
0,23
4,72
5,04
2,45
7,77
17
2,04
0,08
4,47
3,35
1,67
5,45
18
0,45
0,00
1,88
1,03
0,12
2,97
19
0,44
0,07
1,35
0,75
0,10
1,27
20
0,34
0,03
0,68
0,27
0,05
0,48
21
0,00
0,18
0,18
0,02
0,00
0,02
22
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
-p
re
lP
na
23
ro of
0
ur
Presence measurement offices and meeting rooms
Time.Interval
Average
Max
Post-Densification Average Min
Max
0,00
0,00
0,00
0,00
0,00
0,00
1
0,00
0,00
0,00
0,00
0,00
0,00
2
0,00
0,00
0,00
0,00
0,00
0,00
3
0,00
0,00
0,00
0,00
0,00
0,00
4
0,00
0,00
0,00
0,00
0,00
0,00
5
0,00
0,00
0,00
0,00
0,00
0,00
6
0,00
0,00
0,00
0,00
0,00
0,00
7
0,50
0,05
5,00
0,64
0,22
4,00
8
2,66
0,28
9,00
3,09
0,45
10,00
9
3,76
0,15
9,00
5,22
2,15
11,00
Jo
0
Pre-densification Min
4,09
0,13
11,00
6,00
2,97
12,00
11
4,17
0,48
10,00
6,05
2,68
12,00
12
2,49
0,32
8,00
2,69
1,18
10,00
13
3,04
0,73
9,00
4,05
1,90
14,00
14
3,89
0,77
10,00
6,16
3,52
11,00
15
3,65
0,17
11,00
6,10
2,95
14,00
16
3,52
0,18
11,00
5,88
3,53
13,00
17
2,42
0,18
11,00
3,94
1,98
10,00
18
0,53
0,10
5,00
1,15
0,12
7,00
19
0,45
0,00
2,00
1,01
0,10
3,00
20
0,35
0,00
1,00
1,06
0,48
3,00
21
0,10
0,00
0,18
0,02
0,00
2,00
22
0,00
0,00
0,00
0,00
0,00
2,00
23
0,00
0,00
0,00
0,00
0,00
0,00
Data of behavior perception
13.04.17 Office 2
6
2
P2
7
5
P3
6
P1
7
P2
7
5 3
5
7
7
5
7
6
6
5
5
5
8
7
3
5
P1
8
8
2
P2
7
6
7
P3
3
5
5
P1
7
3
5
P2
9
5
3
P3
7
7
4
P1
7
5
4
P1
8
4
4
P2
3
3
8
P1
8
8
2
P2
5
5
5
P1
7
5
5
P2
7
3
1
P3
7
7
2
P1
7
5
2
na
P3
Office 1
Jo
19.04.17
ur
Office 2
Office 2 Office 1
20.04.17
Office 2
24.04.17
Office 1 Office 2
Averages Arousal Dominance
3
7
P2
Pleasure
5
7
P1
18.04.17
Answer Arousal Dominance
P1
P3 Office 1
Pleasure
-p
Office 1
Person
re
Office
lP
Pre-exp
ro of
10
6,7
5,2
5,0
6,0
5,5
5,3
7,5
5,0
4,0
6,0
5,0
4,8
6,9
5,0
2,9
Office 2
Office 1 27.04.17 Office 2
28.04.17
Office 2
P3
8
8
4
P4
6
4
3
P1
9
1
1
P2
7
5
5
P3
7
7
5
P1
8
5
5
P2
6
6
6
P1
7
6
5
P2
5
7
7
P3
5
7
5
P4
8
4
3
P1
7
5
3
P2?
5
7
3
P3
7
7
5
P4
7
3
5
P1
6
5
6
P2
8
3
3
P3
8
9
1
P1
6
7
P2
7
4
P3
5
2
4
P2
5
5
7
6
4
3
3
3
6
7
5
5
P1
6
4
5
P2
7
3
3
P3
5
7
7
P1
5
5
5
P2
5
4
5
P3
5
4
4
P1
7
6
4
P2
7
5
5
P3
7
3
6
P4
7
5
5
P5
5
5
2
P6
9
9
1
P1
5
5
5
P2
7
3
5
Quite
P3
8
7
1
Interactive
P1
7
7
4
P4
na
P5
ur
Interactive
Jo
Quite
02.05.17
Interactive
03.05.17
6,3
6,0
5,0
5,6
3,7
4,3
4,3
5
3
01.05.17
4,4
6,0
5
P3
4,8
6,9
3
Quite
P1
7,4
3
lP
26.04.17
3
ro of
Office 2
3
-p
25.04.17
6
re
Office 1
P2
5,4
4,0
5,1
6,3
5,1
4,1
7,1
6,3
3,7
05.05.17 Interactive
P3
9
9
3
P4
7
7
5
P1
5
5
3
P2
4
4
9
P3
4
4
4
P4
7
5
5
P5
5
6
5
P1
7
6
1
P2
9
9
3
P3
7
5
5
P4
6
6
5
P5
7
8
2
P6
8
5
2
P1
7
6
5
P2
7
3
4
P3
4
3
3
P1
8
7
6
P2
9
9
1
P3
7
5
P4
7
7
P5
7
7
P6
9
P7
7
P8
9
P1 Quite
P2 P3
08.05.17
Jo
Quite
09.05.17
Interactive
10.05.17
Quite
Interactive
5
6
5
7
3
4
4
7
6
4
5
7
3
5
9
5
2
6
5
2
P2
7
7
3
P3
7
7
2
P1
4
5
6
P2
7
5
3
P3 P1
9
1
5
9
1
5
P2
7
7
5
P3
8
6
3
P4
7
7
5
P5
9
7
3
P1
7
6
3
P2
7
3
3
P3
7 8
5
5
2
4
8
6
3
P4 P1
4,0
7,4
6,1
3,9
6,6
5,0
3,7
7,5
4,9
4,4
7,0
4,7
3,7
3
P1
ur
Interactive
5,7
5
7
na
P4
3
6,3
ro of
Quite
3
-p
Interactive
6
re
04.05.17
7
lP
Quite
P2
12.05.17 Interactive
Quite
15.05.17
P3
4
5
7
P1
1
1
9
P2
7
5
5
P3 P1
7
7
9
6
7 3
P2
6
5
5
P3
7
5
1
P4
6
4
4
P5
3
5
7
P1
7
7
5
P2 P1
7
5
7
6
4
5
P2
9
3
3
P3
9
8
1
P4
7
4
3
P5
5
5
7
P6
7
7
5
P1
7
4
4
P2
8
3
P3
6
5
P4
6
5
P5
7
P6 P1
6
P2 Interactive
P3 P4
Jo
16.05.17
Interactive
Quite 17.05.17 Interactive
7,1
5,4
4,5
6,7
4,6
4,2
6,8
5,0
4,4
7,2
6,1
4,0
5
4
4
7
3
8
4
1
5
4
6
7
6
3
6
3
4
P6
7
5
5
P1
6
5
5
P2
8
5
3
P3
7
5
3
P4 P1
6
5
6
6
4
6
P2
7
5
5
P3
7
6
3
P4
7
5
4
P1
7
6
3
P2
9
7
1
P3
7
7
3
P4 P1
7
7
5
8
7
3
P2
7
7
2
P3
8
6
4
ur
Quite
5,1
7 6
na
P5
4,8
2
5
7
5,8
ro of
Quite
1
-p
Interactive
6
re
11.05.17
8
lP
Quite
P2
19.05.17 Interactive
Quite
22.05.17
Interactive
P5
8
5
5
P6
5
5
7
P7
5
3
5
P1
5
3
6
P2 P1
3
3
6
5
5
5
P2
7
7
4
P3
8
4
5
P4
9
7
5
P1
6
6
5
P2
8
3
3
P3
7
7
4
P4 P1
9
7
5
3
5
5
P2
6
3
5
P3
7
5
7
P1
9
7
5
P2
7
6
3
P3
7
5
P4
5
7
P5
8
3
P6 P1
8
P2
7
P3 P4 P5
7
3
6
5
7
5
5
6
6
2
7
3
3
7
2
3
6
7
5
P3
9
7
3
P4
7
7
3
P5
ur
8
2
2
P6
7
5
5
P7 P1
5
7
5
7
5
3
Interactive
P2
8
8
3
P3
5
1
7
P1
7
7
3
P2
8
5
5
P3
8
2
3
P4
7
3
5
P5 P1
9
5
2
7
7
4
P2
8
6
1
Jo
23.05.17
Quite 24.05.17
Interactive
6,6
5,1
4,9
7,1
5,5
3,4
6,9
5,1
3,9
7,6
4,8
3,5
1
P2
Quite
5,2
3 2
na
P1
4,8
5
6
7
6,2
ro of
Quite
6
-p
Interactive
7
re
18.05.17
8
lP
Quite
P4
7
3
5
P1
4
7
8
Quite
P2
6
8
7
7
5
5
Interactive
P3 P1
6
8
5
5,8
7,0
6,3
Jo
ur
na
lP
re
-p
ro of
26.05.17
P3
oo
f
Inventory data
Office scale (Pre-Densification) Material
Intermediate slab
Interior walls
CEDnr (MJ)
GWP (kg CO2)
60
106,3
104,5
14,5
10.005
kg
60
0,8
0,8
0,1
1.003
kg
60
13,5
12,8
0,7
06.003
kg
25
57,4
33,5
2,2
11.014
10710
kg
60
39,4
8,1
0,4
07.002
40720
kg
60
3,0
2,8
0,3
01.041
Concrete
71970
86362
Reinforcing steel
1840
2204
Linoleum
1325
1586
Wood
8925
Precast concrete element
33935
Linoleum
kg
1325
1584
kg
25
57,4
33,5
2,2
11.014
5555
5650
kg
60
26,9
2,9
0,1
07.011
2830
2874
kg
40
38,0
12,7
0,7
10.009
17,5
22
m2
40
1856,8
1741,5
109,5
05.016
2
na l
Triple glazing, U<0.6 W/m2K Windows
KBOB
CED (MJ)
264
Soft fiberboard
Environmental impacts per unit
year
220
Wood
Service life
pr
Slab
Unit
e-
Extruded polystyrene
Quantity Interaction Quiet
Pr
Component
Wooden frame
7,5
9,4
m
40
4623,2
2063,1
128,6
05.005
Columns
Wood
55
54
kg
60
39,4
8,1
0,4
07.002
Electical box
Wood
105
104
kg
60
39,4
8,1
0,4
07.002
Jo ur
Heating distribution Ventilation
2
123
147
m ERA
25
110,9
94,8
5,8
31.021
123
147
m2 ERA
25
729,3
675,9
43,6
32.011
2
Sanitary equipment
123
147
m ERA
25
74,6
70,3
4,5
33.001
Electical equipment
123
147
m2 ERA
25
683,9
409,8
23,9
34.002
3130
kWh
1
3,008
2,52
0,102
45020
1910
kWh
2585,2
2411,1
148,9
Lighting
Appliances
7
9
unit
1 5
Monitors
7
13
unit
6
1795,5
1671,7
120,4
Docking station
7
9
unit
5
123,0
105,8
7,1
Laptops
Hoxha et al. 2017
9
unit
5
Mouse
7
9
unit
5
Working table
11
15
unit
Working chair
7
9
unit
7,5
7,5
unit
Sofa
1
0
Meeting chair
15
21
Round table
1
1
Metal tambour cabinets
8
Boiler
1
White board
12 3
Jo ur
na l
Clothes hanger
269,0
18,9
79,0
72,3
5,0
20
1235,8
611,3
67,8
15
1346,9
1245,3
82,3
15
812,1
680,5
63,5
pr 20
5515,8
4098,5
240,0
unit
15
304,4
286,4
18,1
unit
15
727,0
312,9
33,5
13
unit
20
2130,5
1878,5
160,4
0
unit
10
87,7
85,1
4,1
12
unit
15
28,8
26,8
1,5
3
unit
15
218,1
209,2
16,4
e-
unit
Pr
Cabinets
293,2
f
7
oo
Keyboards
Quantity Interaction Quiet
Material
Intermediate slab
Interior walls
264
kg
Concrete
71970
86362
kg
Reinforcing steel
1840
2204
Linoleum
1325
1586
Wood
8925
10710
Precast concrete element
33935
Linoleum
1325
Wood
5555
Soft fiberboard
2830
Triple glazing, U<0.6 W/m K Windows Columns
Wood
Electical box
Wood
Heating distribution
Jo ur
Sanitary equipment
Electical equipment Lighting
GWP (kg CO2)
BL
106,3
104,5
14,5
10.005
BL
0,8
0,8
0,1
1.003
kg
BL
13,5
12,8
0,7
06.003
kg
25
57,4
33,5
2,2
11.014
kg
BL
39,4
8,1
0,4
07.002
40720
kg
BL
3,0
2,8
0,3
01.041
1584
kg
25
57,4
33,5
2,2
11.014
5650
kg
BL
26,9
2,9
0,1
07.011
2874
kg
40
38,0
12,7
0,7
10.009
2
22
m
40
1856,8
1741,5
109,5
05.016
7,5
9,4
m2
40
4623,2
2063,1
128,6
05.005
55
54
kg
BL
39,4
8,1
0,4
07.002
105
104
kg
BL
39,4
8,1
0,4
07.002
123
147
m2 ERA
25
110,9
94,8
5,8
31.021
123
147
m2 ERA
25
729,3
675,9
43,6
32.011
123
147
m2 ERA
25
74,6
70,3
4,5
33.001
25
683,9
409,8
23,9
34.002
3,008
2,52
0,102
45020
123
Appliances Laptops
CEDnr (MJ)
17,5
na l
Wooden frame
147
2
m ERA
3000
kWh
1
1801
kWh
1
12
15
unit
5
2585,2
2411,1
148,9
4
10
unit
6
1795,5
1671,7
120,4
Docking station
0
0
unit
5
123,0
105,8
7,1
Keyboards
12
13
unit
5
293,2
269,0
18,9
Monitors
KBOB
CED (MJ)
pr
220
2
Ventilation
Service life
e-
Slab
unit
Pr
Extruded polystyrene
Environmental impacts per unit
oo
Component
f
Office scale (Post-Densification)
Hoxha et al. 2017
15
unit
5
Working table
8
6
unit
20
Working chair
4
10
unit
2,5
1,5
unit
Sofa
0
0
unit
Meeting chair
15
17
Round table
1
0
Metal tambour cabinets
0
0
Boiler
1
72,3
5,0
1235,8
611,3
67,8
15
1346,9
1245,3
82,3
15
812,1
680,5
63,5
20
5515,8
4098,5
240,0
pr
Cabinets
