# Integrating life cycle sustainability assessment into building renovation strategies

Goessel Thibault, Simon Ligier, Robin Girard

► To cite this version:

Goessel Thibault, Simon Ligier, Robin Girard. Integrating life cycle sustainability assessment into building renovation strategies. Building and Environment, 2025, 286, pp.113694. 10.1016/j.buildenv.2025.113694. hal-05286845

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# Integrating life cycle sustainability assessment into building renovation strategies

Goessel Thibault<sup>a,b,*</sup>, Ligier Simon<sup>b,†</sup>, Girard Robin<sup>a</sup>

## ARTICLE INFO

**Keywords:**

Life Cycle Sustainability Assessment

Building renovation

Prospective scenario

Pareto front

## ABSTRACT

This study presents a multi-objective optimization methodology for building renovation, integrating a holistic and prospective approach. The methodology incorporates thermal discomfort into the Life Cycle Sustainability Assessment, addressing a critical gap in existing approaches that undermine the effects of climate change.

The methodology involves using Building Dynamic Energy Simulation to quantify energy consumption and thermal discomfort, Life Cycle Assessment for environmental impacts, and Life Cycle Cost analysis for economic evaluation. The study integrates Pareto-optimal strategies and multi-criteria clustering to identify archetypes of optimal renovation strategies which represent a trade-off between environmental (carbon footprint), economic (direct global cost) and comfort (thermal discomfort) objectives.

A case study on a French residential building explores over 11,000 renovation strategies under varying climate zones and initial energy systems. In all configurations, carbon footprint and direct global cost values for optimal renovation strategies are not widely dispersed around median values, with relative dispersions inferior to 0.32. However, thermal discomfort following optimal renovations is strongly dependent on climate zone, with a relative dispersion of 1.15 in the context of a warm French climate (H3). 4 to 5 clusters of optimal renovation strategies have been identified for every configuration, highlighting the possible trade-offs between the 3 objectives. Gas-based systems are consistently replaced by thermodynamic solutions, whereas the type and performance of envelope insulation differ across clusters. In all configurations, the majority of optimal renovation strategies include solar protections (56–62%), while cooling systems are implemented less frequently, appearing in only 20–38% of cases.

## 1. Introduction

The effects of climate change (CC) on the built environment are becoming increasingly significant. On the one hand, as it represented 40 % of the greenhouse gas (GHG) emissions globally in 2017 [1], the sector has a major role to play in the climate change mitigation. A massive renovation of the building stock is therefore necessary to achieve a strong decarbonization [2], in order to respect carbon budgets aligned with climate objectives [3]. In France, a target of 600,000 to 1 million annual efficient renovations is set for 2030 [4]. On the other hand, renovated buildings must be designed for future climate conditions. In France, the built environment will face rising temperatures as well as more frequent and more severe heatwaves. This is why the French government has established a plan to adapt in a country with a +4°C temperature increase [5].

The scientific community has therefore developed methodologies to evaluate the relevance of building renovation strategies, considering their impacts over the life cycle of the renovated building. Such holistic approaches are regrouped into the Life Cycle Sustainability Assessment (LCSA). Recent reviews detail the development of this approach for the assessment of building renovation strategies [6,7]. First, it includes the Life Cycle Assessment (LCA) for the environmental impacts. The review made by Vilches et al. explains the principle of this methodology for building [8], while Palacios-Munoz et al. [9] and Nydahl et al. [10] are two recent examples of LCA for building renovations. Secondly, LCSA includes the Life Cycle Cost (LCC) for the economic consequences. Jafari et al. have estimated the total life-cycle cost of various renovations [11], while Elbeze detailed the different useful indicators for a LCC. Third, the Social Life Cycle Assessment (SLCA) focuses on the consequences for building occupants. Different impact categories may be considered for

<footnote><sup>a</sup> Mines Paris - PSL, Centre for Processes, Renewable Energy and Energy Systems (PERSEE), 1 rue Claude Daunesse, Sophia-Antipolis, 06904, France</footnote>

<footnote><sup>b</sup> Centre Scientifique et Technique du Bâtiment (CSTB), 84 avenue Jean Jaurès, Champs-sur-Marne, 77420, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: thibault.goessel@minesparis.psl.eu (G. Thibault).</footnote>
the SLCA, such as health and comfort, accessibility, maintenance or security [12]. More precisely, a relevant indicator for SLCA in the building sector is the thermal discomfort, as it characterizes the sensation perceived by occupants [6]. Table 1 describes recent studies that focused on the optimization of building renovation strategies, using at least one of the three pillars of LCSA. Most recent studies analyzed environmental and economic impacts, mainly life cycle carbon emissions and life cycle cost. For example, Galimshina et al. studied the renovation strategies which minimized both the carbon footprint and the global cost for buildings in Switzerland [13]. A trade-off was necessary in this study to obtain optimal renovation strategies. However, the third pillar of the LCSA, which is the Social Life Cycle Assessment, is barely studied when it comes to building renovation. Analysis of Table 1 shows that only few studies have used a complete LCSA approach to identify optimal renovation strategies [14–18]. This is a strong research gap because recent studies highlight that optimal renovations considering environmental and economic impacts are not necessarily the best regarding thermal discomfort. Mostavi et al. showed on a school in the USA that cost-carbon optimal renovations have a poor thermal comfort index [14]. Even if Bayat Pour et al. investigate moisture-related issues for the social impacts of renovation strategies in their Inutility Decision Analysis, they mention the necessity to include health indicators and thermal discomfort into the methodology [18]. The inclusion of health indicators into the methodology is also presented as a necessity for Ongpeng et al. at the end of their study [19]. Highly-efficient windows and cooling systems are often detrimental for life cycle carbon emissions and life cycle costs but enable to reduce

thermal discomfort, as highlighted by Mostafazadeh et al. in their study on a residential building in Iran [16].

Moreover, following Table 1, only one study combined a complete LCSA approach with an integration of climate change for the energy simulation [16]. Mostafazadeh et al. have indeed studied the effect of global warming on optimal renovation strategies for a specific residential building in Iran (climate zone 3B "Warm Dry" according to ASHRAE 469-2013 climate classification [20]). This is another strong limitation in the literature because future climate conditions will drastically change the heating and cooling needs of buildings, thus modifying the optimal renovation strategies. Hernandez-Cruz et al. highlighted a projected decrease in heating degree days and an increase in cooling degree days across northern Spain (climate zone 4C "Mixed Marine" according to ASHRAE [20]) in the coming decades, regardless of the climate change scenario considered [21]. Rising temperatures, especially during heatwaves, also have a tremendous impact on thermal discomfort and can cause widespread mortality [22].

Besides, renovation strategies often focus on building envelope and/or photovoltaic installation, while HVAC (heating, ventilation, and air conditioning) systems are scarcely studied. Only Mostafazadeh et al. [16] included these two categories in their complete LCSA approach, but only considered two heating systems (gas boiler and heat pump). Results are highly sensitive to the country of the case study, as climate series, environmental and economic data differ. This reflects in the design of experiments and the renovation strategies that are considered. For example, the four studies which have a complete LCSA approach do not consider common heating systems in France, like electric radiators or

