Evaluating Environmental Impacts of Urban Development Strategies: A Case Study of the Fontaine d’Ouche District

Abstract

Life cycle assessment (LCA) is a methodology used to analyze the environmental impacts of a product. Initially, it was applied to buildings only, but recently, it has also been applied to entire neighborhood. This expansion from individual buildings to the neighborhood scale requires additional inputs, such as the impacts of public spaces, transportation, public network systems, and roads. Additionally, conducting an LCA for a neighborhood requires software tools to simulate the neighborhood and databases to store the environmental impacts of all the components integrated into the neighborhood. This paper presents an LCA case study of a neighborhood in Dijon, France. The study aims to analyze the thermal impacts of buildings and the neighborhood with single-, double-, and triple-pane windows. The second part of the study involves two LCA studies using the 1996 version of ecoinvent. The first study is a continuation of the thermal simulation analysis, while the second study compares concrete, masonry, and timber-based construction designs for the neighborhoods. The results show that the heating demands inside the buildings are substantially reduced when transitioning from single- to triple-pane, while the cooling demands show the opposite effect. Furthermore, doubling the width of double-pane windows resulted in less profound environmental damage for the construction, use, and demolition phases compared to single-pane windows, with only the renovation phase being more damaging. Additionally, the comparison between different construction scenarios shows that the timber-based variant is slightly advantageous from an environmental point of view compared to the concrete and masonry designs.

1. Introduction

The world is currently facing significant environmental challenges, including pressure on natural resources, ozone depletion, loss of biodiversity, and climate change. There is a growing awareness of these issues and their impact on the environment, leading to changes in how we operate and plan. Environmental regulations have become a crucial part of every enterprise and industry worldwide. Approximately 65% of global energy is consumed in urban areas, and this is expected to increase due to population growth [1,2]. To move towards sustainability, the construction industry, under pressure from stakeholders and governments, is continuously developing plans for better, more reliable, and resilient cities.

Several environmental assessment tools and methods are used to evaluate the impact of processes in the construction and urban sector [3]. The life cycle assessment (LCA) approach shows significant potential for environmental assessments in the construction and urban planning sectors [4] and has been increasingly adopted by researchers, engineers, and decision-makers over the last decade in those respective sectors [5,6,7]. The construction sector LCA methodologies and regulations are on the rise and have been extensively adopted in research all over the world, ranging from small houses to larger apartments and entire buildings and recently extended to the neighborhood scale.

The life cycle assessment or analysis of buildings/neighborhoods is a systematic tool that analyzes the environmental impact of the construction materials, systems, and, activities that constitute the building and its surrounding area over the entire life cycle of the building, starting from the construction phase, passing through the use phase, and finally the end-of-life (EOL) phase [8].

The LCA method, being a comprehensive tool, offers many advantages when compared to other tools. First, incorporating LCA methods even before the construction phase begins allows decision-makers to analyze and compare different construction materials to try and find the most environmentally friendly materials [5,9]. In addition, LCA methods compare the physical impact on a product [10]

The LCA method is effective in measuring the environmental impacts of the construction sector at each of its stages [11]. This characteristic is known as “Burden Shifting” [12].

The LCA of buildings can be represented mathematically according to the following equation [13]:

$$I=I_{extraction }+I_{manufacture}+I_{onsite}+I_{operation}+I_{demolition}+I_{recycling}+I_{disposal}$$

(1)

where $I$ represents the life cycle environmental impact, and each $I_n$ represents the impacts of a certain “n” phase of the life cycle.

Sustainable urban planning, smart infrastructure, renewable energy, and the Internet of Things are just a few of the technologies that can be integrated into neighborhood development to improve resource efficiency, lower carbon emissions, and the overall quality of life. Case studies, such as Egypt and Sweden, which show how planning, participation, depoliticization, and new technologies can support resilient, livable, and energy-efficient communities, are highlighted in the literature on sustainable urban planning [14,15]. One of the main topics of discussion in recent academic debates is how to integrate technology while maintaining equality and inclusivity.

The objective of this research paper is to evaluate the environmental impacts generated by the renovation of an important neighborhood in Dijon. This will be achieved by first simulating the neighborhood and its surrounding environment. Subsequently, the same software, coupled with an environmental database, will be used to estimate the environmental footprint of this neighborhood.