79,0
f
12
oo
Mouse
15
304,4
286,4
18,1
unit
15
727,0
312,9
33,5
unit
20
2130,5
1878,5
160,4
0
unit
10
87,7
85,1
4,1
10,3
unit
15
28,8
26,8
1,5
3
unit
15
218,1
209,2
16,4
2
0
unit
20
163,7
146,9
9,6
1
0
unit
20
326,1
293,2
20,6
1
0
unit
20
2682,7
2094,8
214,0
2
0
unit
20
104,5
95,8
11,3
14
0
unit
15
457,3
428,3
28,4
45
16,7
kg
20
63,1
62,1
2,5
6,3
0
kg
20
4,0
3,8
0,8
1
0
unit
15
339,8
174,4
22,5
Sofa chair
2
0
unit
15
364,6
67,3
5,3
USM - Storage (Pos. E)
0
1
unit
20
4050,5
3600,1
261,3
USM - Storage (Pos. F)
0
1
unit
20
7980,5
7334,9
865,9
USM - Storage (Pos. G)
0
1
unit
20
6655,0
6116,6
722,1
USM - Storage (Pos. H)
2
3
unit
20
3221,0
2960,6
349,3
USM - Storage (Pos. I)
0
1
unit
20
1772,8
1629,4
192,4
USM - Storage (Pos. J)
1
0
unit
20
4368,5
3991,5
471,0
USM - Storage (Pos. K)
1
0
unit
20
7434,8
6793,1
801,8
White board
12,1 3
Pr
Clothes hanger Hanging lights T-1 Hanging lights T-2
Chair ´Vitra´ Flower vase
PVC Fired clay
Jo ur
Plastic chair
na l
Tables '1001 feuilles' Tabouret
e-
unit
ecoinvent
10
unit
20
965,9
892,9
126,0
Wood table
0
3
unit
20
831,3
178,3
24,2
Wood chair
0
3
unit
15
93,3
14,6
2,1
Meeting chair T-2
0
2
unit
15
457,3
428,3
28,4
Table lights T-1
0
4
unit
20
54,2
50,7
4,1
Table lights T-2
0
15
Armchair
0
2
Sofa table
0
2
pr unit
20
457,3
428,3
28,4
unit
20
3888,6
2953,0
184,2
unit
20
57,3
52,8
6,8
e-
Pr
f
4
oo
Standard table 75x150cm
Building scale
Quantity
Components / Materials
Pre-Densifi
Service life
Environmental impacts per unit
Source
year
CED (MJ)
CEDnr (MJ)
GWP (kg CO2)
KBOB
kg
60
0,5
0,5
0,1
1.001
442740
kg
60
0,8
0,8
0,1
1.003
1245360
kg
60
3,0
2,8
0,3
01.041
46369
kg
60
13,5
12,8
0,7
06.003
423166
kg
60
39,4
8,1
0,4
07.002
Solid wood, air dried
67666
kg
60
23,3
2,5
0,1
07.010
OSB chipboard
16337
kg
60
39,6
9,9
0,6
07.013
PE vapor barrier
151
kg
60
92,6
89,3
5,3
09.002
Bituminous waterproofing
3467
kg
60
45,9
45,0
3,3
09.003
Glass wool
7561
kg
60
35,8
27,9
1,1
10.001
Cellulose board
7235
kg
60
4,6
3,7
0,3
10,01
Polystyrene
5652
kg
60
106,6
104,8
14,5
10.005
Reinforced plasterboard
43205
kg
30
9,2
8,9
0,5
03.007
na l
132030
Poor concrete Concrete CEM I
Prefabricated concrete element Reinforcing steel Glue-Laminated timber
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Building envelope
Post-Densifi
Unit
IDEM
kg
30
Gravel
105504
kg
30
Mortar
41825
kg
720
kg
Solid wood
18085
kg
Glue-Laminated timber
8290
Solid wood, air dried
13560
OSB chipboard
1560
PE vapor barrier
240
Bituminous waterproofing
3465
Glass wool
2105
Cellulose board
29395
Linoleum
52150
0,3
03.008
0,2
0,2
0,0
03.012
30
1,7
1,6
0,1
04.005
30
29,0
27,9
1,8
06.010
30
45,2
10,8
0,5
07.001
30
39,4
8,1
0,4
07.002
kg
30
23,3
2,5
0,1
07.010
kg
30
39,6
9,9
0,6
07.013
kg
30
92,6
89,3
5,3
09.002
kg
30
45,9
45,0
3,3
09.003
kg
30
35,8
27,9
1,1
10.001
kg
30
4,6
3,7
0,3
10,01
kg
30
166,3
96,8
6,4
11.014
2
m
60
6538
5760
362
05.004
157
m2
30
4622
2063
128
05.005
365
2
m
30
993,2
939,6
66,8
05.012
56
m2
60
993,2
939,6
66,8
05.012
56
m2
30
3636
2376
155
12.001
Toilet doors
71
m2
30
3478
1389
86
12.003
Heat pump
1
unit
20
21694
19872
2180
31.017
Heat diffusion through the ceiling
5255
SRE
20
111,1
95,0
5,8
31.025
Ventilation system
5255
SRE
20
729,3
675,9
43,6
32.011
Sanitary equipment
5255
SRE
20
74,6
70,3
4,5
33.001
Electrical equipment
5255
SRE
20
683,9
409,8
23,9
34.002
3,0
2,5
0,1
45.020
Triple glazing Triple glazing
Electric ity
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Internal doors
na l
Wood frame
Pr
14
e-
kg
Aluminium frame
Equipment
4,9
pr
Fixing screw
5,1
f
198955
oo
Gypsum plasterboard
Heating
1569622
1889722
kWh
1
DHW
1199839
1204444
kWh
1
kWh
1
Lighting
3074768
2472068
kWh
1
Appliances Laptops
1204354
1080540
288
486
kWh unit
Monitors
360
252
unit
Docking station
288
0
Keyboards
288
450
Mouse
288
486
Working table
438
Working chair
f
623631
oo
654469
2585
2411
148,9
6
1796
1672
120,4
pr
1 5 5
123,0
105,8
7,1
unit
5
293,2
269,0
18,9
unit
5
79,0
72,3
5,0
372
unit
20
1236
611,3
67,8
288
252
unit
15
1347
1245
82,3
Cabinets
258
120
unit
15
812,1
680,5
63,5
Sofa
18
0
unit
20
5516
4099
240,0
Meeting chair
564
912
unit
15
304,4
286,4
18,1
Round table
36
18
unit
15
727
313
33,5
Metal tambour cabinets
378
0
unit
20
2131
1879
160,4
18
18
unit
10
87,7
85,1
4,1
396
547,2
unit
15
28,8
26,8
1,5
102
132
unit
15
218,1
209,2
16,4
0
36
unit
20
163,7
146,9
9,6
Hanging lights T-2
0
18
unit
20
326,1
293,2
20,6
Tables '1001 feuilles'
0
18
unit
20
2683
2095
214,0
Tabouret
0
36
unit
20
104,5
95,8
11,3
Chair ´Vitra´
0
252
unit
15
457,3
428,3
28,4
Flower vase (PVC)
0
1110,6
kg
20
63,1
62,1
2,5
Flower vase (Fired clay)
0
113,4
kg
20
4,0
3,8
0,8
Plastic chair
0
18
unit
15
339,8
174,4
22,5
Sofa chair
0
36
unit
15
364,6
67,3
5,3
White board Clothes hanger Hanging lights T-1
Pr
na l
Boiler
e-
unit
Jo ur
Appliances
Ventilation
Hoxha et al. 2017
ecoinvent
18
unit
20
USM - Storage (Pos. F)
0
18
unit
20
USM - Storage (Pos. G)
0
18
unit
USM - Storage (Pos. H)
0
90
unit
USM - Storage (Pos. I)
0
18
unit
USM - Storage (Pos. J)
0
18
USM - Storage (Pos. K)
0
18
Standard table 75x150cm
0
252
Wood table
0
Wood chair
0
Meeting chair T-2
0
Table lights T-1
0
Table lights T-2
0
Armchair
0
Sofa table
0
3600
261,3
7981
7335
865,9
20
6655
6117
722,1
20
3221
2961
349,3
20
1773
1629
192,4
pr 20
4368
3991
471,0
unit
20
7434
6793
801,8
unit
20
966
892
126,0
54
unit
20
831
178
24,2
54
unit
15
93,3
14,6
2,1
36
unit
15
457
428
28,4
72
unit
20
54,2
50,7
4,1
270
unit
20
457
428
28,4
36
unit
20
3888
2953
184
36
unit
20
57,3
52,8
6,8
e-
unit
Pr
na l
Jo ur
4051
f
0
oo
USM - Storage (Pos. E)
8. Acknowledgement The work presented in this paper has been funded by the ….. We thank all anonymous participants as well as ……. team for their valuable discussion. We also thank ….for its contribution in designing the great, new office layouts, and the ….. for its contribution to this experiment. The design of this new layout was proposed with the assistance of “Atelier Oï”, an
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architecture and design office that conducts several participative workshops with future users.
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1. Design of Experiment Phase A : pre-densification
Phase B : post-densification
Team A Team B Team C
2. Measurements
Presence measurement Energy consumption
3. Life cycle assessment
Environmental impacts (ex: kg CO2e) in pre-densification in post-densification
Furniture Appliances embodied Appliances usage Lighting Ventilation DHW Space heating Building embodied
ur
na
generalizazion at the buidling scale
Furniture Appliances embodied Appliances usage Lighting Ventilation DHW Space heating Building embodied
Environmental impacts (ex: kg CO2e) in pre-densification in post-densification
Building components
4. Life cycle assessment
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Building components
at the two offices scale
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Perceived comfort
Time in post-densification
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Rate of measuring parameters
Time in pre-densification
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circles represent time spent at the working place
Figure 1: General methodology schema. (Step 1: the circles of teams schematically present the time the
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employees spend in their working places. Step 2: x-axis presents the time in the pre- and post-densification phases, while the y-axis presents the rate of measuring parameters. Steps 3 and 4: environmental impacts of building components in the pre- and post-densification phases.)
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Figure 2: Pleasure-Arousal-Dominance (PAD) questionnaire with a 9-point scale and three axes: “Pleasure” (first row), “Arousal” (second row), and “Dominance” (third row) (Bradley et
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na
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al., 1994).