<table><caption>**Table 1**
Recent studies about optimization of building renovation strategies.</caption><thead><tr><th>Year</th><th>Authors</th><th>Case study (Climate zone [20])</th><th>Renovation strategies</th><th>LCA</th><th>LCC</th><th>SLCA</th><th>CC</th></tr></thead><tbody><tr><td>2017</td><td>Mostavi et al. [14]</td><td>School, USA (5A)</td><td>Building envelope</td><td>✓</td><td>✓</td><td>✓</td><td>X</td></tr><tr><td>2020</td><td>Garcia Kerdan and Morillon Galvez [24]</td><td>Residential housing, Mexico (2A, 2B, 3A)</td><td>Building envelope<br>HVAC operation<br>Solar and wind utilization</td><td>X</td><td>✓</td><td>✓</td><td>X</td></tr><tr><td>2020</td><td>Oregi et al. [25]</td><td>Residential housing, Spain (3A)</td><td>Building envelope<br>HVAC systems</td><td>✓</td><td>✓</td><td>X</td><td>X</td></tr><tr><td>2020</td><td>Rosso et al. [26]</td><td>Residential building, Italy (3A)</td><td>Building envelope<br>Photovoltaic installation</td><td>✓</td><td>✓</td><td>X</td><td>X</td></tr><tr><td>2021</td><td>Panagiotidou et al. [27]</td><td>Residential housing, Greece (3B)</td><td>Building envelope<br>HVAC systems</td><td>✓</td><td>✓</td><td>X</td><td>X</td></tr><tr><td>2021</td><td>Xu et al. [28]</td><td>School, China (3A)</td><td>Building envelope<br>Window-to-wall ratio<br>Sunshades<br>Photovoltaic installation</td><td>X</td><td>✓</td><td>✓</td><td>X</td></tr><tr><td>2022</td><td>Amini Toosi et al. [15]</td><td>Residential building, Italy (4A)</td><td>Photovoltaic installation</td><td>✓</td><td>✓</td><td>✓</td><td>X</td></tr><tr><td>2022</td><td>Ongpeng et al. [19]</td><td>School, Philippines (0A)</td><td>Building envelope<br>Photovoltaic installation</td><td>✓</td><td>✓</td><td>X</td><td>X</td></tr><tr><td>2022</td><td>Razmi et al. [29]</td><td>Residential building, Iran (3B)</td><td>Orientation<br>Window-to-wall ratio<br>Sunshade</td><td>X</td><td>X</td><td>✓</td><td>X</td></tr><tr><td>2023</td><td>Chen et al. [30]</td><td>Office building, China (4A)</td><td>Building envelope<br>Systems efficiency<br>Internal gain</td><td>✓</td><td>✓</td><td>✓</td><td>X</td></tr><tr><td>2023</td><td>Mostafazadeh et al. [16]</td><td>Residential building, Iran (3B)</td><td>Building envelope<br>Water fixtures<br>HVAC systems<br>Photovoltaic installation</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>2023</td><td>Wu et al. [31]</td><td>Office building, China (2A, 3A, 4A, 6A)</td><td>Building envelope<br>Window-to-wall ratio<br>Systems efficiency</td><td>✓</td><td>X</td><td>X</td><td>X</td></tr><tr><td>2024</td><td>Alimohamadi et al. [32]</td><td>Residential building, Iran (3B)</td><td>Building envelope<br>HVAC systems</td><td>X</td><td>✓</td><td>✓</td><td>X</td></tr><tr><td>2024</td><td>Bayat Pour et al. [18]</td><td>Building, Sweden (5A)</td><td>Building envelope</td><td>✓</td><td>✓</td><td>✓</td><td>X</td></tr><tr><td>2024</td><td>Cui et al. [33]</td><td>Office building, China (3A)</td><td>Building envelope<br>HVAC operation</td><td>X</td><td>✓</td><td>✓</td><td>X</td></tr><tr><td>2024</td><td>Galimshina et al. [13]</td><td>Residential building, Switzerland (4A)</td><td>Building envelope<br>HVAC systems</td><td>✓</td><td>✓</td><td>X</td><td>✓</td></tr><tr><td>2024</td><td>Hernandez-Cruz et al. [21]</td><td>Residential building, Spain (4C)</td><td>Building envelope</td><td>✓</td><td>X</td><td>X</td><td>✓</td></tr><tr><td>2024</td><td>Shi et al. [34]</td><td>Residential building, China (5B)</td><td>Building envelope</td><td>X</td><td>X</td><td>✓</td><td>X</td></tr><tr><td>2024</td><td>Zhan et al. [17]</td><td>Apartement building, China (4A)</td><td>Building envelope<br>Photovoltaic installation</td><td>✓</td><td>✓</td><td>✓</td><td>X</td></tr></tbody></table>
wood boiler [23].

Concerning the holistic approaches included in LCSA, major research
gaps are to be tackled:

* For the LCA, a recent review by Fnais et al. reminded that the impact of operational energy use is necessary to have a global overview of the environmental impacts [35]. Review by Amini Toosi et al. highlighted the need for future energy mixes in environmental data, in order to evaluate impacts of different prospective scenarios [6]. So-called prospective LCA is barely included in LCSA for building renovation strategies and shall be included to have more comprehensive LCA. Moreover, LCA dedicated to building renovation is different from LCA for new buildings, because it needs to consider the initial state of the building and thus the preserved and substituted components due to the renovation phase. Embodied greenhouse gases emissions become predominant in ambitious scenarios of worldwide buildings decarbonization [3]. To achieve that, a building decomposition model is necessary to trace component flows for each renovation strategy, as suggested by the IEA EBC Annex 72, dedicated to life cycle related environmental impacts caused by buildings [36]. It is worth noting that none of the five comprehensive LCSA studies mentioned in Table 1 use a prospective LCA or a building decomposition model.

* For the LCC, a need for future construction and energy prices integrated in prospective scenarios is highlighted by [6]. As the building life span varies often between 30 and 50 years, these assumptions highly affect LCA and LCC results [6]. The building decomposition model mentioned above is also necessary for the LCC.

* For the SLCA, the common indicator used to assess thermal discomfort is questionable. Indeed, the PMV/PPD indicator is mainly used in studies mentioned in Table 1 but its performance is contested, as its accuracy varies strongly between ventilation, building types and climate [37]. In particular, the PMV/PPD is not suitable for heterogeneous conditions and extreme temperatures, while other models reproduce more precisely human thermoregulation and thermal sensation in these conditions [38].

Finally, actors in the buildings renovation sector are in need for
practical and operational solutions. Thus, the identification and the
description of optimal renovation strategies is substantial. Multi-
objective optimization (MOO) is one of the solutions as it solves prob-
lems with two or more conflicting objectives. Such optimization
methods have emerged in the recent years, especially in building reno-
vation [6]. Optimal solutions, which present various trade-offs among
the objectives, are regrouped in a so-called Pareto front [39,40]. Recent
optimization tools applied non-dominated sorting genetic algorithm
(NSGA-II or NSGA-III) [16,26] to reduce the computational cost. Clus-
ters of optimal renovation strategies located on the Pareto front are
sometimes implemented, in order to synthesize the outputs in arche-
types of renovation strategies [41]. Indeed, solutions on the Pareto front
may induce very different renovation strategies and a technical classi-
fication based on the renovation categories is thus required.

Therefore, the aim of this paper is to provide a decision-making
framework to identify optimal building renovation strategies,
following a LCSA methodology. The originality of this research lies in
the combination of six points:

* An LCSA framework that evaluates renovation strategies from a life cycle perspective. The mixed assessment of environmental, economic and health impacts is a real novelty. This article proposes an approach to better integrate social performance into the sustainability evaluation of building renovations.

* Integration of climate change into the framework, to assess impacts of future climate conditions on the choice of renovations.

* A large experiment plan of renovation strategies, which include building envelope and HVAC systems.

* Integration of prospective data for LCA and LCC to consider future changes, as well as a building decomposition model to trace thorough component flows for each renovation strategy. Choice of a thermal discomfort indicator more suitable for heterogeneous conditions and extreme temperatures.

* A MOO to identify a Pareto front of optimal renovation strategies, followed by a clustering to retain archetypes of optimal renovation strategies.

* Prospective data and renovation strategies adequate to the French context, illustrated by a case study of a residential building.

Section 2 details the methodology of our study. It contains the description of an existing building and the utilization of a renovation actions' database, the multi-sector forecast, the description of the modeling tools, the choice of indicators for the MOO and the identification of optimal renovation strategies. Three indicators are calculated in this study: carbon footprint for the LCA, direct global cost for the LCC and the Standard Effective Temperature (SET) for thermal discomfort. The study further integrates multi-criteria clustering and Pareto-optimal strategies to identify cost-effective, low-carbon renovation solutions that ensure climate resilience.

This methodology is then applied to a residential building in France
subsection 3.1 and the outputs are described in subsection 3.2. Then, the
influence of two key parameters is calculated: the climate zone and the
initial energy systems for heating and domestic hot water (DHW)
(subsection 3.3). Eventually, results are compared to the literature
(section 3.4), as limitations and outlooks are presented. The results
provide concrete, robust guidelines for policymakers and professionals
in the sector, which could be adapted and replicated elsewhere.

## 2. Methodology

The methodology presented here aims to evaluate and identify
optimized renovation solutions that can be applied to an existing
building, using an enhanced LCSA methodology [6]. It deals with three
major issues involved in the life cycle of a renovated building: carbon
footprint of the renovation, summer thermal discomfort for the occu-
pants and overall cost associated with the renovation. Among all the
social factors that could be considered, only the summer thermal
discomfort is calculated in this study. The methodology developed by
the authors is described in Fig. 2. The first phase implements the design
of experiments. Components and energy performance of the existing
building are described, while the renovation strategies are defined.
Then, the second phase consists of modelling the impacts for every
renovation strategy. A climate, environmental and economic forecast is
implemented into the Dynamic Energy Simulation and the LCSA tools.
Therefore, the third phase calculates indicators for every renovation
strategy, namely thermal discomfort, carbon footprint and direct global
cost. Finally, the MOO provides a 3-dimensional Pareto front of optimal
renovation strategies. Based on the 3 indicators, clusters of optimal
renovation strategies are created.

2.1. *Design of experiments*

The methodology is applied on a specific residential building
described by approximately a hundred variables, detailing generic in-
formation about the building, the energy systems and the thermal en-
velope. This simplified description, called *Pivot Data Sets* (PDS), has been
developed since 2022 by the CSTB in order to obtain a format that is
interoperable with the various dynamic energy simulation engines used
in France (such as the 3-CL method for the DPE [42] or the COMETh
engine [43]). A PDS describing the studied initial building is automat-
ically modified when a renovation strategy is applied.

A database has been assembled gathering representative renovation
actions for every main renovation category: insulation of walls, roof, low
floor, windows, solar protections, ventilation, heating, DHW, cooling.
Every renovation action is associated with physical characteristics, environmental data, and costs.