This paper is novel for several reasons, making it critical to study a life cycle assessment of a sustainable neighborhood. These reasons include the transformation of existing infrastructure, innovation in energy management (solar panels, batteries, charging stations, hot water tanks, LED for street lighting, sensors measuring air quality, thermostats, and insulation of buildings), and the use of many scenarios and simulations. It is important to conduct the life cycle assessment of the neighborhood to determine the correct design for buildings, infrastructure, and public lighting, as well as to study the environmental impact of renovation also.

2. Difference Between LCA of Buildings and LCA of Neighborhoods

2.1. LCA of Buildings

The research of LCA at the individual building scale involves analyzing the components of a building without considering its interaction with the surrounding built environment [1].

Several reviews [5,8,13,16] have categorized LCA studies based on their analytical scale. Some studies have focused on the environmental impacts of different building materials [9,17], while others have examined the differences among renovation, demolition, and the potential of recycling [18,19,20,21]. Additionally, many articles and case studies have concentrated on a life cycle energy analysis (LCEA) and the assessment of carbon dioxide emissions [22,23,24,25]. LCEA studies are crucial as they assess the energy consumption impacts within a building, including operational and embodied energy. Carbon dioxide assessment studies focus on the greenhouse gas emissions of the building, aiding in evaluating its carbon footprint. Other information gathered from LCA studies includes building morphology differences, energy characteristics (passive or active heating and cooling systems, the integration of renewable energy, etc.), and social impacts, such as user behavior [26].

The ongoing efforts by researchers and designers to minimize the environmental impacts of buildings and the construction sector have been successful, particularly in terms of improving the energy efficiency of buildings, leading to High Energy Performance Buildings (HEPBs). This progress is evident in the evolution from constructing low-energy buildings to passive buildings, zero-energy buildings (ZEBs), and eventually positive energy buildings (PEBs). Additionally, there is continuous research on autonomous buildings, which are not connected to an energy distribution grid [27]. A previous study by [28] focused on the LCA of a smart building, the ESTP Campus building. This study will focus on the LCA of neighborhoods, as described in the following section.

2.2. LCA of Neighborhoods

Recently, the focus has been shifted in focus from the building to the entire urban area to understand the potential impacts of a population’s activities. Sustainable practices can be implemented at the neighborhood level because neighborhoods offer a good balance in size. They are large enough to address broader issues that go beyond individual buildings yet small enough to allow practical actions to be taken and monitored effectively [29].

The methodology of neighborhood LCAs involves analyzing not only the buildings within a neighborhood but also their interaction with each other and with surrounding components, such as public networks, public spaces, and the mobility sector [6,28].

Neighborhoods, being complex and dynamic systems, can present challenges in the environmental evaluation of their components. This is primarily due to variations in functional units and system boundaries (spatial aspects), as well as temporal variations within each neighborhood [30]. The system boundary significantly affects the results of LCAs on an urban scale, with implications for study outcomes [26].

Two studies have specifically focused on the effect of road infrastructure on the environment [31,32]. On the other hand, other researchers also consider the effect of mobility [29,33]. For a comprehensive analysis of the urban scale, all the different components mentioned above must be included, which is the case in several studies [3,34]. The aforementioned variables represent the spatial aspect of the study.

Besides the system boundaries of the neighborhood, additional temporal and behavioral parameters can also affect the results of the study, increasing uncertainties, and thus must be considered. The most important factor that needs to be studied is the behavior of the residents of the neighborhood. Not all residents consume the same amount of energy and heat, nor do they all recycle with the same proportions or commute the same distances within the limits of a neighborhood [35,36]. A second factor that should be studied is the difference in energy mixes and future emission intensity of electricity, which are expected to change and become more sustainable going forward [26]. Finally, the ever-increasing climate change and the variability in the climatic conditions will affect the heating and cooling demand of the population. A reduction in the heating demand is certain due to the forecasted increase in global temperature and consequently an increase in the cooling demand [26]. Usually, to analyze the effects of such parameters, sensitivity and contribution analyses are required and conducted because these factors are unpredictable and dynamic within a neighborhood.

The life cycle assessment of neighborhoods has the advantage of being able to analyze the environmental impacts of various urban projects, such as the construction of new buildings, the demolition of existing lots, and the development of new urban areas [6,20].