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Figure 3: An office before (left photo) being converted into the interactive room after densification (right).
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Figure 4: Plan layout of the smart living building (6 identical floors) 16
SIA-2024, (2015) Pre-Densification
12
Post-Densification
10
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8 6 4 2 0 0
1
2
3
4
5
6
7
8
9
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Number of employees
14
10
11
12
13
14
15
16
17
18
19
20
21
22
23
Offices and meeting rooms Offices
na
5 4 3 2 1 0
ur
Number of employees
6
Pre-densification
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0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Times in hours
7 6
Number of employees
7
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Time in hours
Post-densification Offices and meeting rooms Offices
5 4 3 2 1 0
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Times in hours
Figure 5: Rate of densification during two phases of the experiment, according to the SIA-2024 Swiss norm and the real-presence monitoring before and after densification.
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Figure 6: PAD responses during pre- and post-densification phases. Graphics present the mean
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values while error bars represent minimal and maximal values of PAD responses.
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densification.
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Figure 7: Participants’ pleasure, arousal, and dominance emotional states before and after
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Figure 8: Pleasure and arousal answers in the post-densification phase of the experiment.
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Figure 9: Environmental impacts at room-scale for pre- and post-densification phases.
Figure 10: Environmental impact at office scales according to different functional units for
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pre- and post-densification phases.
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densification.
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Figure 11: Environmental impacts on the smart living building scale before and after
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Figure 12: Environmental impact of the smart living lab on different units.
PII:
S2210-6707(20)30019-6
DOI:
https://doi.org/10.1016/j.scs.2020.102032
Reference:
SCS 102032
To appear in:
Sustainable Cities and Society
Received Date:
21 July 2019
Revised Date:
23 December 2019
Accepted Date:
23 December 2019
Please cite this article as: Hoxha E, Liardet C, Jusselme T, Office densification effects on comfort, energy, and carbon lifecycle performance: an integrated and exploratory study, Sustainable Cities and Society (2020), doi: https://doi.org/10.1016/j.scs.2020.102032
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Office densification effects on comfort, energy, and carbon lifecycle performance: an integrated and exploratory study Hoxha, E.1, Liardet, C.1, Jusselme, T.1 1
Building2050 Research Group, Ecole Polytechnique Fédérale de Lausanne (EPFL), Fribourg,
Switzerland
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Corresponding author Endrit HOXHA (PhD, M.Sc, Dip-Ing) Ecole Polytechnique Federale de Lausanne Building 2050 Research Group smart living lab, Halle Bleue, Site de blueFACTORY Passage du Cardinal 13B CH – 1700 Fribourg
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Highlights
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e-mail : [email protected]
This research measured the environmental impacts of office densification.
Densification did not significantly impacted the occupant’s comfort.
Environmental impacts (CED, CEDnr, GWP) were reduced by almost 50%.
A new functional unit "eq-nominal people per effective presence" is introduced.
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Abstract
Generally, the environmental impacts of buildings are benchmarked per square meter as a functional unit. However, this practice prevents developing a user-centered approach in which
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performance is linked to real usage and not building size. Currently, the less a building is used, the lower the energy consumed during its use and, consequently, the impact per square meter, which does not make sense regarding both usability and environmental performance. Following user-centered design principles, the goal of the present research is to assess the environmental impact of buildings based on novel user-based functional units and to understand the environmental impact consequences of office occupation density. An experiment within the area of an academic office offered the opportunity to test the densification of working spaces
and evaluate the resulting environmental and comfort impacts. In the end, the new user-based functional units highlight a reduction of all environmental indicators by almost 50%. Also, the functional unit "eq-nominal people per effective presence" is introduced as most suitable to evaluate environmental performance according to real building usage, as a complement to square meter function units.
Keyword: Office densification; User-centered assessment; Life cycle assessment; functional
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unit.
1. Introduction Urgent action is necessary for mitigating greenhouse gases worldwide, especially in the construction sector, as it is among the main CO2-eq emitters. This sector must be carbon-neutral by 2050 to achieve the targets set by the Paris Climate Agreement (IPCC, 2014; Rockströmet et al., 2017; IPCC, 2018). Although implementing active and passive energy strategies (Lehmann, 2013; Monteiro et al., 2016; Gao et al., 2018; Saretta et al., 2019) with environmental-friendly materials has drastically reduced the impact of this sector (Jelle et al., 2010; Shoubi et al., 2015; Qoraut, 2017, Shen et al., 2018), additional solutions are necessary for achieving sustainable use of raw
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materials and energy (Jusselme et al., 2016). The life cycle assessment (LCA) method has been largely used for evaluating environmental impacts. It consists of four steps: goal and scope definition, life cycle inventory, environmental impacts assessment, and interpretation of results (ISO-14040, 2006). The first non-technical step of LCA consists of defining a functional unit, which is crucial since on it are all inventory
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data and impact evaluations normalized (Weidema et al., 2004; Hottenroth et al., 2018). Square meter of net-, gross-, or energy-floor area or per capita metric are the common functional units
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used by the scientific community and practitioners for assessing the environmental impacts of buildings (Peuportier, 2001; Lebert et al., 2012; Thiers and Peuportier, 2012; Heinonen and
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Junnila., 2014; Rashid and Yusoff, 2015; Lasvaux et al., 2017; Hoxha et al., 2017). In addition to these units, the European norm EN-15978 (2011) recommends the use of units per-building piece, -year, or -person. These units are used extensively for the evaluation of the environmental
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impacts of buildings (De Wolf et al., 2017).
Although these units are generally accepted by the scientific community and largely considered in evaluating the environmental impacts of buildings, they link the construction performance to
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the building size.
Indeed, an almost empty building might have a low environmental impact per square meter, but
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a very high impact per user. Thus, it is difficult to benchmark the performance of buildings in use if their intensity of use is not considered; i.e., their occupation rate. However, surface functional units prevent the development of a user-centered approach where performance depends on effective usage. Motivated by this knowledge gap, the first aim of this study is to introduce a novel functional unit capable of assessing the environmental impacts of buildings according to their usability in addition to the existing square meter units. This new user-centered functional unit might allow
professionals to consider the densification of building spaces as a possible strategy for minimizing the impacts of building projects. Densification of spaces has been found to be a robust solution for the minimization of environmental impacts (Resch et al., 2016). At building scale, the densification is studied through the occupancy behavior, which has a crucial influence on the operating energy of the building (Hong et al., 2016; Ekwevugbe et al., 2017; Capozzoli et al., 2017; Jafarinejad et al., 2019). Aiming for the minimization of the energy used during the operational phase of the building, Hong et al.’s (2016) study recommended the collection of good and adequate data for
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understanding the behavior of occupants. In an office building situated in Netherland, Capozzoli et al. (2017) measured the occupancy profile of employees in the different thermal zones. They proposed an optimized schedule in considering the variability of occupancy profiles. Due to this improvement, it was possible to reduce by 14% the energy required by HVAC systems that were operating with fixed schedules. With an identical scope, Peng et al. (2017) reported 7% to
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52% energy savings for cooling systems. Curto et al. (2019) investigated energy-saving measures for the ventilation system of a large shopping center. Jafarinejad et al. (2019) found
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a saving potential of around 19% for the operational energy of a building by proposing a more optimal occupancy profile. Pisello et al. (2012) and Erickson et al. (2014) also highlighted the
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necessity of real-time occupancy data in order to achieve energy-savings. Staff presence in a university building was investigated via questionnaire in a pilot study presented by Gul and Patidar (2015). Their conclusion found that only 8% were academic staff with permanent
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working places and the remaining 92% of the users were visitors. Zhao et al. (2014) also developed a data mining method to learn building occupant behavior and observed the impacts of different occupation schedules on energy consumption. The influence of user behavior in
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energy use during the operational phase of the building was also evaluated by Naylor et al. (2018). Moreover, D´Oca et al. (2018) uses an extensive literature review to show that the
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influence of the human dimensions in the energy performance of buildings is as significant as that of advanced technologies. Based on this literature review, it is evident that occupant behavior significantly influences energy consumption during the operational phase of the building. However, two aspects of occupant behavior and, more precisely, of building densification, have never been assessed. Assessed performance indicators were limited to the energy consumption during the operational phase of the building and never extended to the entire building lifecycle performance by also considering the embodied impacts. Occupant densification has always
been considered a contributor to energy consumption and never as a strategy to increase building performance. Hence, the second objective of this research is to assess building densification efficiency regarding lifecycle performance through a case study. As densification might impact user comfort, this parameter will also be monitored to ensure that environmental benefits will not be offset by user discomfort.
2. Method To address the problem presented, the current research implemented a methodology in four
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distinct steps according to the following schema (Figure 1). The first step sketched out the whole project and identified the offices where the experiment would take place and their layout for both phases. Moreover, the intended participants were identified and protocols for the evaluation of their comfort, tools for tracing their presence in offices, and equipment for measuring energy consumption were determined.
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The second phase was dedicated to the collection and analysis of the energy consumption data, comfort perception, and presence of employees in the work spaces. The third phase of the data
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collection was used for the evaluation of the environmental impacts of two offices where the experiment took place. Since it was not possible to include into the study boundary the
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environmental impacts of several building components at office scale (e.g., foundations, corridors, etc.,), in the last phase of the methodology the data collected were generalized at building scale. During this phase the environmental impacts of the buildings were assessed,
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from which conclusions were drawn.
2.1.Design of the experiment
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The objective of the present research is to introduce a novel functional unit to drive the evaluation toward user-centered design principles, and then to assess the potential benefits of
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densification strategies. However, the densification of building areas influences perceived comfort for its users, and a real occupation dataset is hard to gather. Therefore, a field experiment is judged necessary to answer our research question. According to Figure 1, the methodology is a two-phase experiment: Phase-A or pre-densification, and Phase-B or postdensification. During the pre-densification phase of the experiment the layout of offices and working conditions must not change. In the post-densification phase, the goal is to increase occupancy rates by increasing the population within the same boundaries. To do so, the layout
of offices has to change to welcome a greater number of users while maintaining their working comfort. Then, the goal is to monitor each phase in terms of comfort, density, and energy consumption. The experiment boundaries, its total duration, and specific conditions must be defined to represent the real world to scale the results at the building level. Indeed, the boundary of the study under evaluation should be accurately chosen to include details and represent the building itself. The time span of the experiment is defined based on the minimal representative time for the year and the largest time tolerable by employees to remain under observation. Considering
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both conditions, one month per phase can be considered the minimum suitable duration.
2.2.Measurements and evaluations
Over the course of the experiment, several parameters - such as user presence, comfort perception, and energy consumption - must be measured. Moreover, an inventory of materials employed in office construction and those used for furniture and appliances must be quantified.
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Finally, all these energy consumers and materials must be converted in terms of environmental impacts for both phases of the experiment.
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2.2.1. Presence measurement
One of the key points of this experiment is the ability to monitor the effective presence of
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employees within the experimentation boundaries. As the experiment must be performed over two months to be representative (one month per experiment phase), counting based on observation is not a realistic option. Verma et al. (2017) propose a numerical infrastructure
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where each experiment participant can be provided with a bracelet that broadcasts a Bluetooth signal based on a per-second time step. In each room, one data logger receives the bracelet signals to determine each employee´s location and presence duration. The signals recorded in
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data loggers were then uploaded and analyzed in a separate Excel file. During 24 hours on a per-second time step an Excel cell for each employee contained the number 1 or 0. The number
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1 highlighted the presence of the employee and the number 0 his absence.
2.2.2. Behavior perception A monitoring protocol must be set up to follow employees’ emotional responses to the densification. The self-assessment manikin technique (Bradley et al., 1994) can be used during the pre- and post-densification phase. This method is easy to implement, inexpensive, and already widely used by researchers. At the end of their workday, all employees completed a
Pleasure-Arousal-Dominance (PAD) questionnaire (Figure 2), grading their emotional states using a non-verbal pictorial technique. The grade of parameters ranged from 1 to 9. For the pleasure parameter, grade 1 meant unpleasant and grade 9 meant very pleasant. For the arousal parameter, grade 1 meant sleepy and grade 9 meant very excited. For the dominance parameter, grade 1 meant very dominant and grade 9 meant not dominant.
2.2.3. Energy consumption measurement Energy consumption must be monitored using sensors. All electrical equipment used by the participants was monitored thanks to individual Z-Wave energy meters directly plugged into
the energy consumption of electrical appliances and lights.