## 2.2. Forecast modelling

The modeling of the renovated building life cycle is needed to evaluate the impacts associated to each renovation strategy. Since buildings and their components generally have a long lifespan [9,44], spanning several decades, a multi-sector forecast is necessary. A service life span of 50 years is often used in LCSA studies [6] and is thus chosen in this paper. The renovation is supposed to happen in 2022. The various forward-looking models are described below.

### 2.2.1. Climate forecast

Climate series are used in this study, using the EURO-CORDEX database [45]. A representative concentration trajectory (RCP) of 8.5 by 2100 is set, i.e. the pessimistic business-as-usual global warming scenario [46]. This RCP trajectory is then incorporated into the combination of six global climate models (GCM) and five regional climate models (RCM) as the uncertainty related to the models is more important than the choice of the RCP for assessments in a few decades' time [47]. Lastly, a bias correction procedure is used to locate the climate series on a weather station, based on observed ERA5 data over the period 1981-2019 and a Hermite cubic interpolation Hermite interpolation [48], in order to obtain hourly climate series. The detailed methodology is described in [49]. Finally, for every location, every combination of a GCM and a RCM produces a future Typical Meteorological Year (fTMY) [50] representing the 2020-2058 period. Thus, 26 climate series are created for this period.

### 2.2.2. Environmental forecast

To realize a Prospective Life Cycle Assessment pLCA (subsubsection 2.3.2), it is important to model the evolution of the background of the studied system (here the renovated building), hence the energy and the industry (for components). In this paper, a Scenario-based pLCA is presented where the background evolution follows different possible futures. Only the evolution of the energy sector is modeled as it is more studied in the literature [51,52], and because the industry is very diverse and a precise description of the life cycle inventory is necessary [53]. Since only three energy vectors are considered in the experiment, to be compared with more than 30 components, the prospective study is limited to the energy sector. However, this is a strong limitation, and potential solutions are described in the Discussions section.

First, considering the biomass and the use of wood as energy, it is assumed that the carbon intensity (CI) remains constant over the 50 years, as there are no studies on the potential evolution of the CI of the wood-energy in France to the authors' knowledge. The value of 30 gCO₂eq/kWh is chosen as it is the reference value calculated by ADEME for the French context ([54], annex 4).

Secondly, the outlook for electricity generation is based on the work of [23]. They calculated the evolution of the carbon intensity of the French electricity mix according to the different energy scenarios between now and 2050 proposed by the RTE transmission network [55]. Of these six scenarios, an 'intermediate' scenario called N1 is chosen in this paper, in which nuclear power accounts for 27 % of the electricity mix in 2050, while renewable energies complete the mix. Assuming that the CI of each energy source remains constant, [23] obtain an electricity mix that falls from 78.3 gCO₂eq/kWh in 2020 to 20.6 gCO₂eq/kWh in 2050, and then remains constant after 2050. Assuming a linear decrease of the electricity's CI, this corresponds to an annual reduction of 2.54 %. This gives an annual value for the CI of the French electricity mix for the environmental forecast.

Finally, the choice of decarbonization scenarios for gas is based on ADEME's forecasting work ([56], p.397). Four scenarios have been modeled. For this paper, a median scenario (S2) has been adopted, as for electricity. This scenario implies a high degree of sobriety in demand

(particularly in industry and energy) as well as a major development of methanization. The same methodology as for the electricity prospective is made and described in Table 2.

Fig. 1 synthesizes the evolution of carbon intensity for the three energy vectors due to the environmental forecast in our study. These assumptions are based on literature, as detailed above. The yearly reduction calculated for gas and electricity enable the calculation of annual values for each carbon intensity.

### 2.2.3. Economic forecast

The economic forecast is modeled thanks to two main parameters:

* Inflation rate. It measures the percentage rise in the general price level over time. In this paper, its value is fixed at 2 % as it is a common value for prospective values in France [4].

* Discount rate. It is the rate used to convert future cash flows into their present value. The French think tank *France Stratégie* advises a value of 3.2 % for an economic forecast in landscaping projects [60].

## 2.3. Modelling tools

### 2.3.1. Building dynamic energy simulation (BDES)

Building Dynamic Energy Simulation (BDES) is needed to quantify the energy consumption induced by the renovation strategy, and to model precisely the physical parameters in the building. These hourly physical parameters are then used to assess thermal discomfort. Five energy usages are evaluated: heating, cooling, ventilation and auxiliaries, domestic hot water and lighting. A BDES is needed as it describes more accurately the dynamic phenomena involved in the building, such as inertia, a key factor in assessing thermal discomfort [61]. The Cometh-BDES model [43] is retained in this methodology and the building model is set up from the PDS format. The occupation scenario is based on the 2020 French regulation for new buildings [62] and is in the depot linked to this article [dataset] [63].

The Cometh-BDES model, developed by CSTB, is based on a simplified yet robust dynamic thermal model inspired by ISO 13790. It simulates heat transfers between the indoor and outdoor environments using an electrical analogy, modeling the building as a 5RC network (five thermal resistances and a single heat capacity). The calculations are performed at the room level and uniform thermal and airflow conditions are assumed. Validated through reference protocols like ASHRAE 140 (BESTEST), Cometh-BDES accurately reproduces annual heating and cooling needs as well as peak temperatures, while maintaining fast computation times and compatibility with real-world design and regulatory constraints.

As the methodology focuses on the building level and not on the district level, the influence of localized external parameters is not studied. Therefore, the specificity of buildings in dense urban areas, in which urban heat islands can occur, is not treated in this methodology.

<caption>**Table 2**</caption>

Carbon Intensity (CI) and Yearly Reduction for the prospective of gas production.

<table><thead><tr><th>Source</th><th>Carbon Intensity (gCO<sub>2</sub>-eq/kWh)</th><th>Current mix 2015</th><th>ADEME S2 2030</th><th>ADEME S2 2050</th></tr></thead><tbody><tr><td>Biogas</td><td>44 [57]</td><td></td><td>14%</td><td>60%</td></tr><tr><td>Syn gas</td><td>44</td><td></td><td>0%</td><td>3%</td></tr><tr><td>Power to methane</td><td>100 [58]</td><td></td><td>1%</td><td>20%</td></tr><tr><td>Natural gas</td><td>227 [59]</td><td></td><td>83%</td><td>16%</td></tr><tr><td>Estimated Carbon Intensity (gCO<sub>2</sub>-eq/kWh)</td><td></td><td>227 [59]</td><td>196</td><td>84.8</td></tr><tr><td>Yearly reduction in average CI (%/year)</td><td></td><td></td><td>0.91%</td><td>2.8%</td></tr></tbody></table>

Notes: Due to lack of information, it is assumed that the syn gas CI is equal to the biogas CI
<figure><img src="image_4.png" /><figcaption>Fig. 1. Evolution of carbon intensity for electricity, wood and gas due to the environmental forecast.</figcaption></figure>

As we consider detached buildings, the effect of solar masks due to nearby buildings is also not considered in the BDES. Besides, it is worth noting that this choice is consistent with the climate forecast in our methodology (section 2.2.1). Indeed, prospective climate series are interpolated with weather stations that are often located in rural areas, with lower building density.

### 2.3.2. *Life cycle assessment (LCA)*

Environmental assessment of retrofit and renovation strategies is more and more used in studies as Fnais et al. found more than 50 recent LCA studies conducted on use case issues related to buildings [35]. Most studies focus on the reduction of energy consumption during the use phase, while the environmental impacts may be transferred to different life cycle stages [64]. While some case studies only consider thermal insulation as retrofit measures [65,66], others only calculate the environmental impacts of new components and not those that are preserved or substituted during the renovation [25,67]. Therefore, the Quartier Energie Carbone methodology, applied for building renovation, is used in this paper [68] as it enables to compare different renovation strategies while considering preserved, substituted and new components [69]. The Quartier Energie Carbone methodology only considers future impacts in the life cycle of the renovated building but extends the boundaries, with different assumptions depending on the type of component. For new embodied components, modules A1-5 (product stage), B1-5 (use stage without operational energy and water) and C1-4 (end-of-life stage) are considered, while only module C is taken into account for remaining existing building materials and substituted materials [8]. The yearly energy consumptions of the renovated building, calculated with the Cometh model (subsubsection 2.3.1), are integrated in the LCA as the module B6 encompasses the operational energy use. Only the module B7 (operational water use) is not considered in this paper, due to lack of data.

The environmental data comes from the French INIES database [70] which is a reference in the building sector. Whenever it is possible, collective data are preferred, followed by individual data and finally environmental data by default. This database gives detailed LCA results for every building component, broken down into modules A, B and C. For equipment producing an energy service, the B6 result is retrieved as it represents a conventional situation and is thus replaced by the outputs of the BDES.