However, comparing neighborhood life cycle assessment studies can be challenging due to the numerous factors that need to be considered. The different plans and structures of urban areas make it difficult to establish uniform working hypotheses. A calculation hypothesis that works in one region may not be applicable in another region [11]. Figure 1 illustrates the differences between the components of a building LCA and a neighborhood LCA.

3. Materials and Methods

3.1. Site Description

The site discussed in this paper is the neighborhood known as “Fontaine d’Ouche” in Dijon, France. This neighborhood was chosen because Dijon was selected, along with the city of Turku in Finland, to pilot the European program “RESPONSE” (integRatEd Solutions for POsitivity eNergy and reSilient citiEs) as a part of the European project “H2020, intelligent cities and communes”.

Dijon is a metropolis with 250,000 inhabitants, consisting of 24 communes, with the largest, Dijon, having over 155,000 inhabitants. The Dijon Metropolis is strongly committed to achieving an energy transition with renewable energy production increasing by 83% since 2010 to reach 380 GWh/year in 2017.

Fontaine d’Ouche is one of the most important neighborhoods in Dijon, with around 7000 inhabitants spread over of 23 km². Under the RESPONSE project, two building lots will undergo an ambitious and innovative energy and thermal renovation, making the district a leading example in the fight against climate change.

3.2. Building Simulation

The case study will focus on the renovation of three buildings, known as buildings 3, 4, and 5, in the 2nd block of the project. The plan involves renovating these buildings and creating two energy-positive blocks, which will include residential buildings, public buildings, a gymnasium, and other facilities.

Unfortunately, due to the project’s complexity and the lack of cooperation from the stakeholders, we did not receive any data regarding the project, such as details about construction materials, equipment used, types of windows, etc.

Building 3:

$$Dimensions=Length\times Width\times Height=40 \mathrm{m}\times 10 \mathrm{m}\times 12.5 \mathrm{m}$$

(2)

The building consists of five floors including the ground floor.

Building 4:

$$Dimensions=Length\times Width\times Height=15 \mathrm{m}\times 10 \mathrm{m}\times 30 \mathrm{m}$$

(3)

The building consists of 12 floors including the ground floor.

Building 5:

$$Dimensions=Length\times Width\times Height=40 \mathrm{m}\times 10 \mathrm{m}\times 20 \mathrm{m}$$

(4)

The building consists of eight floors including the ground floor.

We used a graphic simulation to analyze the thermal and environmental impacts of different building designs. These impacts could be influenced by various factors, such as changes in the construction materials, thermal characteristics of windows, and type of equipment, as well as the dynamic behavior of the occupants.

The current case study involves a two-part examination. The first part focuses on analyzing the thermal needs of the buildings, while the second part evaluates the environmental impacts of the neighborhood using the Pleiades Software (5.22.11.3) and a life cycle assessment. Each part of the study used different objectives, inputs, outputs, and data concerning the construction materials used, requiring differentiation in each part.

4. Thermal Dynamic Simulation

The first part of the study involves analyzing the thermal needs of buildings by creating different scenarios for each building. The goal is to observe how the results vary when comparing these scenarios with alternative, optimized ones, to choose the most effective conditions for optimizing a building’s thermal needs without relying on thermal equipment. The Thermal Dynamic Simulation (TDS) was conducted to compare two sets of scenarios and three variants of window panes: Simple Pane (SV), Double Pane (DV), and Triple Pane (TV). This comparison was carried out for three buildings in the studied neighborhoods. Additionally, the study compared standard-living thermal scenarios with optimized variants and considered a combination of both. Since we lacked exact data about the construction materials and elements of the three buildings analyzed, we had to make assumptions about the construction data for the different walls.

4.1. Variant One: Different Window Pane

The three types of windows adopted have the following thermal characteristics (

Table 1. Characteristics of the three different types of window panes used.

	Uw Vertical (W/m2·K))	Uw Horizontal (W/m2·K))	Solar Factor—Sw
Single pane windows—SV	2.6	2.6	0.7
Double pane windows—DV	1.6	1.6	0.7
Triple pane windows—TV	0.8	0.8	0.7

):

The U value, also known as thermal transmittance, describes the heat transfer through a square meter of a window. To calculate the U value of a window, you need to know the thermal transmittance value for the window frame, expressed as Uf, and the thermal transmittance value for the glass, expressed as Ug. A lower Uw value signifies a well-insulated building, whereas a higher value indicates that the building has poor thermal performance. On the other hand, the solar factor (Sw) measures the capacity of a window to transmit solar heat across its glass and into a room (Table 1). Therefore, the higher the Sw coefficient is, the more capable the window is of facilitating the transmission of solar heat from the exterior into the rooms.