2.2.4. Environmental impact assessment
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each socket and their data collected through a Raspberry Pi. This allowed the measurement of
Embodied impacts of office, furniture, and appliances, as well as the environmental impacts of
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energy required for lighting, must be evaluated and considered in the boundary of the study. Five functional units are proposed to test the impact evaluation: energy reference area (ERA) per year;
number of employees evaluated according to the Swiss norm SIA-2024, (2015);
real number of employees;
full-time-equivalent employees according to their administrative contract;
and effective-full-time-equivalent employees, according to their effective presence.
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Although practitioners and researchers (Peuportier et al., 2001; Rashid and Yusoff, 2015; Lasvaux et al., 2017; Hoxha et al., 2017) generally use the first functional unit (i.e., ERA), this cannot highlight the influence of a densification strategy on the environmental impacts of
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building, as its surface will remain the same before and after densification. The second functional unit has the same issue: the number of employees per office is evaluated recognizing
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a ratio of 14 m²ERA per employee according to the SIA, which does not allow the possibility of changing the density in the assessments. In order to provide a complementary approach, we introduced three other functional units based on the real employee count. These three new units have different precision levels, from contract- to reality-based counting. First, the real number of employees is a basic count of the number of people using the building. The real number of employees refers to the people employed by the institution and are expected to work in the offices (ex: four real employees are counted even though their work contracts are respectively 100% full time; 40% part-time; 50% part-time and 60% part-time). For this reason, the second
unit, a full-time-equivalent employee, gives a better understanding of their possible presence based on their work contract. A new number of employees is considered by converting all parttime working employees to full-time. The conversion is made based on the percentage of their part-time work contracts. In this case the part-time working contracts are summed and converted to full time (ex: altogether the four employees present 100+40+50+60=250% or 2.5 full-time-equivalent employees). Employee mobility induces many absences for different reasons in the office for part or all day. This is the reason for introducing a third unit, which is highly accurate and measured with the Bluetooth bracelet as described in section 2.2.1. This unit is called effective-full-time-
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equivalent employees. Since the presence of full-time employees in the office is not 8 hours (ex: first employee stays in the office only 60% of his time; second employee 70%; third employee 50% and the last 90%), the adjusting conversion is made based on data measured from the Bluetooth bracelet. In the end, the number of effective full-time employees is calculated by multiplying the percentages of work contracts with their presence percentage; in
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the case of the example the results are (100x60)+(40x70)+(50x50)+(60x90)=167% or 1.67 effective-full-time-equivalent employees.
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Evaluating the environmental impacts of a building and its elements must follow the European norm EN-15978 (2011) and EN-15804 (2011), while assessment of furniture and appliances
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should fulfil the norm ISO-14040 (2006) since, until now, the evaluation of environmental impacts of furniture and appliance are not supported by specific norms. Including furniture and appliances in the study boundaries gives details about the evaluation of the building´s
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environmental impacts and can be found in Hoxha and Jusselme (2017). KBOB (2016) and the Ecoinvent database (Wernet et al., 2016) will be used for the assessment of the following three environmental indicators: cumulative energy demand (CED), non-renewable energy (CEDnr)
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(Bösch et al., 2007; Frischknecht et al., 2007), and global warming potential (GWP) (Intergovernmental Panel on Climate Change [IPCC], 2007). In Switzerland, these indicators
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are considered the most important for sustainable and equitable use of the world’s raw materials and energy according to the 2000-Watt Society vision (Jochem et al., 2004; SIA-D0236, 2011).
2.3.Generalization at building-scale level To evaluate the benefits of densification, the conclusions of the experiment have to be scaled to the building level. Doing so should overcome criticism about the limits of the boundaries of the field study by also considering impacts that might be not possible to monitor within the experiment’s perimeter, but also other building areas (hall, corridors, cafeterias, etc.). The
building-scale project should enlarge the concept of office densification at the building-scale level. Plans, the orientation of offices, and the surfaces of external windows and walls are criteria to consider. Energy consumption will be generalized through field measurements, and when no data is available they will be evaluated with the help of Design-Builder v. 5 dynamic simulation software (Crawley et al., 2001; Design-Builder Software v.5, 2018) following the base of the measurements during the experiment and the Swiss standard SIA 2024 (SIA-112, 2014; SIA-118, 2013; SIA-2024, 2015).
3. Case study Given the wide range of method application an interdisciplinary research team handled the
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experiment. Ecole Polytechnique Fédérale de Lausanne’s (EPFL) Building-2050 group implemented the experiment and developed the methodology for assessing environmental impacts. The Human-IST Institute of the University of Fribourg (UNI-FR) was responsible for measuring space occupancy.
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The methodology was applied within the spaces of two offices and two meeting rooms (Figure 3) situated within an academic building in Fribourg, Switzerland. This building was suitable
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for this intrusive experiment, as it is a living lab with occupants willing to participate. Also, energy meters were already available to measure the overall office consumption and its lighting
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factors.
Offices and meeting rooms were affected in the experiment since they composed the largest
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areas of academic buildings constituting the target. Used for the staff of two laboratories, each office and meeting room had a surface of 73 m2 of ERA. They had modular construction systems in which the principal materials employed were wood, concrete, and wood-wool.
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Triple-glazed windows with wood-aluminum frames were used for the openings. All building envelope elements had a thermal transmittance of 0.1 Wm-2K-1, except for the floor slab that
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was of 0.15 Wm-2K-1.
The post-densification layout was adapted from Myerson’s concept (Myerson and Bichard., 2016) of spaces for concentration and collaboration. Then, we proposed an activity-based work layout with what we called a “quiet room” and an “interactive room”. The quiet room targeted concentration and contemplation tasks without noisy activities or sources of distraction. The interactive room was designed for collaboration and communication tasks with spaces for sharing documents and the appropriate technological equipment to support remote collaboration. To increase the density, the desk-sharing concept was introduced by removing
the concept of private places since during a work day the employees may need to work in calm conditions or have discussions or meetings in loud voices. Consequently, all employees could choose and change offices during the day according to their schedules. Information about the materials and components used in offices during the pre- and post-densification phase are presented in the Supplementary Material section. The pre-densification phase of the experiment lasted three weeks, during employees in their private workplaces were supported by one laptop, one docking station, and one or two monitors. They had one private cabinet and at a minimum one metal tambour cabinet. When they were present at the office their place was always free and supported by these items.
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In the post-densification phase of the experiment, which lasted for four weeks, all employees were supported only with private cabinets in which they could put their personal equipment. In both offices, employees had to use their laptops. Unlike the pre-densification phase, all docking stations and metal tambour cabinets were removed in this phase of the experiment. Moreover, some workplaces were supported by monitors and some were not. Workplaces in the quiet room
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were also equipped with table lights. Except that loud discussions were not permitted in the quiet office no other rules were given to the employees. The interactive office was also
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supported with tables for meetings, in which employees could have short discussions, while the meeting rooms stayed unchanged during both phases of the experiment. These rooms were
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equipped only with tables, chairs, whiteboards, and projectors. These areas allowed for possible Skype discussions, long meetings with more than four employees, or private discussions. In post-densification, the number of employees working in offices almost doubled from the
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employees of the three other laboratories who were invited to work there. All differences between layouts of offices in the pre- and post-densification phases are presented in Figure 1. As highlighted, the concepts of offices were later generalized at building-scale by an archetype
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of the smart living building (Jusselme et al., 2016). Figure 4 illustrates the layout of one floor of this building. Hot water, cooling, and heating were not monitored within the experiment
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boundaries due to difficulties in implementing a separate count of these needs. Therefore, consumption differences before and after densification of these energy needs were disregarded. However, they were calculated according to SIA norms when scaling the results at the building level.
4. Results 4.1.Effective presence schedule
Figure 5 summarizes the presence schedule for both pre- and post-densification phases based on the data collected with the Bluetooth bracelets. These schedules are compared to what is proposed by Swiss norm SIA-2024 (2015), used by building designers to evaluate energy power and consumption for lighting, appliances, heating, and cooling. As shown, the results in Figure 5 highlight different densification rates between schedules. Thirteen employees worked in offices where the experiment took place. This number corresponds exactly to the number recommended by SIA-2024 (2015) norm (14 m2 energy-floor areas per employee), and the presence schedule in the pre-densification phase should be similar to that of SIA. Per contra, the real measurement of the pre-densification phase shows the occupancy rate is far
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below the schedule of design. The figure also presents the minimum and maximum presence of employees in all phases. The minimum and maximum correspond to a single day, which may not be the same for both. The important information is the maximum number of employees present in the office at the same time, which guides the number of working places the offices should have.
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There are two primary reasons for the differences between the SIA schedule and the one measured in the pre-densification phase. First, the academics (professors, researcher, PhD
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students, etc.) work in different places, maybe more than an average employee would. They
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spend time in meetings, conferences, or laboratories. Second, many researchers work part-time.
In the second phase of the experiment, the number of employees working in offices increased to 25. Based on the differences between measures of the pre- and post-densification phases with
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the presence schedule proposed by the SIA-2024 (2015), we conclude that in our case, using the standard rate of 14 m2 energy, floor area per employee would lead to substantial inaccuracies. Another interesting fact is that the real lunchtime phase is at 12:00 and not at
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13:00, as proposed by the norm. Also, some employees start work at 7:00 am, and some finish at 22:00 pm, highlighting the diversity of behaviors for each employee.
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Regarding the comparison between pre- and post-densification phases, we conclude that the number of employees increased by 92% (from 13 to 25), while the unit rate of full-timeequivalent employees increased by 73% (from 10.1 to 17.5), and the unit rate of effective fulltime-equivalent employees increased by 56% (from 3.57 to 5.56). The unit of effective fulltime-equivalent employees is calculated based on the graphics of office occupation in pre- and post-densification phases (the area under the graphic is divided by 8 hours). All the data about the number of employees, their percentage working contract, and the occupancy rate of offices with and without meeting rooms are given in the Supplementary Material section.
In the frame of this experiment, these functional unit differences lead to major variations in the calculations of the environmental impact when based on the effective presence, but are suspected to not change at all when based on the building surface or Swiss norms. However, densification will affect the building occupants’ comfort in many cases, and its consequences on working conditions must be limited and carefully evaluated.
4.2.Comfort perception Results of employee behavior in pre- and post-densification phases, evaluated with the help of the self-assessment manikin technique, are presented in Figure 6. The comparison between the
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answers received during pre-densification and post-densification are tested with the help of a ttest. This test shows the degree of differences between the two groups by comparing their average values (Garson. 2012). Based on the results obtained, we conclude there is not a significant difference (t-test is not significant at p < .01) in employee behavior between pre-
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and post-densification phases.
However, the intervals of responses for post-densification are larger than those of pre-
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densification, especially for pleasure and arousal parameters, demonstrating the highest varieties in emotional states. To identify if this comes from the densification of spaces, in Figure
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7 we present the average values of responses for the days surveyed in pre- and post-densification phases. The first conclusion drawn is that employees’ comfort was not influenced by the densification of spaces, as it is not possible to recognize a different pattern for each stage. Even
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though the densification rate was higher in the second phase of the experiment, the grades of each PAD parameter were not lower.
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Figure 8 presents more detailed results from the questionnaire for the parameters of pleasure and arousal. As noticed in the participant answers, the behavior perception was pleasant, except
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the day on which the experiment passed from pre- to post-densification, with all the logistical consequences involved. Another interesting result from the questionnaire is the employees’ responses regarding the quiet room, in which they felt calm, and in the interactive room, where they felt excited. Separating the offices into calm and interactive areas was the primary goal of the experiment. As a conclusion, we notice that the densification of offices did not induce any significant decrease in working comfort, but affected the arousal states according to the room in which the participants choose to work.
4.3.Environmental impacts Environmental impacts for CED, non-renewable energy (CEDnr), and GWP at room-scale are presented in Figure 9. Impacts are composed of four groups: building (A1-C4), equipment (A1C4), appliances and furniture, and use phase (B6). Embodied impacts from cradle-to-grave for a building´s components are included in the first group, for equipment in the second group, and for the appliances and furniture in the third. The last group considers the environmental impacts of the use phase (lighting and appliances). Based on the m2ERA/yr as the functional unit, the densification strategy of spaces increases the environmental impacts. The increment in the post-densification phase is mainly due to the
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increment of furniture with higher impacts and the number of appliances (mostly laptops). However, even if the number of computers was doubled because of densification, the energy consumed by appliances in the post-densification phase was not doubled. This is because most employees did not choose to use monitors and worked directly on their laptops. Conversely, the energy for lighting in the post-densification phase was lower. This was mainly from the quiet
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room, where employees preferred to use a table lamp instead of the ceiling light.