Regarding the quantification of building components involved in every renovation strategy, a dynamic model of the building's materiality is included in the methodology. Indeed, the IEA EBC Annex 72 highlighted in 2023 the need to extend the scope of buildings' components in LCA methods to reach soon a harmonized information structure for the

building decomposition model. Building internal elements, finishes or building systems shall be incorporated in LCA studies [36]. This is why the TyPy tool is used in this paper, following the works of [71]. The building's architecture corresponds to the tree structure deployed by the Environmental and Sanitary Data Sheets in the INIES database [70]. A set of algorithms is used to automatically associate the building components with the building, based on the information provided by the PDS. The algorithms use predefined assemblies that are representative of the building. Using a simplified description of the building, such as the PDS, the calculation time is reduced, to the order of a tenth of a second per renovated building. It can be used to analyze material flows of consequent building stocks [72,73].

### 2.3.3. *Life cycle cost (LCC)*

The life cycle cost analysis is used to estimate the total cost of the renovation. The objective is to evaluate the overall cost that accounts for the different costs operating over the life cycle of the renovated building [74]. The ISO 15686-5:2017 norm details the general methodology [75]. As for the LCA, the production stage (module A) is considered for new components. The repair and replacement (modules B3-B4) stages are considered for both new and remaining components. The end of life (module C) is considered for new, remaining and substituted components. Costs linked to the operational energy use (module B6) are also included.

Economic data come from the Batiprix database [81] which references the cost estimates for construction materials and the workforce in the building sector, for new constructions and renovations. The data from 2022 were used.

## 2.4. *Indicators*

3 indicators have been selected for the multi-objective optimization (MOO).

### 2.4.1. *Thermal discomfort*

The SET (Standard Effective Temperature), based on the 2-node thermoregulation model of Gagge [76], can be used to modulate thermal comfort and the perceived health risk as a function of physiological variables. The SET corresponds to the 'equivalent dry temperature of an isothermal enclosure at 50% relative humidity in which a subject, while wearing standard clothing for the activity concerned would have the same skin humidity and the same heat exchange at the skin surface as in the environment studied. ASHRAE details the classes of thermal sensation as a function of the SET (Table 3, [77]). This indicator is the most appropriate for the study because it allows to analyze several thermal
<table><caption>**Table 3**
Quantitative thermal sensation scale of the SET index [77].</caption><thead><tr><th>SET (°C)</th><th>Thermal sensation</th><th>Physiological state</th></tr></thead><tbody><tr><td>&gt;37.5</td><td>Very hot, very uncomfortable</td><td>Failure of thermoregulation</td></tr><tr><td>34.5-37.5</td><td>Hot, very unacceptable</td><td>Profuse sweating</td></tr><tr><td>30.0-34.5</td><td>Warm, uncomfortable, unacceptable</td><td>Sweating</td></tr><tr><td>25.6-30.0</td><td>Slightly warm, slightly unacceptable</td><td>Slight sweating, vasodilation</td></tr><tr><td>22.2-25.6</td><td>Comfortable and acceptable</td><td>Neutrality</td></tr></tbody></table>

environments (moderate or hot), while being able to modify physiological (metabolism *met*, clothing *clo*) and environmental variables (air temperature, radiant temperature, relative humidity, air speed). The clothing and metabolism values are based on ASHRAE reference values (Eq. 1, Eq. 2)

$$clo = 0.5 \qquad (1)$$

$$met = \begin{cases} 1.1 & \text{if } 6 < \text{hour} < 22 \\ 0.7 & \text{else} \end{cases} \qquad (2)$$

The indicator used for the optimization is the Degree Hours of SET SETDH (°C hour) above the thermal discomfort threshold (25.6°C), for a 1-year simulation with the fTMY climate series.

### 2.4.2. Carbon footprint

The carbon footprint CF [kgCO₂-eq/m²/year] is used for the MOO thanks to the Life Cycle Assessment, using the characterization model from the EC-JRC report [78] which is based on the AR6 report of the IPCC2021 [79]. The functional unit used to compare the different renovation strategies is the building itself, analyzed over its Reference Study Period (RSP). The RSP of the renovated building is fixed at 50 years, as it is a common value among the community [6]. The GWP100 midpoint indicator (kgCO₂-eq) is used to calculate the GHG emissions. The carbon footprint is the only indicator currently evaluated in the LCA, as it is the most studied [6].

### 2.4.3. Direct global cost

The LCC enables to calculate the Direct Global Cost (DGC) [€/m²/year] and represents all the costs linked directly to the renovated building [79]. The functional unit used to compare the different renovation strategies is the building itself, analyzed over its Reference Study Period (RSP). The RSP of the renovated building is fixed at 50 years, as it is a common value among the community [6]. The Eq. described by [80] is used in this paper and reproduced in Eq. 3 to calculate the LCC. The formula to calculate the DGC is in Eq. 4.

$$LCC = \sum_{i=1}^{b} C_{production,i} + \sum_{i=1}^{b} \sum_{l=1}^{n_i} \frac{C_{replacement,i}(1+r)^{l+RSL_i}}{(1+d_{nominal})^{l+RSL_i}} \\ + \sum_{l=1}^{RSP} \left( \sum_{i=1}^{b} \frac{C_{repair,i}(1+r)^l}{(1+d_{nominal})^l} + \frac{Q_{F,a} \times m_{op} \times ERA \times (1+r)^l}{(1+d_{nominal})^l} \right) \\ + \sum_{i=1}^{b} \frac{C_{EoL,i}(1+r)^{RSP}}{(1+d_{nominal})^{RSP}} \qquad (3)$$

$$DGC = \frac{LCC}{RSP \times ERA} \qquad (4)$$

The initial investment cost $C_{production,i}$ [€] for component i is calculated using the Batiprix database [81]. The Reference Study Period (RSP) is fixed at 50 years, while the discount rate $d_{nominal}$ values 3.2% and the inflation rate r values 2% (these assumptions are justified in subsection 2.2.3).

$C_{EoL,i}$ [€] is the end-of-life cost for component i. Due to a lack of data, it is assumed that it represents 10% of the production cost $C_{production,i}$.

For every component i, $n_i = |RSP/RSL_i| - 1$ is the number of times the component must be replaced during the building's service life RSP.

$ replacement,i = Cproduction,i + CEoL,i [€] is thus the cost related to

the replacement of the component i.

Finally, during the building's service life RSP, operational costs are considered every year through two parameters:

* The repair cost $C_{repair,i} = k \times C_{production,i}$ [€]. k depends on the component and the values were advised by the IEA EBC Annex 75 for an Austrian neighborhood [82].

* The utilization cost due to energy consumptions. $Q_{F,a}$ [kWh/m²/year] is the final yearly energy need of the building (calculated with the Dynamic Energy Simulation), ERA [m²] is the energy reference area and $m_{op}$ [€/kWh] is the operational cost for all energy uses calculated in the Dynamic Energy Simulation. The reference values were taken in 2023 with the values issued by the SDES [83].

### 2.5. Identification of optimal renovation strategies

To identify optimal renovation strategies, the Pareto dominance relation is studied, which is the most famous and widespread approach [84]. Eq. 5 describes this notion.

Let N be the number of criteria and $f_i$ be the objective function associated with criterion i, for $i=1,...,N$. $x^*$ dominates x if and only if:

$$ \begin{cases} \forall i \in [1...N], f_i(x^*) \le f_i(x) \\ \exists k \in [1...N], f_k(x^*) < f_k(x) \end{cases} \qquad (5) $$

In this paper, the Pareto dominance relationship is based on 3 criteria: thermal discomfort, carbon footprint and direct global cost. 3 objective functions are thus calculated using the LCSA framework, following the description of the indicators in section 2.4. Pareto solutions seek to minimize these 3 objective functions.

Besides, a clustering is realized to identify archetypes of optimal renovation strategies. The classification is based on the three continuous variables used in the MOO (carbon footprint, direct global cost, thermal discomfort). A K-Means clustering is therefore applied as it is a fast and efficient clustering for such homogeneous data [85]. As the number of clusters is chosen beforehand, the Silhouette Score [86], ranging from 0 to 1, is calculated for every classification, from 2 to 9 clusters. The number of clusters is thus a trade-off between the maximized Silhouette Score and the classification of the clusters in terms of renovation actions.

### 2.6. Overall methodology

The overall methodology is synthesized in Fig. 2.

## 3. Results

Section 3 is dedicated to the results of the methodology. Section 3.1 presents the case study, which is a French residential building, as well as the design of experiments. Two initial energy systems (A: electricity, B: gas) and two French climate zones (H1a, H3) are studied. Section 3.2 details the results for one building configuration (B-H3). Section 3.3 focuses on the influence of the climate zone (section 3.3.1) and of the initial energy systems (section 3.3.2) on the identification of optimal renovation strategies.