From the previous information, we can conclude that to maintain and increase heat inside a room, we must adopt windows that have low Uw values and high Sw values. To facilitate the comparison between the different types of windows, we have set a fixed value of 0.7 for the Sw, and thus, the only variable between the different types of windows is the Uw.

4.2. Variant Two: Thermal Scenarios

To conduct a TDS properly, it is essential to assign scenarios to the thermal zones of the buildings. This can be achieved using the module STD Comfie [37], which is integrated into Pleiades. Each building, house, and apartment contains different thermal zones that vary according to the different uses of the occupants. For instance, the bedroom differs from the living room in terms of hours of occupation, hours of ventilation rates, etc. To simplify the study, we considered assigning a different thermal zone to each floor of the building, but it is important to note that different thermal zones can have the same scenarios.

To dissipate power, lighting, ventilation, and occupancy scenarios in buildings are established based on realistic assumptions, thanks to data feedback from the neighborhood (

Table 2. Proposed scenarios regarding the dissipated power conditions for all three buildings.

Dissipated Power
Hours		00:00–7:00	7:00–9:00	9:00–17:00	17:00–00:00
Standard Scenario	WD	4 W/m2	7 W/m2	1 W/m2	7 W/m2
	WE	4 W/m2	7 W/m2
Optimized Scenario	WD	2 W/m2	4 W/m2	0.5 W/m2	4 W/m2
	WE	2 W/m2	4 W/m2

,

Table 3. Proposed scenarios regarding the lighting conditions for all three buildings.

Lighting Scenarios
Hours	00:00–6:00	6:00–7:00	7:00–11:00	11:00–17:00	17:00–22:00	22:00–00:00
Standard Scenario	0 Lux	0.79 W/m2	4 W/m2	7.9 W/m2	4 W/m2	0.79 W/m2
Optimized Scenario	0 Lux	0.4 W/m2	2 W/m2	4 W/m2	2 W/m2	0.4 W/m2

,

Table 4. Proposed scenarios regarding the ventilation conditions for all three buildings.

Ventilation Scenario
Hours		00:00–7:00	7:00–8:00	8:00–12:00	12:00–13:00	13:00–17:00	17:00–19:00	19:00–20:00	20:00–22:00	22:00–23:00	23:00–00:00
Standard Scenario		0.3 vol/h
Optimized Scenario	Winter	0.29 vol/h	0.35 vol/h	0.12 vol/h	0.35 vol/h	0.12 vol/h	0.35 vol/h	0.66 vol/h	0.35 vol/h	0.35 vol/h	0.29 vol/h
	Summer	3 vol/h	0.35 vol/h	0.12 vol/h	0.35 vol/h	0.12 vol/h	0.35 vol/h	0.66 vol/h	0.35 vol/h	3 vol/h	3 vol/h

and

Table 5. Proposed scenarios regarding the number of occupants for all three buildings.

Hours	Building 3			Building 4		Building 4
	Ground Floor	1st–3rd Floor	4th Floor	Ground Floor	1st–11th Floor	Ground–2nd Floor	3rd–5th Floor	6th–7th Floor
00:00–8:00	30 Occupants	100 Occupants	60 Occupants	30 Occupants	70 Occupants	48 Occupants	30 Occupants	70 Occupants
8:00–9:00	15 Occupants	40 Occupants	30 Occupants	15 Occupants	35 Occupants	24 Occupants	15 Occupants	35 Occupants
9:00–12:00	0 Occupants	0 Occupants	0 Occupants	0 Occupants	0 Occupants	0 Occupants	0 Occupants	0 Occupants
12:00–13:00	15 Occupants	40 Occupants	30 Occupants	15 Occupants	35 Occupants	24 Occupants	15 Occupants	35 Occupants
13:00–17:00	0 Occupants	0 Occupants	0 Occupants	0 Occupants	0 Occupants	0 Occupants	0 Occupants	0 Occupants
17:00–00:00	30 Occupants	100 Occupants	60 Occupants	30 Occupants	70 Occupants	48 Occupants	30 Occupants	70 Occupants

respectively). These data provide accurate insights into how buildings are used, enabling a more precise understanding of energy consumption patterns. We can provide detailed information about these scenarios.