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Figure 10 presents the impacts evaluated on other functional units to better identify the influence of the densification of workspaces on the environmental impacts of the building. As
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stated, during the design phase of the building, the number of employees according to the Swiss norm SIA-2024 (2015) was evaluated thanks to the ratio that each employee averaged 14 m2 of energy floor area. Based on this unit, the number of employees is linked to the surface, and it
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is impossible to see any variation in the densification strategy. This is also the conclusion drawn from the results in Figure 9. For the three other units the environmental impacts of offices decreased by 90% for CED and CEDnr and 83% for GWP. These differences in percentage are
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evaluated by using the equation: 100% - (pre-densification impact / post-densification impact) x100. However, for a better evaluation of the environmental impacts of offices, the different
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working percentage of employees is converted into full-time equivalent employees. Therefore, in this unit the decrement of the indicators of CED, CEDnr, and GWP are, respectively, 71%, 70%, and 45%. Yet, the real-time presence of employees is still not considered in these units. To that end, the indicators of CED, CEDnr, and GWP, due to the real densification of office areas, were respectively reduced by 58%, 57%, and 52% on the unit effective-full-time equivalent employees.
To better understand the equations used for the calculation of results presented in Figures 9 and 10, the following example gives the details for GWP indicator for the post-densification phase of the experiment. The overall impact for the GWP indicator for both offices and meeting rooms has the value of 5.68 t CO2e/yr.
Using the energy reference area (ERA) per year as a functional unit, the value of the GWP indicator is divided by 270 m2 (147 m2 offices and 123m2 meeting rooms) and the obtained value is 21 kg CO2e/m2 per year.
Using the number of employees evaluated according to the Swiss norm SIA-2024, (2015) as a functional unit, the value of the GWP indicator is divided by 10.5 employees
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(each employee will be associated with 14 m2 of office area) and the obtained value is 0.54 t CO2e/employees according to (SIA-2024, 2015) per year.
Using the real number of employees as a functional unit, the value of GWP is divided by 25 employees and the obtained value is 0.227 t CO2e/employees per year.
Using the full-time-equivalent employees according to their administrative contract as
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a functional unit, the value of GWP is divided by 17.5 employees and the obtained value
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is 0.324 t CO2e/full-time-equivalent employees per year.
Using the effective-full-time-equivalent employees according to their effective presence
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as a functional unit, the value of GWP is divided by 5.6 employees and the obtained value is 1 t CO2e/effective-full-time-equivalent employees per year. Details for the calculation of all functional units about the number of employees, their
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percentage working contract, and presence in the pre- and post-densification phase are given in the Supplementary Material section.
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4.4.Environmental impacts at building scale Figure 11 summarizes the environmental impacts of the smart living building based on the unit
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m2ERA/yr. The figure highlights the building life-cycle impact in four groups: building (A1-C4), equipment (A1-C4), appliances and furniture, and use phase (B6). Each of these groups are further composed into building components and systems in the bottom part of the chart. Since the building is a generalization of offices as presented in the previous section, normally the results should have the same trend. Impacts in the post-densification phase are higher for CEDnr and GWP, but lower for the CED indicator. The results obtained for the indicator of CED highlight the effect of the non-heated area in the smart living building. Since normalization to the number of employees depends only on ERA, the increment of the impacts of building due
to non-heated areas does not follow the same trend as does normalization. As a result, the numerator (environmental impacts) increases with a different power than the denominator (number of employees). For the CED and CEDnr indicators, the use phase contributes, respectively, 44% and 50% in pre-densification of the experiment. the impacts are the largest contributors for the GWP indicator, with around 86% in both phases of the experiment. However, the impacts between the two phases of the experiment in almost all phases are not significantly different. In the postdensification phase the appliances and furniture have higher impacts than those in the predensification phase.
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This can be justified by the layout of the offices in the post-densification phase changing with furniture of better quality and, consequently, with higher impacts. Moreover, the number of appliances, especially laptops and monitors, is another reason for the increment of the embodied impacts of appliances, which share a larger impact compared to furniture.
Within the embodied impact of building elements (A1-C4), horizontals (slabs and roof) make
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the most substantial contribution, followed by verticals (external envelope). Embodied impacts of the ventilation system and electrical equipment represent the largest impacts in the group of
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environmental impacts of equipment (A1-C4).
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Figure 12 presents the environmental impacts of the smart living building on the unit of employees. Based on the number of employees evaluated according to the SIA-2024 (2015) norm, the results followed the same trend as those at room-scale, even for the CED indicator,
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where the environmental impacts in the post-densification phase are higher. As stated previously, the results make the m2 ERA/yr. unit less appropriate for evaluating the benefits of densification. For all other proposed indicators, units highlight benefits of up to 50%.
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For the real-employees unit the results show a minimization of CED, CEDnr, and GWP by 93%, 92%, and 83%, respectively. For the full-time-equivalent-employees unit the decrement
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of indicators of CED, CEDnr, and GWP are, respectively, 73%, 72%, and 64%. For the effective-full-time-equivalent-employees unit the minimization of indicators CED, CEDnr, and GWP is evaluated at 60%, 59%, and 52%, respectively. Each of these decreases is mainly due to the number of employees using the office that doubled.
Between the three user-centered functional units, the one-per-employee is the easiest to calculate as it does not need any tracking technology. However, it overestimated the benefits, as the real presence of employees is far below 100% effective presence. Even the second unit
full-time-equivalent employee does not reflect the effective presence of employees, as made by the last unit. Finally, the results obtained for the smart living building are similar to those obtained at roomscale by strengthening the robustness of the conclusions.
5. Discussion The results presented in this article can be useful during the use phase of the building. This statement came as a result of an experiment in the offices of an academic building; the results previously presented can only be useful for this type of building typology. Although this
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statement is almost true for the results, it is not true for the methodology, which can feasibly be applied to other building typology. The importance of this type of experiment is dictated by the large differences between presence schedules proposed by SIA-2024 (2015) and those measured during the experiment. This gap shows the impossibility of the presence schedule recommended by the norm to accurately cover all office building typologies. Based on that, we
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recommend our presence schedule for a more accurate evaluation of energy for heating, cooling, lighting, and appliances only for the case of academic buildings, and additional
sectors, like production and industry.
re
experiments for other building typologies since the results presented are not valid for other
lP
The presence schedule proposed in this article was measured for one month. This time span can be considered a criticism for a robust representation of the whole year, but was the largest time employees agreed to stay under observation and participate in the experiment. Their choice is
na
fully understandable since on each day they were obliged to wear the bracelet (for tracing their position), to come from other universities to work in the office where the experiment took place (the previous offices of half the participants included in the post-densification phase of the
ur
experiment were not in the building where the experiment took place), and to reply to the questionnaire every day. According to expert judgement, the phase of one month per each phase
Jo
of the experiment was found to be robust and representative. Comparing the presence schedule measured for a whole year by Menezes et al. (2012) and Duarte et al. (2013) with those presented in this manuscript (Figure 5), we observed they are similar for the post-densification phase. Of note is that the curves presented in (Menezes et al., 2012; Duarte et al., 2013) compared with that presented in this study (Figure 5) differ from each other only in how they are distributed over 24 hours. This comparison strengthens the conclusion that the phase of one month was sufficient for measuring the rate of presence of employees in their offices.
The results of the comfort perception questionnaire are another point deserving discussion. Among several methods and techniques recommended by the scientific community for evaluating working comfort, we set up a PAD questionnaire. The limitation of this method for a detailed evaluation of working comfort should be noted. However, this technique is the most relevant for a simple, generally accepted, and rapid daily evaluation of behavior perception. Moreover, evaluations of behavior perception are not biased, since all employees answered the questionnaire without knowing the objective of the experiment, which was the densification of workspaces. The employees of one laboratory applied this concept to their office after the experiment, which deserves to be highlighted and reinforces the validity of the PAD survey.
ro of
This allows for confirmation that the results of working comfort are robust. Assessing impacts at the office room-scale presents limits in the boundary of the study evaluated and allocation issues. For this reason, the assessment was also applied at building scale. Since the results obtained at the office room and building scale were almost alike, criticism of the boundary of the study and allocation of impacts is no longer valid.
-p
We demonstrated that the functional unit energy floor area per year, which is generally used by researchers (Peuportier et al., 2001; Rashid and Yusoff, 2015; Lasvaux et al., 2017; Hoxha et
re
al., 2017) and recommended by European norm EN-15978 (2011) for evaluating the environmental impacts of the building, was insufficient. For this reason, we introduce a
lP
complementary functional unit full-time-equivalent employee, which is most suitable for the assessment of environmental impacts of a new project during the design phase. This unit is more general and allows a more robust evaluation. However, it should be accepted that it is not
na
easy to know the real number of employees and their percentage of work time during the early design phase of a building. This can be improved by statistical studies in similar buildings or by a short tracking campaign of the targeted population.
ur
Although the presented research primarily has the character of an exploratory study, several points are validated and can be used for the improvement of Swiss norm SIA-2024, (2015) and
Jo
European EN-15978 (2011) to improve the robustness of the evaluation of the results. In addition, the results presented in this manuscript are valid only for offices.
6. Conclusion Results presented in this article show the effectiveness of space densification for minimizing a building’s environmental impacts. Through a real-time experiment, we demonstrated the possibility of densification of working areas while limiting impacts on employee comfort. Separating the academic office building into calm and interactive shared spaces might enable
densification of spaces by almost doubling the number of employees. Although this separation increases the embodied impacts of the new layout due to the requirement of high-quality furniture and more electrical equipment, the overall environmental impacts of the building per employee are greatly minimized. Moreover, novel units introduced for the first time evaluated the improvement of the environmental impacts of a building. In the unit of full-time-equivalent employees due to densification of spaces, the indicators of cumulative energy demand, non-renewable energy, and global warming potential were minimized by 71%, 70%, and 58%, respectively. To strengthen the reliability of the results, this functional unit is suitable for use in the early design
ro of
phase of the building project - if the future building occupants are already known at that stage. As for the unit effective-full-time equivalent employees, the indicators of cumulative energy demand, non-renewable energy, and global warming potential were minimized by 58%, 57%, and 52%, respectively. This unit suits mostly for a better evaluation of the environmental impacts of buildings during the use phase. This unit is more suitable for increasing accuracy in
-p
evaluating the performance of the environmental impact of building stock.
Surface-based functional units are more conveniently used at the early beginning of the design
re
process, as surfaces are always easy to measure. However, they cannot demonstrate or support a densification strategy improving the building performance. Thus, this paper demonstrated the
lP
relevance of using user-based units to have a better benchmark of building performances in use. This exploratory study calls for future research regarding a more holistic understanding of the comfort consequences of office space densification. Also, easy-to-use user counting techniques
na
must be developed, facilitating real occupancy rate monitoring.