### 3.1. Case study

For the rest of this article, a case study involving an existing individual housing is considered, whose characteristics are defined in Table 4. This architecture is particularly representative of the French building stock as it refers to buildings created just after the first thermal regulation in France (in 1974) which required a minimum level of envelope insulation for new buildings. One third of the individual houses built between 1975 and 2000 followed that architecture. This typology is particularly important as it represents 13% of the French building stock of individual houses and 12% of the final energy consumption
<figure><img src="image_5.png" /><figcaption>Fig. 2. Scheme describing the overall methodology (made by the authors).</figcaption></figure>

<table><caption>**Table 4**

Description of the case study.</caption><thead><tr><th>Usage</th><th>Individual housing</th></tr></thead><tbody><tr><td>Typology</td><td>4-room house, solid concrete block walls, no storey</td></tr><tr><td>Construction year</td><td>1975-1985</td></tr><tr><td>Floor area (m²)</td><td>95</td></tr><tr><td>Walls</td><td>25cm, no insulation</td></tr><tr><td>Roof</td><td>Attic roof, insulated with 8cm of glass wool (U=0.485 W/m².K)</td></tr><tr><td>Windows</td><td>Single-glazed wood windows (Uw=4.7 W/m².K), facing North, West and South</td></tr><tr><td>Low Floor</td><td>Solid concrete slab, not insulated (U=0.86 W/m².K), on solid ground</td></tr><tr><td>Ventilation</td><td>Self-adjusting single flow mechanical ventilation (P=50W, Q=1.1m³/h)</td></tr></tbody></table>

[87].

To complete the description of the case study, two parameters are missing: the climate zone as well as the heating & DHW systems of the building. Indeed, this typology can have multiple energy carriers and is present in different climate zones all over France. 50% use electricity while 28% use gas, while this typology can be found in rural areas or on the outskirts of urban areas and are not exclusive to one climate zone. Therefore, these parameters will be varying in this study and will have

two possible values:

* Climate zone: H1a (Vélizy, close to Paris, northern France: low temperatures in winter and a consequent thermal amplitude between winter and summer, equivalent to a 4A "Mixed Humid" for ASHRAE [20]) and H3 (Nîmes in southern France: mild winter, hot and dry summer, equivalent to a 3A "Warm Humid" for ASHRAE [20]). The nomenclature follows the one described by the 2020 French regulation for new buildings [62]. For climate zone H1a, the fTMY climate series is a combination of the GCM from MOHC-Had-GEM2-ES and the RCM of the CRNM-ALADIN63. For climate zone H3, it is a combination of the GCM from ICHEC-ECEARTH and the RCM from KNMI-RACMO22E. Specifications of these climate series are synthesized in Table 5 and Fig. 3. As a reminder, the fTMY methodology used for climate forecast is detailed in section 2.2.1 and precisely explained in [49]. Heating degree days are 30% lower in climate zone H3 while cooling degree days are 2 times greater.

* Heating & DHW initial systems: configuration A (electric radiators, electric water heater and 250L hot water tank) and B (condensing gas boiler and DHW production with micro-accumulation and a 30L hot water tank).
<table><caption>**Table 5**
Specifications of the climate series H1a and H3.</caption><thead><tr><th></th><th>H1a (Vélizy) 2020-2058</th><th>H3 (Nîmes) 2020-2058</th></tr></thead><tbody><tr><td>Annual high temperature (°C)</td><td>16.67</td><td>20.75</td></tr><tr><td>Annual low temperature (°C)</td><td>9.06</td><td>11.32</td></tr><tr><td>Average relative humidity (%)</td><td>75.62</td><td>53.53</td></tr><tr><td>Heating degree days (baseline 18°C)</td><td>2227</td><td>1533</td></tr><tr><td>Cooling degree days (baseline 22°C)</td><td>112.6</td><td>287.2</td></tr></tbody></table>

Thus, the study aims to analyze the results for three variations of the case study: A-H1a, B-H1a and B-H3. Therefore, the study will calculate the singular impact of these two parameters. The PDS of the configurations A and B are in the depot [dataset] [63].

A database of renovation actions has been created to implement an experimental plan that fits with the case study described in subsection 3.1. Renovation actions are described in Table 6. Technical parameters, such as nominal powers of systems or thermal conductivities of

insulations are based on the values declared on the French environmental database INIES [70]. In the absence of certain data, assumptions were established based on the expertise of CSTB professionals. Environmental and economic data are in the depot linked to this article [dataset] [63].

A renovation action may have induced renovation actions, depending on the existing state. For example, for configuration A, the $HD_2$ renovation action implies the removal of the electric radiators and the installation of water radiators. These induced renovation actions are taken into account in the methodology, thanks to TyPy [71].

Thus, every renovation strategy S studied in the design of experiments is a combination of every renovation category's possibility. However, the creation of S is not a pure statistical combination, to avoid overlapping renovation actions in the same renovation category. For example, S cannot include a HD renovation action (heating and DHW) and a D renovation action (DHW). Moreover, the "No renovation" action is considered. For every renovation category, the index 0 suggests that the component is preserved over the life cycle. Therefore, for configuration A, $HD_4 = HD_0$ and, for configuration B, $HD_3 = HD_0$.

The calculation was made on an individual computer with 16GB

<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Hourly temperature variations for climate series H1a (top) and H3 (bottom), based on the fTMY methodology [49].</figcaption></figure>
<table><caption>**Table 6**
Renovation actions for this case study.</caption><thead><tr><th>Renovation Category</th><th>Description</th><th>ID</th></tr></thead><tbody><tr><td>Heating (H)</td><td>Air-to-air heat pump (P = 10kW, SCOP = 3.9)</td><td>H<sub>1</sub></td></tr><tr><td>Heating (H)</td><td>Electric radiators (P = 1kW)</td><td>H<sub>2</sub></td></tr><tr><td>Heating (H) &amp; DHW (D)</td><td>Air-to-water heat pump (P = 5kW, SCOP = 4.0, V = 190L)</td><td>HD<sub>1</sub></td></tr><tr><td>Heating (H) &amp; DHW (D)</td><td>Wood boiler (P = 27kW, V = 128L)</td><td>HD<sub>2</sub></td></tr><tr><td>Heating (H) &amp; DHW (D)</td><td>Condensing gas boiler (P = 24kW), microaccumulation for DHW (V = 30L)</td><td>HD<sub>3</sub></td></tr><tr><td>DHW (D)</td><td>Thermodynamic water heater with storage (V = 300L)</td><td>D<sub>1</sub></td></tr><tr><td>DHW (D)</td><td>Electric water heater (P = 2kW, V = 250L)</td><td>D<sub>2</sub></td></tr><tr><td>Cooling (C)</td><td>Air-to-air heat pump (P = 10kW, SEER = 6.0)</td><td>C<sub>1</sub></td></tr><tr><td>Heating (H) &amp; Cooling (C)</td><td>Reversible air-to-air heat pump (P = 10kW, SCOP = 3.9)</td><td>HC<sub>1</sub></td></tr><tr><td>Cooling (C)</td><td>Ceiling fan for day use (Q = 10000m<sup>3</sup>/h)</td><td>C<sub>2</sub></td></tr><tr><td>Ventilation (V)</td><td>Dual-flow ventilation (Q = 125m<sup>3</sup>/h, η = 0.85)</td><td>V<sub>1</sub></td></tr><tr><td>Wall (Wa)</td><td>ETI<sup>1</sup>: 24cm wood wool (U = 0.16W/m<sup>2</sup>.K)</td><td>Wa<sub>1</sub></td></tr><tr><td>Wall (Wa)</td><td>ETI<sup>1</sup>: 24cm glass wool (U = 0.17W/m<sup>2</sup>.K)</td><td>Wa<sub>2</sub></td></tr><tr><td>Wall (Wa)</td><td>ITI<sup>1</sup>: 12cm glass wool (U = 0.27W/m<sup>2</sup>.K)</td><td>Wa<sub>3</sub></td></tr><tr><td>Wall (Wa)</td><td>ITI<sup>1</sup>: 10cm wood wool (U = 0.38W/m<sup>2</sup>.K)</td><td>Wa<sub>4</sub></td></tr><tr><td>Roof (R)</td><td>ITI<sup>1</sup>: 30cm rock wool (U = 0.11W/m<sup>2</sup>.K)</td><td>R<sub>1</sub></td></tr><tr><td>Roof (R)</td><td>ITI<sup>1</sup>: 30cm cellulose wadding (U = 0.10W/m<sup>2</sup>.K)</td><td>R<sub>2</sub></td></tr><tr><td>Windows (Wi)</td><td>PVC Double-glazed windows (U<sub>w</sub>=1.4W/m<sup>2</sup>.K)</td><td>Wi<sub>1</sub></td></tr><tr><td>Solar Protections (SP)</td><td>External white roller shutters (U = 5W/m<sup>2</sup>.K)</td><td>SP<sub>1</sub></td></tr><tr><td>Solar Protections (SP)</td><td>Aluminum sunshades above windows (e = 1m)</td><td>SP<sub>2</sub></td></tr><tr><td>Low Floor (LF)</td><td>ITI<sup>1</sup> under screed: 15cm wood wool (U = 0.2W/m<sup>2</sup>.K)</td><td>LF<sub>1</sub></td></tr><tr><td>Low Floor (LF)</td><td>ETI<sup>1</sup>: underside, 31cm PSE (U = 0.11W/m<sup>2</sup>.K)</td><td>LF<sub>2</sub></td></tr></tbody></table>

memory and a 12th Gen Intel Core i5 1.60GHz processor. The calculation time for a complete design of experiments, including more than 11,000 renovation strategies, is 30 hours.