Pleiades utilizes the Dynamic Thermal Simulation model to analyze energy exchanges in buildings. This model calculates heating, cooling, and ventilation systems. It integrates national energy standards, such as Thermal Regulation 2012( RT 2012), which govern the thermal performance and energy use in buildings.

The thermal scenarios assigned to the buildings in the studied neighborhood are as follows:

(1)

Heating conditions for all three buildings: a temperature of 20 °C is considered in the standard scenario, while the optimized scenario involves a temperature fluctuating between 16 °C and 20 °C.

(2)

Cooling conditions for all three buildings: a temperature of 26 °C is considered in the standard scenario, and the optimized scenario involves a temperature fluctuating between 26 °C and 29 °C.

5. Life Cycle Assessment

After conducting the TDS of the simulated buildings, the next step involves estimating the environmental impact of each building. This is followed by performing a life cycle assessment (LCA) of the surrounding neighborhood by aggregating the LCAs of the individual buildings and adding all thermal requirements and corresponding data related to the networks and open spaces. The LCA analysis of the neighborhood consists of two sections. The first section involves the LCA analysis of each building integrated into the study, while the second section concerns the aggregation of these results into a unified study. Each section contains various data that need to be added, including data at the building scale for the LCA of the buildings and data at the neighborhood scale for the LCA of the neighborhood after the aggregation of the building results. The two types of data will be explained in the following parts.

Two case studies regarding LCA in the neighborhood were conducted. The first case study was a continuation of the TDS part, where the objective was to compare the environmental impacts of installing SV and DV windows. Two scenarios were compared to optimize the layout of the glassed surfaces. In the first scenario, the buildings were equipped with SV windows on each facade with a height and width of 0,5 m. In the second scenario, the windows were changed from SV to DV windows with a width doubled from 0.5 m to 1 m while keeping their height at 0.5 m.

Furthermore, to search for an optimized construction design, a comparison between three construction materials was made. In the first scenario, all three buildings were constructed with concrete-based materials and elements. In the second scenario, the construction materials favored a masonry-based material, and in the third scenario, the focus was on a more sustainable, timber-based data set. The data used for the concrete variant were based on the default setting for the concrete construction in the Pleiades software (5.22.11.3), called “ITE BBC-Mur Béton”, which roughly translates to a concrete design with exterior thermal insulation. For the masonry variant, the same data as those used in the TDS analysis and the first part of the LCA study concerning the layout of the glassed surfaces were used. The third and final set of construction data concerning the timber-based materials was also based on the default setting in the Pleiades software called “Ossature Bois”.

5.1. Building-Scale Data

The analyzed variants have different construction data for each building in the neighborhood, as well as varying data concerning the dimensions and types of windows installed. However, the energy consumption, water usage, and waste generation data remained the same for all the variants throughout the entire life cycle assessment analyses. It is important to mention that all the inputs entered into the software were either hypothetical, based on similar studies using the same software, or were simply the default data provided by the software.

The transportation distances of the materials selected for installation in the buildings in the LCA are as follows: 80 km between the production site and the construction site, and 20 km between the construction site and the discharge site. A material surplus, which corresponds to the average fall rate of the different construction products, of 5% accounting for the average loss rate of different construction products, has been taken into consideration. Additionally, the expected lifetimes are 30 years for frames and windows, 15 years for coatings, and 20 years for overall equipment.

The energy data were assessed using the default French mix in the software, with a network loss of 9% factored in. In terms of water usage, the drinking water network’s efficiency stands at 80%. Cold-water consumption is set at 100 L per person per day, while hot-water consumption is at 40 L per person per day. Waste management involves selective sorting is used, with 90% of glass and 75% of paper and cardboard being sorted for recycling rather than being sent to landfills. An average of 1500 g of daily household waste per person is considered, with 40% of this waste being sent for incineration, yielding an 85% conversion into natural gas. The distances from the site to the garbage dump are 20 km, 100 km to the incinerator, and 20 km to the recycling site.

Another important parameter under examination is the mobility of the building occupants. For simplicity, it is assumed that all the occupants commute similarly, with 80% commuting daily, either by car or by bus. The distances from home to work, home to the bus station, and home to trade centers are 2500 m, 250 m, and 500 m, respectively.