ur
7. Supplementary Material data
Jo
Data about employees
Necessary data for the functional units
Office scale Pre-densifi Post-Densif
Building scale Pre-densifi Post-Densifi
270
5255
10,5
227
Energy reference area (m2) Number of employees evaluated according to the Swiss norm SIA-2024, (2015) Real number of employees Full-time-equivalent employees according to their administrative contract
13
25
234
450
10,1
17,5
182
315
Effective-full-time-equivalent employees according to their effective presence
5,56
64
100
Post-Densification
Employee 1
100%
100%
Employee 2
100%
100%
Employee 3
60%
60%
Employee 4
100%
100%
Employee 5
100%
100%
Employee 6
50%
50%
Employee 7
50%
50%
Employee 8
50%
50%
Employee 9
100%
100%
Employee 10
100%
Employee 11
80%
Employee 12
80%
Employee 13
40%
Employee 14
-
Employee 15
-
Employee 16
-
Employee 17
-
Employee 18 Employee 19 Employee 21 Employee 22
ur
na
Employee 23 Employee 25
80% 80% 40%
-p
20% 20% 40% 50%
-
100%
-
100%
-
100%
-
80%
-
80%
-
50%
-
50%
-
50%
Time.Interval
00:00-6:00
7:00
8:00
9:00
10:00
11:00
12:00
6-March
-
0,40
3,68
5,08
3,42
5,38
4,35
7-March
-
0,55
4,50
4,45
4,37
5,05
3,10
8-March
-
0,57
2,18
2,13
2,75
2,30
0,98
9-March
-
1,62
4,48
3,70
4,45
4,32
3,50
10-March
-
1,07
3,30
3,92
0,85
1,00
1,70
13-March
-
0,57
4,80
5,03
3,85
4,45
3,37
14-March
-
0,83
3,68
4,68
5,50
0,05
1,75
15-March
-
0,60
2,90
4,72
4,47
2,90
3,43
16-March
-
0,78
3,93
5,90
5,57
3,68
2,40
Jo PRESENCE DURING PREDENSIFICATION
100%
lP
Employee 20
Employee 24
ro of
Pre-Densification
re
Working time percentage
3,57
0,40
2,35
3,42
3,37
3,58
2,93
20-March
-
0,43
1,87
3,30
5,25
4,70
2,40
21-March
-
1,05
4,42
4,68
2,88
4,93
2,43
22-March
-
0,58
3,45
4,30
5,05
4,23
2,77
23-March
-
0,60
3,83
4,87
3,88
4,48
1,65
24-March
-
0,37
3,07
4,48
3,02
3,67
2,57
27-March
-
0,00
0,00
0,00
0,95
1,58
0,27
28-March
-
0,00
1,33
2,75
2,93
1,58
0,00
10-April
-
0,45
2,30
4,05
4,58
4,77
1,32
11-April
-
0,20
1,42
1,77
4,40
5,72
3,73
12-April
-
0,72
2,32
2,38
2,45
2,05
0,22
13-April
-
0,33
1,48
1,20
3,17
3,27
1,05
14-April
-
0,00
0,00
0,00
0,00
0,03
0,05
18-April
-
0,53
1,98
3,25
4,52
3,78
1,50
19-April
-
0,00
0,28
1,83
4,40
5,97
3,17
20-April
-
0,42
2,12
2,35
1,68
1,87
2,35
21-April
-
0,43
2,30
2,60
2,43
1,77
1,55
22-April
-
0,02
0,15
0,15
0,02
0,02
0,08
24-April
-
0,48
2,68
25-April
-
0,47
1,79
26-April
-
0,72
1,53
27-April
-
0,45
28-April
-
0,63
1-May
-
0,00
2-May
-
3-May 4-May
-p
ro of
-
4,02
3,52
2,02
3,35
2,63
2,20
2,31
2,70
3,26
2,67
1,90
1,97
0,98
1,95
3,03
2,04
1,95
1,61
1,40
1,72
1,48
0,70
4,03
3,55
3,42
2,73
0,37
2,37
4,67
5,10
0,85
0,15
-
0,77
2,75
4,92
5,17
5,88
1,65
-
0,42
4,68
7,45
7,62
7,58
3,53
-
0,37
3,85
5,78
5,67
6,47
2,97
-
0,42
2,42
4,12
4,08
3,03
2,52
-
1,73
4,97
5,95
5,85
6,35
2,70
-
0,60
4,98
6,28
6,52
6,72
2,22
11-May
-
0,43
3,83
5,47
4,02
5,25
3,43
12-May
-
1,15
3,78
3,88
4,60
4,78
2,17
15-May
-
0,38
3,75
5,93
7,85
8,92
1,18
16.May
-
0,75
2,23
3,27
4,87
3,58
1,32
17-May
-
0,32
1,25
3,73
5,07
5,52
2,62
18-May
-
0,50
2,70
5,52
6,20
3,12
0,70
19-May
-
0,70
2,12
2,68
4,23
2,60
1,13
22-May
-
0,00
0,45
3,08
3,72
7,22
2,55
23-May
-
0,00
0,00
2,03
5,33
5,57
1,08
24-May
-
0,20
1,97
5,93
6,42
6,07
3,22
26-May
-
0,00
0,47
2,68
2,92
3,65
1,95
8-May 9-May
lP
ur
10-May
na
5-May
re
3,37
Jo
PRESENCEDURING POST-DENSIFICATION
17-March
14:00
15:00
16:00
17:00
18:00
19:00
20:00
21-23
6-March
5,40
5,17
2,57
4,22
4,47
0,72
-
-
-
7-March
2,90
3,32
3,72
3,48
4,00
0,28
-
-
-
8-March
2,08
2,62
2,62
2,55
2,37
0,58
-
-
-
9-March
4,98
5,02
5,07
4,72
3,28
1,88
1,35
0,32
-
10-March
3,07
3,80
3,22
3,02
1,55
0,28
-
-
-
13-March
2,15
3,43
3,05
3,30
2,75
1,27
0,20
-
-
14-March
4,75
2,77
5,32
4,62
2,55
0,18
-
-
-
15-March
3,45
4,65
3,45
3,80
4,15
1,12
-
-
-
16-March
2,03
4,12
4,12
3,83
3,00
0,48
-
-
-
17-March
1,85
3,28
2,20
1,93
1,73
0,52
-
-
-
20-March
3,38
2,28
3,52
3,80
3,65
0,52
-
-
-
21-March
4,58
3,92
1,88
1,35
1,60
0,35
-
-
-
22-March
2,45
4,38
3,58
4,07
2,57
0,00
-
-
-
23-March
2,30
3,60
2,80
2,57
2,35
0,37
-
-
-
24-March
3,02
4,30
4,12
3,50
1,60
0,00
-
-
27-March
0,00
0,97
0,75
0,92
0,87
0,05
-
-
-
28-March
1,63
1,92
1,37
1,83
1,08
0,00
-
-
-
10-April
3,22
4,02
3,08
11-April
4,70
4,92
3,80
12-April
2,92
2,17
1,85
13-April
2,78
3,28
1,95
14-April
0,03
0,00
18-April
1,85
19-April
-p
3,35
3,08
0,33
-
-
-
2,32
2,40
0,32
-
-
-
2,65
1,30
0,13
0,07
-
-
1,18
1,03
0,77
-
-
-
0,00
0,00
0,00
0,00
-
-
-
2,37
1,75
1,80
0,83
0,03
-
-
-
2,65
4,25
3,57
2,98
2,20
0,03
-
-
-
20-April
2,37
2,92
1,68
1,00
0,70
0,33
-
-
-
21-April
1,10
2,00
1,72
1,30
1,05
0,32
0,27
-
-
22-April
0,95
0,90
0,60
0,23
0,08
0,32
0,68
0,68
-
24-April
0,98
2,75
2,62
1,88
1,07
0,13
-
-
-
25-April
2,08
2,22
2,53
2,78
1,52
0,18
-
-
-
26-April
2,54
2,90
1,73
2,00
1,34
0,00
-
-
-
27-April
2,76
1,96
1,40
2,40
2,48
1,43
0,28
-
-
28-April
2,29
2,57
2,37
2,68
2,64
1,52
0,20
0,03
-
1-May
0,85
3,52
3,35
4,05
3,45
1,93
0,95
0,48
-
2-May
6,47
7,72
5,63
5,03
4,95
1,22
-
-
-
3-May
3,42
5,78
5,60
6,90
5,45
2,97
1,27
0,05
-
4-May
3,08
5,33
6,90
5,45
4,93
1,53
0,68
-
-
5-May
4,17
3,72
2,60
2,45
1,67
0,22
-
-
-
8-May
3,02
2,55
2,88
3,43
2,72
0,68
-
-
-
lP
ur
PRESENCE POSTDENSIFICATION
re
-
na
ro of
13:00
Jo
PRESENCE PRE-DENSIFICATION
Time.Interval
4,00
3,83
3,05
2,97
2,78
0,40
-
-
-
10-May
4,25
7,32
7,90
6,00
3,75
1,08
-
-
-
11-May
6,33
7,25
5,97
6,20
3,70
0,12
-
-
-
12-May
3,12
5,47
5,93
6,67
3,88
1,97
0,10
-
-
15-May
3,62
8,05
9,92
7,77
2,30
1,40
-
-
-
16.May
3,72
6,28
5,18
6,98
4,52
0,80
-
-
-
17-May
4,05
5,17
6,98
6,95
3,48
0,32
-
-
-
18-May
2,20
4,72
3,67
3,42
2,78
0,57
-
-
-
19-May
2,27
4,02
2,62
2,82
2,28
0,33
-
-
-
22-May
3,35
6,90
5,05
6,42
2,77
0,82
-
-
-
23-May
2,82
4,78
4,72
4,58
1,97
0,00
-
-
-
24-May
4,45
6,10
7,20
3,92
2,92
0,72
-
-
0,02
26-May
1,40
3,47
3,27
3,67
3,40
1,52
-
-
-
ro of
9-May
Data of presence measurement
1
SIAMean Averag e 0,00
AverageMinimum 0,00
Avera ge 0,00
2
0,00
0,00
0,00
3
0,00
0,00
0,00
4
0,00
0,00
5
0,00
0,00
6
0,00
0,00
7
0,00
0,00
8
0,00
9
2,10
10
6,30
11
10,50
12
Post-Densification
AverageMinimum 0,00
Avera ge 0,00
MaximumAverage 0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,45
0,50
4,50
0,43
0,64
3,36
2,38
2,66
6,34
2,64
3,09
6,91
3,61
3,76
5,24
3,07
5,22
5,78
3,96
4,09
6,91
3,04
6,00
6,00
10,50
3,69
4,17
5,83
3,37
6,05
5,95
8,40
2,17
2,49
5,51
1,50
2,69
7,31
na
lP
re
MaximumAverage 0,00
Jo
13
Pre-Densification
ur
Time.Inte rval
-p
Presence measurement comparisons (offices and meeting rooms)
14
4,20
2,30
3,04
5,96
2,15
4,05
9,95
15
6,30
3,12
3,89
6,11
2,65
6,16
4,84
16
10,50
3,49
3,65
7,35
3,15
6,10
7,90
17
8,40
3,34
3,52
7,48
2,35
5,88
7,12
18
6,30
2,23
2,42
8,58
1,96
3,94
6,06
19
2,10
0,43
0,53
4,47
1,04
1,15
5,85
20
0,00
0,45
0,45
1,55
0,91
1,01
1,99
21
0,00
0,35
0,35
0,65
0,58
1,06
1,94
22
0,00
0,10
0,10
0,08
0,02
0,02
1,98
23
0,00
0,00
0,00
0,00
0,00
0,00
2,00
24
0,00
0,00
0,00
0,00
0,00
0,00
0,00
Presence measurement only in offices Time.Interval
Pre-densification Average Min
Max
Post-densification Average Min
Max
0,00
0,00
0,00
0,00
0,00
0,00
1
0,00
0,00
0,00
0,00
0,00
0,00
2
0,00
0,00
0,00
0,00
0,00
0,00
3
0,00
0,00
0,00
0,00
0,00
0,00
4
0,00
0,00
0,00
0,00
0,00
0,00
5
0,00
0,00
0,00
0,00
0,00
0,00
6
0,00
0,00
0,00
0,00
0,00
0,00
7
0,51
0,02
1,62
0,61
0,20
1,73
8
2,44
0,15
4,80
2,74
0,45
4,98
9
3,09
0,15
5,90
4,60
2,03
7,45
10
3,23
0,02
5,57
5,20
2,92
7,85
11
3,13
0,02
5,97
5,08
0,85
8,92
12
2,01
0,05
4,35
2,10
0,15
3,53
13
2,60
0,03
5,40
3,50
0,85
6,47
14
3,09
0,90
5,17
5,37
2,55
8,05
15
2,62
0,60
5,32
5,18
2,60
9,92
16
2,56
0,23
4,72
5,04
2,45
7,77
17
2,04
0,08
4,47
3,35
1,67
5,45
18
0,45
0,00
1,88
1,03
0,12
2,97
19
0,44
0,07
1,35
0,75
0,10
1,27
20
0,34
0,03
0,68
0,27
0,05
0,48
21
0,00
0,18
0,18
0,02
0,00
0,02
22
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
-p
re
lP
na
23
ro of
0
ur
Presence measurement offices and meeting rooms
Time.Interval
Average
Max
Post-Densification Average Min
Max
0,00
0,00
0,00
0,00
0,00
0,00
1
0,00
0,00
0,00
0,00
0,00
0,00
2
0,00
0,00
0,00
0,00
0,00
0,00
3
0,00
0,00
0,00
0,00
0,00
0,00
4
0,00
0,00
0,00
0,00
0,00
0,00
5
0,00
0,00
0,00
0,00
0,00
0,00
6
0,00
0,00
0,00
0,00
0,00
0,00
7
0,50
0,05
5,00
0,64
0,22
4,00
8
2,66
0,28
9,00
3,09
0,45
10,00
9
3,76
0,15
9,00
5,22
2,15
11,00
Jo
0
Pre-densification Min
4,09
0,13
11,00
6,00
2,97
12,00
11
4,17
0,48
10,00
6,05
2,68
12,00
12
2,49
0,32
8,00
2,69
1,18
10,00
13
3,04
0,73
9,00
4,05
1,90
14,00
14
3,89
0,77
10,00
6,16
3,52
11,00
15
3,65
0,17
11,00
6,10
2,95
14,00
16
3,52
0,18
11,00
5,88
3,53
13,00
17
2,42
0,18
11,00
3,94
1,98
10,00
18
0,53
0,10
5,00
1,15
0,12
7,00
19
0,45
0,00
2,00
1,01
0,10
3,00
20
0,35
0,00
1,00
1,06
0,48
3,00
21
0,10
0,00
0,18
0,02
0,00
2,00
22
0,00
0,00
0,00
0,00
0,00
2,00
23
0,00
0,00
0,00
0,00
0,00
0,00
Data of behavior perception
13.04.17 Office 2
6
2
P2
7
5
P3
6
P1
7
P2
7
5 3
5
7
7
5
7
6
6
5
5
5
8
7
3
5
P1
8
8
2
P2
7
6
7
P3
3
5
5
P1
7
3
5
P2
9
5
3
P3
7
7
4
P1
7
5
4
P1
8
4
4
P2
3
3
8
P1
8
8
2
P2
5
5
5
P1
7
5
5
P2
7
3
1
P3
7
7
2
P1
7
5
2
na
P3
Office 1
Jo
19.04.17
ur
Office 2
Office 2 Office 1
20.04.17
Office 2
24.04.17
Office 1 Office 2
Averages Arousal Dominance
3
7
P2
Pleasure
5
7
P1
18.04.17
Answer Arousal Dominance
P1
P3 Office 1
Pleasure
-p
Office 1
Person
re
Office
lP
Pre-exp
ro of
10
6,7
5,2
5,0
6,0
5,5
5,3
7,5
5,0
4,0
6,0
5,0
4,8
6,9
5,0
2,9
Office 2
Office 1 27.04.17 Office 2
28.04.17
Office 2
P3
8
8
4
P4
6
4
3
P1
9
1
1
P2
7
5
5
P3
7
7
5
P1
8
5
5
P2
6
6
6
P1
7
6
5
P2
5
7
7
P3
5
7
5
P4
8
4
3
P1
7
5
3
P2?