## 3.2. Detailed results for one building variation (B-H1a)

The reference scenario, represented by an orange square with blue edges in Fig. 4, exhibits a high carbon footprint due to gas consumption and significant thermal discomfort. However, it maintains a low global cost, as gas remains more affordable than wood and electricity. Each point in the scatter plot corresponds to a unique renovation strategy, with a total of over 11,000 simulated cases. The results show that the carbon footprint of renovation strategies varies between 4 and 50 kgCO₂, eq/m²/year. The direct global cost ranges from 20 to 75 €/m²/year,

while SET thermal discomfort values extend from 0 to 1200°C.h.

The analysis of single-action renovation strategies highlights distinct impacts on carbon emissions, costs, and thermal comfort (Fig. 5). A single-action renovation strategy modifies only one renovation category. The different renovation categories are described in Table 6. For example, renovation strategy $Wi_1$ is a single-action renovation strategy as only windows are renovated, while $Wa_1R_1$ is not as both walls are roof are renovated. It enables to see the unitary impacts of every renovation action. Solar protections ($SP_{1,2}$) effectively reduce thermal discomfort by 16% to 24%, but they slightly increase both the carbon footprint and the direct global cost by 1% to 5%. Wall insulation ($Wa_{1,2,3,4}$) exhibits contrasting effects depending on the type of insulation used. External Thermal Insulation (ETI) significantly reduces both thermal discomfort and carbon footprint but leads to an increase in global cost. In contrast, Internal Thermal Insulation (ITI) decreases both the carbon footprint and global cost but worsens thermal discomfort due to the high inertia of the concrete walls. There is an exception for the low floor insulation ($LF_{1,2}$), either external or internal, which increases thermal discomfort. Indeed, the ITI limits the benefits of the high inertia of the low floor, whereas ETI cuts off the floor's contact with the stable temperature of the ground, limiting cooling possibilities when indoor temperatures are high.

The prospective environmental scenario used in this study, with strong decarbonization of the electricity grid sub-section 2.2.2, indicates that systems based on electricity, such as heat pumps ($H_1, HD_1$) and electric radiators ($H_2$), will experience a substantial reduction in their carbon footprint. Likely, the electric water heater ($D_2$) is more interesting than a thermodynamic water heater ($D_1$). Even though the thermodynamic water heater ($D_1$) reduces the energy consumption (up to 60% for the DHW production, compared to the electric water heater $D_2$), its bigger carbon intensity burdens the carbon footprint, up to 6 kgCO₂,eq/m²/year. Indeed, the emission of refrigerant fluid for the thermodynamic water heater represents 64% of its carbon footprint [dataset] [63]. Moreover, an air-to-water heat pump ($HD_1$) has a lower global cost and carbon footprint than a wood boiler ($HD_2$). It even has a lower direct global cost than the air-to-air heat pump with an electric water heater ($H_1D_2$) The transition to a dual-flow ventilation ($V_1$) has moderate consequences as it only reduces the overall energy consumption by 4%, due to the low exchanger efficiency ($η = 0.85$).

The simulation over the whole design of experiments is then realized. The analysis of Pareto-optimal solutions reveals common renovation patterns (Fig. 6). Heating and DHW systems are always renovated, with three main solutions emerging: air-to-water heat pumps ($HD_1$), electric water heaters ($D_2$), and air-to-air heat pumps ($H_1, HC_1$). The latter can be used either exclusively for heating or for both heating and cooling.

<figure><img src="image_7.png" /><figcaption>**Fig. 4.** Impacts of all renovation strategies for case B-H1a.</figcaption></figure>

<footnote>a External Thermal Insulation</footnote>

<footnote>b Internal Thermal Insulation</footnote>
<figure><img src="image_8.png" /><figcaption>Fig. 5. Impacts of single-action renovation strategies for case B-H1a.</figcaption></figure>

Electric water heaters are preferred over thermodynamic water heaters ($D_1$), thanks to their lower carbon intensity, and the low prospective carbon content of electricity provides little added value to the efficiency gain. Solar protections ($SP_{1,2}$) are more frequently implemented than cooling systems, emphasizing their effectiveness in reducing thermal discomfort. Envelope insulation is consistently suggested, albeit at different levels of intensity. The primary focus is placed on wall insulation, as the reference scenario includes no initial wall insulation. Roof insulation is also prioritized due to its role as a major source of heat loss. Window insulation ($Wi_1$) is found to contribute significantly to reducing thermal discomfort.

Considering thermal discomfort as a third optimization criterion significantly expands the range of Pareto-optimal renovation strategies. The inclusion of this additional criterion particularly enhances the presence of windows, solar protections, and cooling systems, which are absent from the two-indicator Pareto front based solely on carbon footprint and cost (Fig. 7).

The K-Means clustering approach applied to the Pareto-optimal

solutions for case B-H1a identifies four distinct clusters (Fig. 8). The silhouette score is calculated at 0.602, with the highest value observed for two clusters. However, two clusters do not provide sufficient differentiation in terms of renovation actions, justifying the choice of four clusters. The first cluster (blue) is characterized by a low carbon footprint, high cost, and moderate thermal discomfort. The dominant strategies include ETI for the low floor and walls, an air-to-air heat pump, and an electric water heater. The second cluster (orange) exhibits low carbon footprint and global cost but higher thermal discomfort than the blue cluster. It is mainly associated with ITI on walls, an air-to-air heat pump, and an electric water heater. The third cluster (green) is marked by a high carbon footprint but the lowest global cost. Its defining strategies involve air-to-water heat pumps, solar protections, and, in some cases, ceiling fans, with minimal envelope insulation. The fourth cluster (purple) is like the orange cluster but achieves minimal thermal discomfort. Renovation strategies in this cluster include reversible air-to-air heat pumps, electric water heaters, and an ITI on walls.

The clustering approach highlights the key trade-offs present in each
<figure><img src="image_9.png" /><figcaption>Fig. 6. Occurrence of renovation actions in Pareto-optimal solutions for case B-H1a.</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>Fig. 7. Evolution of the Pareto front, comparing carbon footprint and global cost (hatched colors) with the three-indicator approach (full colors).</figcaption></figure>

category of renovation strategies. Small modifications in renovation strategies can lead to significant shifts in the optimal solutions. For instance, starting from the green cluster, which performs best in terms of direct global cost but poorly in carbon footprint and thermal discomfort, a slight increase in global cost highlights the solutions of the orange cluster, which achieve balanced performance across all indicators.

### 3.3. Comparative results

#### 3.3.1. Influence of the climate zone

Detailed results are in the depot linked to this article [dataset] [63]. The climate zone significantly impacts the selection of optimal renovation strategies. In case B-H3, characterized by a mild winter and a hot, dry summer, cooling systems such as ceiling fans or reversible air-to-air heat pumps are more frequently implemented compared to case B-H1a
<figure><img src="image_11.png" /><figcaption>Fig. 8. K-Means clustering with four clusters for case B-H1a.</figcaption></figure>

(Fig. 9). This increased focus on cooling solutions is attributed to the greater thermal discomfort observed in the reference scenario. Indeed, the 4°C increase in average annual high temperature for climate zone H3, compared to climate zone H1a (Table 5) leads to greater thermal discomfort for all renovation strategies for the case B-H3. This applies even for Pareto optimal solutions, as the median of thermal discomfort for Pareto optimal solutions is found to be twice as high, compared to case B-H1a (Fig. 11). Additionally, air-to-water heat pumps are not present in the Pareto-optimal solutions for case B-H3, as heating demand is lower in this climate, reducing the relative advantage of such system.

As a reminder, heating degree days are 30% lower for climate zone H3, compared to climate zone H1a (Table 5).

The K-Means clustering optimizes the Silhouette Score with five clusters (Fig. 10). Similar clusters with the case B-H1a (Fig. 8) can be found:

* The orange cluster in both cases is characterized by an electric water heater, an air-to-air heat pump for heating and an ITI for walls. In both configurations, the blue cluster encourages the ETI of walls, while installing an air-to-air heat pump and an electric water heater.
<figure><img src="image_12.png" /><figcaption>Fig. 9. Pareto front for every variation of the case study.</figcaption></figure>

<figure><img src="image_13.png" /><figcaption>Fig. 10. Clusters of the Pareto optimal solutions for case B-H3 (left) and A-H1a (right).</figcaption></figure>

* The purple cluster in cases B-H1a and B-H3 is described by an envelope insulation (at least walls and roof), an electric water heater and the installation of a cooling system (either reversible air-to-air heat pump or a ceiling fan).

Moreover, 2 different clusters are present in case B-H3:

* The black cluster represents a trade-off between carbon footprint and cost while minimizing thermal discomfort. In all cases, a cooling system is implemented, either in the form of a ceiling fan or a reversible air-to-air heat pump. Heating and DHW systems are always renovated, and internal insulation with wood wool is

consistently applied to walls. Compared to the purple cluster, roof is scarcely insulated.

* The red cluster is characterized by the highest carbon footprint but the lowest global cost. In this category, heating and DHW systems are not renovated. Instead, internal insulation is always applied to the walls using glass wool and to the roof using cellulose wadding.