5.2. Neighborhood-Scale Data

Once the LCAs of the different buildings are completed, it is important to combine their results and incorporate all the parameters and impacts from the surrounding neighborhood, including green spaces, roads, parking, etc. All the data mentioned here are hypothetical. For instance, we assumed a rainfall rate of 1000 L/m². The drinking water system consisted of pipelines made of 50% polyethylene and 50% ductile iron, each 1200 m long. We also assumed the same length for the wastewater and underground water systems connecting the neighborhood. Moreover, the wastewater network was assumed to have a 75-year lifetime with an estimated network loss of 3% and maintenance scheduled every 40 years. The open spaces considered at the neighborhood scale include roads, streets, driveways, parking, and green spaces. Additional details regarding the neighborhood are provided in

Table 6. Proposed data for the analyzed neighborhood parameters of the LCA.

	Roads	Streets	Driveways & Parking	Gardens and Green Spaces
Total surface (m2)	200	50	100	200
Integrated green spaces (m2)	10	5	5	180
Integrated body of water (m2)	0	0	0	15
Impermeability (%)	95	90	85	40
Lighting type	Sodium	Sodium	Sodium	Sodium
Lighting load (kWh/m2/year)	4.8	4.8	4.8	0
Water used (L/m2/year)	10	9	9	60
Waste (kg/m2/year)	25	25	25	3
Lifetime	100	100	100	100

.

6. Results of the Thermal Dynamic Simulation

In this study, we assigned different thermal scenarios to the buildings and analyzed the impact of changing the number of window panes. We also examined the intersection of different sets of conditions. To facilitate the Thermal Dynamic Simulation (TDS) tests, it was essential to select an appropriate meteorological data site. For this study, we chose Macon, which is located 128 km away from Dijon. The results of the thermal needs for Building 4 are represented in the Figure 2:

We can conclude from the results for all buildings that, in terms of heating needs, an increase in the number of window panes reduces the heating needs of the buildings. Conversely, when it comes to cooling needs, the opposite scenario occurs: an increase in the number of window panes increases the need to cool down the temperature inside the buildings. These results are logical since, as mentioned earlier, an increase in the number of window panes is characterized by a decrease in the Uw value, which is a measure of the capacity of a window to lose heat. The choice of window type becomes interesting when designing a new building since adopting highly thermal efficient windows like the TV can, on one hand, reduce the heating needs and consequently the heating power, resulting in a lower heating bill during the cold seasons. However, installing TV windows can lead to very high temperatures during the hot seasons, causing distress for the residents, and they are usually very expensive to buy. The SV windows have the opposite effect: they are inefficient during the winter seasons but are cheap and provide comfort during hot seasons. Therefore, the logical choice is to depend on the DV windows that provide the best of both options. The DV windows containing argon gas between the two glasses are the most adopted in modern eco-friendly houses and buildings.

Optimizing window selection improves thermal performance in buildings, but occupant behavior remains the dominant factor for daily energy consumption. The second part of the STD analysis examines how altering living scenarios can reduce thermal needs, enhancing energy efficiency. The results are represented in Figure 3 and Figure 4.

The results demonstrate that adopting optimized scenarios instead of the standard scenarios leads to substantial heating and cooling needs, regardless of the type of window used. These results indicate the significant impact of the occupant’s behavior in reducing the thermal needs of their homes, as well as the importance of the quality of the installed equipment. Implementing these energy-saving measures can eliminate the necessity of using triple-pane windows in cold areas, saving the high costs associated with their installation. Additionally, even with the use of double-pane windows, optimizing thermal living conditions improves energy efficiency by reducing the demand for heating and cooling, resulting in savings on energy bills for occupants.

7. Results of the Life Cycle Assessments

In the previous analysis, we used buildings and neighborhood simulations along with corresponding data to perform a life cycle analysis over 80 years for both the buildings and the neighborhoods using the LCA Equer module of the Pleiades software (5.22.11.3).

Results of Variant One: Comparison between a single pane and double pane window: The results for the LCAs for both the SV neighborhood and the DV neighborhood’s overall life cycle steps show that, in general, the DV neighborhood has a slightly greater impact on the environment than the SV neighborhood. However, this impact varies across different environmental indicators. It is important to note that a high value for one indicator may have the same impact on the environment as a low value for another indicator. To address this issue, we need to normalize the results of the study. The normalized LCA results for each life cycle phase indicate that if the second variant has a value of 1.4 for a certain indicator, which means that the second variant is 40% more impactful for that indicator than the reference variant.