5
7
3
P3
7
7
5
P4
7
3
5
P1
6
5
6
P2
8
3
3
P3
8
9
1
P1
6
7
P2
7
4
P3
5
2
4
P2
5
5
7
6
4
3
3
3
6
7
5
5
P1
6
4
5
P2
7
3
3
P3
5
7
7
P1
5
5
5
P2
5
4
5
P3
5
4
4
P1
7
6
4
P2
7
5
5
P3
7
3
6
P4
7
5
5
P5
5
5
2
P6
9
9
1
P1
5
5
5
P2
7
3
5
Quite
P3
8
7
1
Interactive
P1
7
7
4
P4
na
P5
ur
Interactive
Jo
Quite
02.05.17
Interactive
03.05.17
6,3
6,0
5,0
5,6
3,7
4,3
4,3
5
3
01.05.17
4,4
6,0
5
P3
4,8
6,9
3
Quite
P1
7,4
3
lP
26.04.17
3
ro of
Office 2
3
-p
25.04.17
6
re
Office 1
P2
5,4
4,0
5,1
6,3
5,1
4,1
7,1
6,3
3,7
05.05.17 Interactive
P3
9
9
3
P4
7
7
5
P1
5
5
3
P2
4
4
9
P3
4
4
4
P4
7
5
5
P5
5
6
5
P1
7
6
1
P2
9
9
3
P3
7
5
5
P4
6
6
5
P5
7
8
2
P6
8
5
2
P1
7
6
5
P2
7
3
4
P3
4
3
3
P1
8
7
6
P2
9
9
1
P3
7
5
P4
7
7
P5
7
7
P6
9
P7
7
P8
9
P1 Quite
P2 P3
08.05.17
Jo
Quite
09.05.17
Interactive
10.05.17
Quite
Interactive
5
6
5
7
3
4
4
7
6
4
5
7
3
5
9
5
2
6
5
2
P2
7
7
3
P3
7
7
2
P1
4
5
6
P2
7
5
3
P3 P1
9
1
5
9
1
5
P2
7
7
5
P3
8
6
3
P4
7
7
5
P5
9
7
3
P1
7
6
3
P2
7
3
3
P3
7 8
5
5
2
4
8
6
3
P4 P1
4,0
7,4
6,1
3,9
6,6
5,0
3,7
7,5
4,9
4,4
7,0
4,7
3,7
3
P1
ur
Interactive
5,7
5
7
na
P4
3
6,3
ro of
Quite
3
-p
Interactive
6
re
04.05.17
7
lP
Quite
P2
12.05.17 Interactive
Quite
15.05.17
P3
4
5
7
P1
1
1
9
P2
7
5
5
P3 P1
7
7
9
6
7 3
P2
6
5
5
P3
7
5
1
P4
6
4
4
P5
3
5
7
P1
7
7
5
P2 P1
7
5
7
6
4
5
P2
9
3
3
P3
9
8
1
P4
7
4
3
P5
5
5
7
P6
7
7
5
P1
7
4
4
P2
8
3
P3
6
5
P4
6
5
P5
7
P6 P1
6
P2 Interactive
P3 P4
Jo
16.05.17
Interactive
Quite 17.05.17 Interactive
7,1
5,4
4,5
6,7
4,6
4,2
6,8
5,0
4,4
7,2
6,1
4,0
5
4
4
7
3
8
4
1
5
4
6
7
6
3
6
3
4
P6
7
5
5
P1
6
5
5
P2
8
5
3
P3
7
5
3
P4 P1
6
5
6
6
4
6
P2
7
5
5
P3
7
6
3
P4
7
5
4
P1
7
6
3
P2
9
7
1
P3
7
7
3
P4 P1
7
7
5
8
7
3
P2
7
7
2
P3
8
6
4
ur
Quite
5,1
7 6
na
P5
4,8
2
5
7
5,8
ro of
Quite
1
-p
Interactive
6
re
11.05.17
8
lP
Quite
P2
19.05.17 Interactive
Quite
22.05.17
Interactive
P5
8
5
5
P6
5
5
7
P7
5
3
5
P1
5
3
6
P2 P1
3
3
6
5
5
5
P2
7
7
4
P3
8
4
5
P4
9
7
5
P1
6
6
5
P2
8
3
3
P3
7
7
4
P4 P1
9
7
5
3
5
5
P2
6
3
5
P3
7
5
7
P1
9
7
5
P2
7
6
3
P3
7
5
P4
5
7
P5
8
3
P6 P1
8
P2
7
P3 P4 P5
7
3
6
5
7
5
5
6
6
2
7
3
3
7
2
3
6
7
5
P3
9
7
3
P4
7
7
3
P5
ur
8
2
2
P6
7
5
5
P7 P1
5
7
5
7
5
3
Interactive
P2
8
8
3
P3
5
1
7
P1
7
7
3
P2
8
5
5
P3
8
2
3
P4
7
3
5
P5 P1
9
5
2
7
7
4
P2
8
6
1
Jo
23.05.17
Quite 24.05.17
Interactive
6,6
5,1
4,9
7,1
5,5
3,4
6,9
5,1
3,9
7,6
4,8
3,5
1
P2
Quite
5,2
3 2
na
P1
4,8
5
6
7
6,2
ro of
Quite
6
-p
Interactive
7
re
18.05.17
8
lP
Quite
P4
7
3
5
P1
4
7
8
Quite
P2
6
8
7
7
5
5
Interactive
P3 P1
6
8
5
5,8
7,0
6,3
Jo
ur
na
lP
re
-p
ro of
26.05.17
P3
oo
f
Inventory data
Office scale (Pre-Densification) Material
Intermediate slab
Interior walls
CEDnr (MJ)
GWP (kg CO2)
60
106,3
104,5
14,5
10.005
kg
60
0,8
0,8
0,1
1.003
kg
60
13,5
12,8
0,7
06.003
kg
25
57,4
33,5
2,2
11.014
10710
kg
60
39,4
8,1
0,4
07.002
40720
kg
60
3,0
2,8
0,3
01.041
Concrete
71970
86362
Reinforcing steel
1840
2204
Linoleum
1325
1586
Wood
8925
Precast concrete element
33935
Linoleum
kg
1325
1584
kg
25
57,4
33,5
2,2
11.014
5555
5650
kg
60
26,9
2,9
0,1
07.011
2830
2874
kg
40
38,0
12,7
0,7
10.009
17,5
22
m2
40
1856,8
1741,5
109,5
05.016
2
na l
Triple glazing, U<0.6 W/m2K Windows
KBOB
CED (MJ)
264
Soft fiberboard
Environmental impacts per unit
year
220
Wood
Service life
pr
Slab
Unit
e-
Extruded polystyrene
Quantity Interaction Quiet
Pr
Component
Wooden frame
7,5
9,4
m
40
4623,2
2063,1
128,6
05.005
Columns
Wood
55
54
kg
60
39,4
8,1
0,4
07.002
Electical box
Wood
105
104
kg
60
39,4
8,1
0,4
07.002
Jo ur
Heating distribution Ventilation
2
123
147
m ERA
25
110,9
94,8
5,8
31.021
123
147
m2 ERA
25
729,3
675,9
43,6
32.011
2
Sanitary equipment
123
147
m ERA
25
74,6
70,3
4,5
33.001
Electical equipment
123
147
m2 ERA
25
683,9
409,8
23,9
34.002
3130
kWh
1
3,008
2,52
0,102
45020
1910
kWh
2585,2
2411,1
148,9
Lighting
Appliances
7
9
unit
1 5
Monitors
7
13
unit
6
1795,5
1671,7
120,4
Docking station
7
9
unit
5
123,0
105,8
7,1
Laptops
Hoxha et al. 2017
9
unit
5
Mouse
7
9
unit
5
Working table
11
15
unit
Working chair
7
9
unit
7,5
7,5
unit
Sofa
1
0
Meeting chair
15
21
Round table
1
1
Metal tambour cabinets
8
Boiler
1
White board
12 3
Jo ur
na l
Clothes hanger
269,0
18,9
79,0
72,3
5,0
20
1235,8
611,3
67,8
15
1346,9
1245,3
82,3
15
812,1
680,5
63,5
pr 20
5515,8
4098,5
240,0
unit
15
304,4
286,4
18,1
unit
15
727,0
312,9
33,5
13
unit
20
2130,5
1878,5
160,4
0
unit
10
87,7
85,1
4,1
12
unit
15
28,8
26,8
1,5
3
unit
15
218,1
209,2
16,4
e-
unit
Pr
Cabinets
293,2
f
7
oo
Keyboards
Quantity Interaction Quiet
Material
Intermediate slab
Interior walls
264
kg
Concrete
71970
86362
kg
Reinforcing steel
1840
2204
Linoleum
1325
1586
Wood
8925
10710
Precast concrete element
33935
Linoleum
1325
Wood
5555
Soft fiberboard
2830
Triple glazing, U<0.6 W/m K Windows Columns
Wood
Electical box
Wood
Heating distribution
Jo ur
Sanitary equipment
Electical equipment Lighting
GWP (kg CO2)
BL
106,3
104,5
14,5
10.005
BL
0,8
0,8
0,1
1.003
kg
BL
13,5
12,8
0,7
06.003
kg
25
57,4
33,5
2,2
11.014
kg
BL
39,4
8,1
0,4
07.002
40720
kg
BL
3,0
2,8
0,3
01.041
1584
kg
25
57,4
33,5
2,2
11.014
5650
kg
BL
26,9
2,9
0,1
07.011
2874
kg
40
38,0
12,7
0,7
10.009
2
22
m
40
1856,8
1741,5
109,5
05.016
7,5
9,4
m2
40
4623,2
2063,1
128,6
05.005
55
54
kg
BL
39,4
8,1
0,4
07.002
105
104
kg
BL
39,4
8,1
0,4
07.002
123
147
m2 ERA
25
110,9
94,8
5,8
31.021
123
147
m2 ERA
25
729,3
675,9
43,6
32.011
123
147
m2 ERA
25
74,6
70,3
4,5
33.001
25
683,9
409,8
23,9
34.002
3,008
2,52
0,102
45020
123
Appliances Laptops
CEDnr (MJ)
17,5
na l
Wooden frame
147
2
m ERA
3000
kWh
1
1801
kWh
1
12
15
unit
5
2585,2
2411,1
148,9
4
10
unit
6
1795,5
1671,7
120,4
Docking station
0
0
unit
5
123,0
105,8
7,1
Keyboards
12
13
unit
5
293,2
269,0
18,9
Monitors
KBOB
CED (MJ)
pr
220
2
Ventilation
Service life
e-
Slab
unit
Pr
Extruded polystyrene
Environmental impacts per unit
oo
Component
f
Office scale (Post-Densification)
Hoxha et al. 2017
15
unit
5
Working table
8
6
unit
20
Working chair
4
10
unit
2,5
1,5
unit
Sofa
0
0
unit
Meeting chair
15
17
Round table
1
0
Metal tambour cabinets
0
0
Boiler
1
72,3
5,0
1235,8
611,3
67,8
15
1346,9
1245,3
82,3
15
812,1
680,5
63,5
20
5515,8
4098,5
240,0
pr
Cabinets
79,0
f
12
oo
Mouse
15
304,4
286,4
18,1
unit
15
727,0
312,9
33,5
unit
20
2130,5
1878,5
160,4
0
unit
10
87,7
85,1
4,1
10,3
unit
15
28,8
26,8
1,5
3
unit
15
218,1
209,2
16,4
2
0
unit
20
163,7
146,9
9,6
1
0
unit
20
326,1
293,2
20,6
1
0
unit
20
2682,7
2094,8
214,0
2
0
unit
20
104,5
95,8
11,3
14
0
unit
15
457,3
428,3
28,4
45
16,7
kg
20
63,1
62,1
2,5
6,3
0
kg
20
4,0
3,8
0,8
1
0
unit
15
339,8
174,4
22,5
Sofa chair
2
0
unit
15
364,6
67,3
5,3
USM - Storage (Pos. E)
0
1
unit
20
4050,5
3600,1
261,3
USM - Storage (Pos. F)
0
1
unit
20
7980,5
7334,9
865,9
USM - Storage (Pos. G)
0
1
unit
20
6655,0
6116,6
722,1
USM - Storage (Pos. H)
2
3
unit
20
3221,0
2960,6
349,3
USM - Storage (Pos. I)
0
1
unit
20
1772,8
1629,4
192,4
USM - Storage (Pos. J)
1
0
unit
20
4368,5
3991,5
471,0
USM - Storage (Pos. K)
1
0
unit
20
7434,8
6793,1
801,8
White board
12,1 3
Pr
Clothes hanger Hanging lights T-1 Hanging lights T-2
Chair ´Vitra´ Flower vase
PVC Fired clay
Jo ur
Plastic chair
na l
Tables '1001 feuilles' Tabouret
e-
unit
ecoinvent
10
unit
20
965,9
892,9
126,0
Wood table
0
3
unit
20
831,3
178,3
24,2
Wood chair
0
3
unit
15
93,3
14,6
2,1
Meeting chair T-2
0
2
unit
15
457,3
428,3
28,4
Table lights T-1
0
4
unit
20
54,2
50,7
4,1
Table lights T-2
0
15
Armchair
0
2
Sofa table
0
2
pr unit
20
457,3
428,3
28,4
unit
20
3888,6
2953,0
184,2
unit
20
57,3
52,8
6,8
e-
Pr
f
4
oo
Standard table 75x150cm
Building scale
Quantity
Components / Materials
Pre-Densifi
Service life
Environmental impacts per unit
Source
year
CED (MJ)
CEDnr (MJ)
GWP (kg CO2)
KBOB
kg
60
0,5
0,5
0,1
1.001
442740
kg
60
0,8
0,8
0,1
1.003
1245360
kg
60
3,0
2,8
0,3
01.041
46369
kg
60
13,5
12,8
0,7
06.003
423166
kg
60
39,4
8,1
0,4
07.002
Solid wood, air dried
67666
kg
60
23,3
2,5
0,1
07.010
OSB chipboard
16337
kg
60
39,6
9,9
0,6
07.013
PE vapor barrier
151
kg
60
92,6
89,3
5,3
09.002
Bituminous waterproofing
3467
kg
60
45,9
45,0
3,3
09.003
Glass wool
7561
kg
60
35,8
27,9
1,1
10.001
Cellulose board
7235
kg
60
4,6
3,7
0,3
10,01
Polystyrene
5652
kg
60
106,6
104,8
14,5
10.005
Reinforced plasterboard
43205
kg
30
9,2
8,9
0,5
03.007
na l
132030
Poor concrete Concrete CEM I
Prefabricated concrete element Reinforcing steel Glue-Laminated timber
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Building envelope
Post-Densifi
Unit
IDEM
kg
30