### 3.3.2. *Influence of the initial energy systems*

In the case A-H1a, the existing building has electric radiators for heating and an electric water heater for DHW, while being located at Vélizy. Compared to case B-H1a, the renovation strategies focus less on HVAC systems and more on envelope insulation (Fig. 9). The DHW system is never renovated, and air-to-water heat pumps are not present
in the optimal solutions. Air-to-water heat pumps were interesting in case B-H1a as the direct global cost was lower but, since case A already has an electric water heater, the renovation of the DHW system is less relevant. In contrast, wall insulation is systematically implemented, and renovation categories associated with envelope insulation are more frequent than in B-H1a.

Optimized Silhouette Score corresponds to three clusters, but the classification in terms of renovation actions is not satisfactory. The classification in five clusters is here represented, as the distribution of renovation actions is more adequate. As for the influence of the climate zone, similar clusters between cases B-H1a and A-H1a can be noted (Fig. 8, Fig. 10):

* The orange cluster is both described by an air-to-air heat pump for heating, an electric water heater and an ITI for walls.

* The purple cluster in both cases installs a reversible air-to-air heat pump and insulates the walls.

On top of that, three unique clusters are present in the case A-H1a:

* The grey cluster has a low carbon footprint and a moderate thermal discomfort. It represents renovation strategies that focus on the envelope: ITI of walls and the roof, insulation of the low floor, installation of a dual-flow ventilation and PVC double-glazed windows as well as solar protections.

* The pink cluster regroups the renovation strategies with the highest global cost, but a low thermal discomfort. Such optimal renovation strategies have an air-to-air heat pump (reversible or only for heating), ETI of walls and the low floor, insulation of the roof, renovated windows and ventilation, while sunshades for solar protections.

* The brown cluster is moderate on every indicator. It is characterized by a strong envelope insulation (walls, roof, windows), solar protections and an air-to-air heat pump for heating.

### 3.4. Key findings

All these results presented in section 3 can be summarized in 3 key findings.

Pareto optimal renovation strategies improve the building performance in every configuration, but to varying degrees. Considering the median of optimal renovation strategies for case B-H1a (Fig. 11), carbon footprint is reduced by 90% and thermal discomfort by 34%. Nevertheless, direct global cost is only reduced by 7%. Direct global cost is not very sensitive to the case study, as the median varies between 26 and 28 €/m²/year. The relative dispersion (RD), defined as $RD = IQR/med$ with IQR the interquartile range and med the median of this series, enables to quantify the dispersion of a series. The RD for direct global cost is small as it varies between 0.12 (B-H3) and 0.32 (B-H1a), highlighting the low dispersion around the median. Carbon footprint also has a similar median for all configurations (5 or 6 kgCO₂,eq/m²/year), except the singularity of the red cluster for case B-H3 where the carbon footprint reaches 23 kgCO₂,eq/m²/year. Values are statistically close to the median as the RD is inferior to 0.2 for every case. Finally, thermal discomfort is highly dependent to the climate zone. For Pareto optimal solutions, the IQR values 2000°C.h in climate zone H1a while it values 11,500°C.h in climate zone H3. Therefore, the RD exceeds 1.15 in case B-H3.

Similarities in the optimal renovation strategies can be observed across the different variations of the case study. Envelope insulation is very often recommended, especially the walls which are initially not insulated (81 to 100% of optimal renovation strategies) and the roof (75 to 89% of optimal strategies). The replacement of windows (37 to 62% of optimal strategies) and the adding of solar protections (56 to 62% of optimal strategies) are a good trade-off between a reduction of thermal discomfort and an increase of the carbon footprint. They are thus present in the Pareto optimal renovation strategies. However, there is an exception on the low floor insulation (0 to 31% of optimal strategies).

<figure><img src="image_14.png" /><figcaption>Fig. 11. Box plot of the Pareto optimal solutions for every cases.</figcaption></figure>
Despite a reduction of the carbon footprint, the slight increase of the thermal discomfort is detrimental to such renovation actions. As for the energy systems, optimal renovated buildings have frequently a heat pump for heating and an electric water heater for DHW (60 to 70% of optimal solutions). Cooling systems are barely recommended, except for the climate zone H3 (38% of optimal solutions).

4 to 5 clusters of optimal renovation strategies are identified for every configuration, but 2 archetypes are present in every configuration: 1/ an air-to-air heat pump for heating, an electric water heater and in ITI on walls (orange cluster), 2/ a reversible air-to-air heat pump, an electric water heater and an ITI on walls (purple cluster). The first archetype has a lower carbon footprint and direct global cost, while the latter has a reduced thermal discomfort. For both archetypes, the ITI on walls can be with wood wool or glass wool. A reduction of thermal discomfort is thus at the expense of the carbon footprint and the direct global cost.

# 4. Discussion

This study offers a comprehensive framework for designing renovation projects tailored to individual buildings. This approach helps stakeholders make informed decisions, balancing carbon footprint, cost, and thermal comfort, and supports the transition to sustainable and resilient buildings.

Section 4.1 compares the results with similar case studies in the literature. Section 4.2 aims to underline the limitations of this paper.

## 4.1. Comparison with other studies

As mentioned in the introduction and methodology sections, other studies applied Life Cycle Sustainability Assessment on building renovation strategies. The comparison between the case studies is complicated as research outcomes highly depend on the typology of the building, the geographical location and modeling assumptions. Functional units used in the LCA and LCC must also be the same between comparative studies but three to four different types of functional units can be found in the literature, as mentioned by recent reviews [6,7]. To our knowledge, only four studies have recently applied a similar MOO including carbon footprint, global cost and thermal comfort for renovation strategies [14-17]. While Amini Toosi et al. [15] applied their LCSA model on energy storage systems, the three others studied various renovation strategies:

* On the one hand, Mostafazadeh et al. [16] applied LCSA to a residential building located in Tehran in Iran. Environmental performance, life cycle cost and thermal discomfort hours are calculated. This building can be compared to our case study with configuration B, as it contains non-insulated exterior walls, single-glazed windows, a natural gas boiler and has a surface area of 150 m². However, the ASHRAE climate zone for Tehran (3B) [20] is different from those in our study (4A for Vélizy and 3A for Nîmes). Even if the heating degree days are similar for Tehran and Nîmes, cooling degree days are three times higher for Tehran (1031 compared to 287). In both cases, wall insulation is present in most of Pareto optimal solutions and no certain type of insulation is dominant. In [16], heating systems are less renovated and gas boiler is preferred over heat pump, mainly because of a reduced global cost. Due to higher cooling needs, split air conditioner systems are installed in 80% of Pareto optimal strategies, compared to 38% in our case study. Even if it is detrimental to life cycle cost and environmental performance, cooling systems guarantee a reduced thermal discomfort.

* On the other hand, Zhan et al. [17] developed a methodology to optimize building envelope and photovoltaic arrangement of a building. Six building performance objectives were considered, including thermal discomfort, carbon footprint and global cost. A case study was presented for a residential building in Jinan, China. Its ASHRAE climate zone (4A) is the same as Vélizy studied in this

paper [20]. Highest (resp. lowest) monthly average temperature reaches 27.0°C in Jinan (resp. -0.4°C), compared to 22.3°C (resp. 4.18°C) in Vélizy. However, renovation of the systems is not considered in this study. Even if it slightly increases the global cost, renovations with low-U-value exterior walls and roof are recommended, as for our case study. However, only polystyrene insulation is considered, and bio-based materials can be present in optimal renovation strategies, as highlighted in our study. This same limit can be applied to Mostavi et al. who only optimized building envelope of a school in the USA and did not consider bio-based materials [14].

These comparative studies highlight that trade-offs are necessary between the life-cycle indicators to find optimal renovation strategies. There is no renovation strategy that minimizes all three indicators. Moreover, climate change plays a key role in the optimization, as it radically changes the energy needs of buildings. Mostafazadeh et al. [16] analyzed the influence of climate change in the Pareto front of optimal renovation strategies. They observed a shift in optimal renovation strategies to more cooling systems and less expensive heating systems. As the outdoor ambient temperature increases, the cooling demand increases, especially during summer, while the heating demand decreases during winter. In our paper, for the reference scenario in case B-H3, the energy consumption is reduced by 23% compared to the case B-H1a. Therefore, the initial carbon footprint is 22% lower in the reference scenario for case B-H3. However, the carbon footprint of the optimal renovation strategies is the same for both climate zones.

Similar studies can be found in Switzerland, where the climate change is closer to what France is facing. Considering only the carbon footprint and the global cost [80], the renovation of the heating system is often present in optimal renovation strategies in the Swiss context [13]. However, as the environmental forecast tends to a lower carbon footprint for electricity in France, compared to Switzerland, the wood boiler is not present in optimal renovation strategies in this paper. Moreover, as thermal discomfort is included in the study, renovation actions that were not considered as beneficial for the carbon footprint and/or the life cycle cost are included in the Pareto optimal solutions. For example, renovation of the windows and the adding of solar protections, as well as cooling systems to a lesser extent, are among several clusters of optimal renovation strategies.