The creation of the variant involved removing a significant amount of the concrete surface to make room for larger windows. Because concrete fabrication is more environmentally damaging than window fabrication, we see a decrease in impacts during the construction phase (Figure 5). Additionally, during the use phase, there is a slight reduction in environmental impacts when transitioning from the SV to the DV neighborhood (Figure 6). This reduction is due to the double-pane windows’ thermal performance and increased solar intake, which reduces energy needs.

The renovation phase involves changing the windows every 30 years. DV windows have a greater environmental impact than SV windows due to their larger size and the presence of a rare gas, argon, between the two panes. These factors explain why the DV variant is more impactful than the SV variant (Figure 7). Unlike the renovation phase, the deconstruction phase’s impacts of the DV variant are less damaging because the installation of DV windows replaces some of the concrete surface, reducing the amount of concrete that needs to be removed (Figure 8).

Since the use phase is the longest in any LCA and carries the heaviest load among all the other phases, its effect will be the most observable throughout the study. Therefore, for the entire LCA of both of our neighborhoods, the radar chart is significantly similar, almost identical to that of the use phase. For more accuracy, the normalized results for the total between the two variants are represented in Figure 9.

The total results show that the neighborhood with buildings featuring larger, double-pane windows is environmentally better than the single-pane neighborhood, even if for some indicators, the difference is minimal.

The results for variant two show the effect of different construction designs.

The timber variant is mainly beneficial for reducing greenhouse gas emissions, inert waste production, and odor. It is also slightly favorable for human toxicity and water usage. The masonry variant also has similar effects but to a lesser extent. Both the timber and masonry variants have unfavorable impacts on cumulative energy demand and acidification, but these do not outweigh their benefits in other areas (Figure 10).

The analysis slightly favors the timber variant over the two construction materials. However, the difference is so small that it is not significant enough to prefer timber construction and rule out the other two designs (Figure 11).

The renovation phase was not included in the analysis because the interior coatings, windows, and doors were all renovated after predetermined periods (15 years for coatings and 30 years for frames and windows) and were the same for all three variants.

The deconstruction phase favors the use of timber, which is logical because dismantling concrete and masonry buildings requires a significant amount of energy and emits a large amount of greenhouse gases (Figure 12). Additionally, most timber materials are more eco-friendly than concrete and masonry materials and therefore do not cause as much toxicity to humans and biodiversity as concrete and masonry materials do.

The timber-derived materials demonstrated significant advantages in both the construction and demolition phases, making the timber variant the most environmentally friendly option when compared to concrete and masonry materials (Figure 13). However, these benefits are specifically notable in terms of global warming, inert waste produced, and photochemical ozone production indicators.

8. Conclusions

Life cycle assessment is a method used to evaluate the environmental impacts of products and systems over their entire lifespan. It is an important tool for designers and engineers in the construction sector, helping them analyze the environmental footprint of their products and compare them with more sustainable alternatives. This research aims to broaden the scope of LCA by examining its role at the neighborhood scale. It seeks to highlight the differences between building-scale and neighborhood-scale LCAs and assess the availability of LCA software capable of extending their analysis from buildings to neighborhoods. Transitioning from buildings to neighborhoods requires a shift from a static analysis of individual buildings to a dynamic analysis of the interconnected systems that link multiple buildings and their surroundings. This includes assessing the environmental impacts of energy, waste, water, and transportation networks. The second part of this research involves conducting an LCA in a French neighborhood undergoing renovation as part of the European “RESPONSE” project. The study includes dynamic thermal simulation tests, which revealed that transitioning from single-pane to double-pane and then to triple-pane windows reduces heating needs in the building but increases cooling needs. However, this transition also comes with increased installation costs. To address this, it is suggested that optimizing the thermal conditions inside the buildings, such as ventilation, heating, cooling, and lighting, can decrease energy consumption and mitigate power losses.

Despite the promising results of this study, challenges persist in conducting life cycle assessments in the construction sector, particularly concerning the reliability of databases for accurately assessing the environmental impacts of neighborhood components. It is also important to explore opportunities to integrate building information modeling (BIM)models into the work to help to collect data. This research addresses an important topic, but further, in-depth studies are needed to fully understand the parameters and support engineers and decision-makers in designing sustainable cities for the future