Gravel
105504
kg
30
Mortar
41825
kg
720
kg
Solid wood
18085
kg
Glue-Laminated timber
8290
Solid wood, air dried
13560
OSB chipboard
1560
PE vapor barrier
240
Bituminous waterproofing
3465
Glass wool
2105
Cellulose board
29395
Linoleum
52150
0,3
03.008
0,2
0,2
0,0
03.012
30
1,7
1,6
0,1
04.005
30
29,0
27,9
1,8
06.010
30
45,2
10,8
0,5
07.001
30
39,4
8,1
0,4
07.002
kg
30
23,3
2,5
0,1
07.010
kg
30
39,6
9,9
0,6
07.013
kg
30
92,6
89,3
5,3
09.002
kg
30
45,9
45,0
3,3
09.003
kg
30
35,8
27,9
1,1
10.001
kg
30
4,6
3,7
0,3
10,01
kg
30
166,3
96,8
6,4
11.014
2
m
60
6538
5760
362
05.004
157
m2
30
4622
2063
128
05.005
365
2
m
30
993,2
939,6
66,8
05.012
56
m2
60
993,2
939,6
66,8
05.012
56
m2
30
3636
2376
155
12.001
Toilet doors
71
m2
30
3478
1389
86
12.003
Heat pump
1
unit
20
21694
19872
2180
31.017
Heat diffusion through the ceiling
5255
SRE
20
111,1
95,0
5,8
31.025
Ventilation system
5255
SRE
20
729,3
675,9
43,6
32.011
Sanitary equipment
5255
SRE
20
74,6
70,3
4,5
33.001
Electrical equipment
5255
SRE
20
683,9
409,8
23,9
34.002
3,0
2,5
0,1
45.020
Triple glazing Triple glazing
Electric ity
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Internal doors
na l
Wood frame
Pr
14
e-
kg
Aluminium frame
Equipment
4,9
pr
Fixing screw
5,1
f
198955
oo
Gypsum plasterboard
Heating
1569622
1889722
kWh
1
DHW
1199839
1204444
kWh
1
kWh
1
Lighting
3074768
2472068
kWh
1
Appliances Laptops
1204354
1080540
288
486
kWh unit
Monitors
360
252
unit
Docking station
288
0
Keyboards
288
450
Mouse
288
486
Working table
438
Working chair
f
623631
oo
654469
2585
2411
148,9
6
1796
1672
120,4
pr
1 5 5
123,0
105,8
7,1
unit
5
293,2
269,0
18,9
unit
5
79,0
72,3
5,0
372
unit
20
1236
611,3
67,8
288
252
unit
15
1347
1245
82,3
Cabinets
258
120
unit
15
812,1
680,5
63,5
Sofa
18
0
unit
20
5516
4099
240,0
Meeting chair
564
912
unit
15
304,4
286,4
18,1
Round table
36
18
unit
15
727
313
33,5
Metal tambour cabinets
378
0
unit
20
2131
1879
160,4
18
18
unit
10
87,7
85,1
4,1
396
547,2
unit
15
28,8
26,8
1,5
102
132
unit
15
218,1
209,2
16,4
0
36
unit
20
163,7
146,9
9,6
Hanging lights T-2
0
18
unit
20
326,1
293,2
20,6
Tables '1001 feuilles'
0
18
unit
20
2683
2095
214,0
Tabouret
0
36
unit
20
104,5
95,8
11,3
Chair ´Vitra´
0
252
unit
15
457,3
428,3
28,4
Flower vase (PVC)
0
1110,6
kg
20
63,1
62,1
2,5
Flower vase (Fired clay)
0
113,4
kg
20
4,0
3,8
0,8
Plastic chair
0
18
unit
15
339,8
174,4
22,5
Sofa chair
0
36
unit
15
364,6
67,3
5,3
White board Clothes hanger Hanging lights T-1
Pr
na l
Boiler
e-
unit
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Appliances
Ventilation
Hoxha et al. 2017
ecoinvent
18
unit
20
USM - Storage (Pos. F)
0
18
unit
20
USM - Storage (Pos. G)
0
18
unit
USM - Storage (Pos. H)
0
90
unit
USM - Storage (Pos. I)
0
18
unit
USM - Storage (Pos. J)
0
18
USM - Storage (Pos. K)
0
18
Standard table 75x150cm
0
252
Wood table
0
Wood chair
0
Meeting chair T-2
0
Table lights T-1
0
Table lights T-2
0
Armchair
0
Sofa table
0
3600
261,3
7981
7335
865,9
20
6655
6117
722,1
20
3221
2961
349,3
20
1773
1629
192,4
pr 20
4368
3991
471,0
unit
20
7434
6793
801,8
unit
20
966
892
126,0
54
unit
20
831
178
24,2
54
unit
15
93,3
14,6
2,1
36
unit
15
457
428
28,4
72
unit
20
54,2
50,7
4,1
270
unit
20
457
428
28,4
36
unit
20
3888
2953
184
36
unit
20
57,3
52,8
6,8
e-
unit
Pr
na l
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4051
f
0
oo
USM - Storage (Pos. E)
8. Acknowledgement The work presented in this paper has been funded by the ….. We thank all anonymous participants as well as ……. team for their valuable discussion. We also thank ….for its contribution in designing the great, new office layouts, and the ….. for its contribution to this experiment. The design of this new layout was proposed with the assistance of “Atelier Oï”, an
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architecture and design office that conducts several participative workshops with future users.
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1. Design of Experiment Phase A : pre-densification
Phase B : post-densification
Team A Team B Team C
2. Measurements
Presence measurement Energy consumption
3. Life cycle assessment
Environmental impacts (ex: kg CO2e) in pre-densification in post-densification
Furniture Appliances embodied Appliances usage Lighting Ventilation DHW Space heating Building embodied
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na
generalizazion at the buidling scale
Furniture Appliances embodied Appliances usage Lighting Ventilation DHW Space heating Building embodied
Environmental impacts (ex: kg CO2e) in pre-densification in post-densification
Building components
4. Life cycle assessment
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Building components
at the two offices scale
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Perceived comfort
Time in post-densification
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Rate of measuring parameters
Time in pre-densification
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circles represent time spent at the working place
Figure 1: General methodology schema. (Step 1: the circles of teams schematically present the time the
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employees spend in their working places. Step 2: x-axis presents the time in the pre- and post-densification phases, while the y-axis presents the rate of measuring parameters. Steps 3 and 4: environmental impacts of building components in the pre- and post-densification phases.)
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Figure 2: Pleasure-Arousal-Dominance (PAD) questionnaire with a 9-point scale and three axes: “Pleasure” (first row), “Arousal” (second row), and “Dominance” (third row) (Bradley et
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na
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al., 1994).
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Figure 3: An office before (left photo) being converted into the interactive room after densification (right).
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Figure 4: Plan layout of the smart living building (6 identical floors) 16
SIA-2024, (2015) Pre-Densification
12
Post-Densification
10
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8 6 4 2 0 0
1
2
3
4
5
6
7
8
9
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Number of employees
14
10
11
12
13
14
15
16
17
18
19
20
21
22
23
Offices and meeting rooms Offices
na
5 4 3 2 1 0
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Number of employees
6
Pre-densification
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0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Times in hours
7 6
Number of employees
7
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Time in hours
Post-densification Offices and meeting rooms Offices
5 4 3 2 1 0
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Times in hours
Figure 5: Rate of densification during two phases of the experiment, according to the SIA-2024 Swiss norm and the real-presence monitoring before and after densification.
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Figure 6: PAD responses during pre- and post-densification phases. Graphics present the mean
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values while error bars represent minimal and maximal values of PAD responses.
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densification.
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Figure 7: Participants’ pleasure, arousal, and dominance emotional states before and after
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Figure 8: Pleasure and arousal answers in the post-densification phase of the experiment.
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Figure 9: Environmental impacts at room-scale for pre- and post-densification phases.
Figure 10: Environmental impact at office scales according to different functional units for
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pre- and post-densification phases.
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densification.
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Figure 11: Environmental impacts on the smart living building scale before and after
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Figure 12: Environmental impact of the smart living lab on different units.