Finally, as pointed out by Bourgeois et al. [88], it is necessary to take into account not only new components but also removed and preserved components during the renovation phase, as it influence the optimal renovation strategies. The initial energy systems have a moderate but still existing influence of the optimization. In this paper, comparisons between the Pareto fronts for configurations A (electric radiators and water heater) and B (condensing gas boiler) underline this phenomenon, as it induces different end-of-life treatments for removed components.

## 4.2. Limitations

A critical perspective on the results is necessary, particularly regarding the significant assumptions underlying the study. Assessing the impact of the various assumptions, for instance through Uncertainty Quantification (UQ) and Sensitivity Analysis (SA), is essential and must be carried out in the future [89], in order to provide reliable and robust analyses:

* On the one hand, the UQ enables to guarantee the robustness of the methodology by covering parameters linked to the models such as the evaluation criteria and the prospective scenarios.

* On the other hand, the SA gives insights on the sensitivity of the methodology to input parameters such as technical parameters of the systems, usage, lifetime of the components, environmental and economic data of renovation actions. Expanding the study to additional case variants, different envelope insulation strategies and
renovation actions (such as solar photovoltaic integration) and other residential building typologies (starting with single-family houses and collective housing) is thus necessary. This would be a prerequisite for analyzing the impact of renovation strategies on a large building stock.

* Because of the consequent calculation time for the actual methodology (30 hours for the case study in 3.1), such analyses were not possible in this article. To reduce the calculation time and to be able to compute thorough UQ and SA, it would be adequate to consider algorithms that identify quickly the Pareto front, such as genetic algorithms and NSGA-III [16].

Therefore, indicators' values must be analyzed with care. The same warning exists for the frequency of occurrence of renovation actions on the Pareto front, as it depends on the design of experiments. The study of orders of magnitude and relative comparisons between solutions should be preferred over the study of absolute result values to perform efficient decision making process. As highlighted by the Discussion section, results are highly dependent on the geographical location and on the building's typology. It would therefore be interesting to apply the methodology to other typologies. Besides, a comparison of the results with experimental data is an interesting perspective for the future.

Moreover, the prospective approach can be achieved with an integrated assessment model that defines the consequences of a specific socio-technoeconomic pathway. The climate, economic and environmental prospective scenarios would therefore be coupled, along the whole chain value of the renovated building. The *premise* tool developed by Sacchi and al. [90] is thus an interesting approach.

Furthermore, a dynamic sizing approach for energy systems and specifically heat pumps could provide a more accurate assessment of system performance. Indeed, the power required for a heat pump depends on the thermal insulation of the building [91]. Under- or over-sizing of the heat pump can lead to malfunctions and degradations [92]. That would undermine specifically renovations that do not improve the envelope insulation, which are already barely present on the Pareto front.

Besides, the study relies on a specific clustering method. Exploring alternative clustering approaches, such as hierarchical clustering that handle continuous and categorical variables, could provide further insights into the classification of renovation strategies.

Finally, indicators from other fields could be integrated to reinforce the holistic approach. For instance, other indicators linked to the social dimension may be considered. Noise comfort, sanitary risk following heatwaves or the study of different occupant types are interesting improvements to the methodology. Indoor environment quality is not evaluated in this study but it would be interesting to integrate it in further studies as it can be detrimental for some renovation strategies [18].

## 5. Conclusion

This study presents a multi-objective optimization methodology for building renovation, integrating a holistic and prospective approach. The methodology's integration of thermal comfort as a key criterion addresses a critical gap in existing Life Cycle Sustainability Assessment approaches, which should no longer overlook the social dimension of sustainability in a context of climate change. These renovated buildings represent a trade-off between environmental and economic goals while also providing a comfortable living environment for occupants. This is particularly relevant in the context of climate change, where rising temperatures and more frequent heatwaves pose significant challenges to building performance.

Moreover, the use of multi-objective optimization and clustering techniques allows for the identification of diverse renovation strategies that have different impacts. The Pareto front analysis and K-Means clustering reveal distinct archetypes of renovation strategies, each

offering a unique balance of carbon footprint, cost, and thermal comfort. This nuanced understanding enables stakeholders to make informed decisions that align with their specific goals and resources. This methodology is also a decision-making tool for decision-makers, as meta-analyses help to identify optimal strategies that can be generalized, as well as influential factors.

Three indicators were calculated in this study: carbon footprint of the renovation, thermal discomfort for the occupants, and direct global cost associated with the renovation. The methodology includes the use of dynamic and thorough modeling tools to simulate the life cycle of the renovated building over 50 years. Building Dynamic Energy Simulation quantifies hourly energy consumption and thermal discomfort, Life Cycle Assessment measures environmental impacts with the TyPy tool to have a realistic picture of new, removed and preserved elements. Life Cycle Cost is used for economic evaluation. The study further integrated Pareto-optimal strategies and multicriteria clustering to identify cost-effective, low-carbon renovation solutions that ensure the lowest impact on comfort for the occupants. Archetypes of optimal renovation strategies were also identified.

The case study involved a residential building in France, with variations in climate zones and initial energy systems to assess the influence of these factors on renovation outcomes. Optimal strategies varied according to the parameters studied, but some common trends emerged. Indeed, most of the optimal renovation strategies involved a combination of envelope insulation, particularly for walls and roofs, and the installation of efficient heating and domestic hot water systems, such as air-to-air heat pumps and electric water heaters. Solar protections and efficient windows were also frequently recommended to balance thermal comfort and carbon footprint. However, variations in climate zones and initial energy systems induced slight differences. On the one hand, the climate forecast induced an overall increase of temperature that affected the heating and cooling needs of buildings. As expected, in warmer climates, some renovation strategies included cooling systems to mitigate summer thermal discomfort, despite an increased carbon footprint. Conversely, in colder climates, the focus was on thermal insulation to reduce heating demands. On the other hand, surprisingly, the existing heating and domestic hot water systems induced little changes in the optimal renovation strategies. The existing gas systems were always renovated while existing electric radiators for heating could be preserved if the envelope insulation was greatly improved.

In conclusion, this study provides a robust framework for optimizing building renovation strategies, emphasizing the need for a balanced approach that considers environmental, economic, and social factors. The findings offer practical guidance for policymakers, project managers, and building owners, supporting the transition towards sustainable and resilient built environments. Future research should continue to refine and expand this methodology, ensuring its applicability across a wide range of building types, countries, policy contexts and renovation scenarios. A closer focus on uncertainty quantification and sensitivity analysis is also necessary to provide robust and trustworthy results. A comparison of the results with experimental data is an interesting perspective for the future. This article is a first step for the better integration of social impacts into the evaluation of renovation strategies. Other impact categories that are commonly used in the SLCA community, such as accessibility, health or maintenance could be added in the near future.

## Glossary

### Acronyms

**BDES** Building Dynamic Energy Simulation. 20-21, 23

**CF** Carbon Footprint. 26

**CI** Carbon Intensity. 17-19

**DGC** Direct Global Cost. 26

**DHW** Domestic Hot Water. 12, 15, 31-32, 34, 36, 38, 40, 46-47, 52

**ETI** External Thermal Insulation. 34-35, 38, 43, 46, 48
GCM Global Climate Model. 16, 31

GHG Greenhouse Gas. 3, 26

GWP Global Warming Potential. 26

HVAC Heating, Ventilation, and Air Conditioning. 5-7, 11, 47

ICHEC Irish Centre for High-End Computing. 31

INIES INIES Database. 23-24

ITI Internal Thermal Insulation. 34-35, 38, 43, 46, 48, 52

LCA Life Cycle Assessment. 8-9, 11, 22-23, 53

LCC Life Cycle Cost. 3, 6, 8-9, 11-12, 24, 26, 53

LCSA Life Cycle Sustainability Assessment. 3-5, 8-11, 13, 15, 29, 53

MOO Multi-Objective Optimization. 10-12, 24, 26, 29, 53

PDS Pivot Data Sets. 14-15, 20, 24, 33

PMV/PPD Predicted Mean Vote/Predicted Percentage of Dissatisfied. 9

RCM Regional Climate Model. 16, 31

RCP Representative Concentration Pathway. 16

SA Sensitivity Analysis. 57

SDES Service of Data and Statistical Studies in France. 28

SET Standard Effective Temperature. 24-25, 37

SLCA Social Life Cycle Assessment. 3-4, 5-7, 9

UQ Uncertainty Quantification. 57

## Data availability

Data are stored in a public depot maintained by Mines Paris – PSL [dataset] [63]. More data will be made available on request.

## CRediT authorship contribution statement

**Goessel Thibault:** Writing – review & editing, Writing – original draft, Visualization, Software, Methodology, Investigation, Formal analysis. **Ligier Simon:** Writing – review & editing, Validation, Supervision, Conceptualization. **Girard Robin:** Writing – review & editing, Validation, Supervision, Conceptualization.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgment

Funding source is provided by the Centre Scientifique et Technique du Bâtiment (CSTB) as part of the PhD project of Thibault Goessel (no Grant number).

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