Assessment of climate resilience in vulnerable buildings in the city of Barcelona

Bachelor’s Thesis Bachelor’s Degree in Industrial Technologies Assessment of climate resilience in vulnerable buildings in the city of Barcelona REPORT Author: Maria Cano Teixidó Supervisor: Roser Capdevila Paramio Call: January 2025 Escola Tècnica Superior d’Enginyeria Industrial de Barcelona Pàg. 2 Memòria Assessment of climate resilience in vulnerable buildings in the city of Barcelona Pág. 3 Resum Aquesta tesi analitza la resiliència i el confort tèrmic de l’edifici tipus més representatiu del barri amb menor renda de Barcelona, ubicat en una zona amb clima mediterrani continental. L’objectiu principal és examinar com respon un habitatge considerat vulnerable i poc preparat davant l’increment de les temperatures i altres fenòmens extrems vinculats al canvi climàtic. La selecció de l’edifici ha estat fruit d’un procés previ de recerca acurada sobre quins indicadors de pobresa energètica poden utilitzar-se a escala de barri. Tot i que molts d’aquests indicadors requereixen dades específiques difícilment accessibles com a estudiant, es va identificar que la renda mitjana disponible és una variable recurrent en la majoria d’ells i, a més, accessible públicament. Per aquest motiu, es va optar per fer servir aquest indicador com a base per triar el barri amb menor renda mitjana de Barcelona. Un cop seleccionat, es van estudiar les tipologies constructives predominants i es va escollir l’edifici més representatiu, juntament amb els materials que el componen. L’estudi incorpora tecnologies de refrigeració assequibles per a famílies amb baixos ingressos, prioritzant l’ús d’estratègies passives, aplicades a un model d’edifici desenvolupat mitjançant les plataformes OpenStudio i EnergyPlus. Les dades climàtiques emprades han estat generades seguint la metodologia de l’Annex 80 per a la ciutat de Barcelona, en vista del pitjor escenari previst de l’IPCC, la via RCP8.5. S’han considerat tres escenaris temporals: el present (2006-2025), el futur a mitjà termini (2041-2060) i el futur a llarg termini (2081-100), amb un enfocament especial en les onades de calor extremes. Per avaluar la resposta de l’edifici, s’han utilitzat diversos indicadors de confort i resiliència. Per avaluar la resposta de l’edifici, s’han utilitzat diversos indicadors de confort i resiliència. A més, s’ha dut a terme una anàlisi d’estudis similars centrats en els efectes del canvi climàtic sobre edificis en situació de vulnerabilitat econòmica, amb l’objectiu d’identificar nous indicadors útils i fàcilment aplicables en futures investigacions. A més, s’ha realitzat un anàlisi d’estudis similars centrats en els efectes del canvi climàtic sobre edificis en situació de vulnerabilitat econòmica, amb l’objectiu d’identificar nous indicadors que puguin ser útils i fàcilment aplicables en futurs treballs. Els resultats obtinguts volen contribuir a la detecció i mitigació de la pobresa energètica, destacant com aquesta pot veure’s agreujada pels efectes del canvi climàtic. Tot i que les estratègies passives mantenen una certa eficàcia en l’escenari actual, el seu rendiment disminueix considerablement en els escenaris futurs, posant en risc la salut dels ocupants. Aquest fet posa de manifest la necessitat d’incorporar solucions híbrides o actives de baix cost. Malgrat les limitacions pròpies dels programes de simulació i la incertesa associada a les projeccions climàtiques, aquest estudi pretén assentar les bases per a futurs anàlisis energètics més complerts, tenint en compte a demés la part socioeconòmica, en què els indicadors de pobresa seran de gran ajuda. Pàg. 4 Memòria Resumen Esta tesis analiza la resiliencia y el confort térmico del edificio tipo más representativo del barrio con menor renta de Barcelona, situado en una zona de clima mediterráneo continental. El objetivo principal es examinar cómo responde una vivienda considerada vulnerable y poco preparada ante el aumento de las temperaturas y otros fenómenos extremos relacionados con el cambio climático. La selección del edificio ha sido fruto de un proceso previo de investigación rigurosa sobre qué indicadores de pobreza energética pueden aplicarse a escala de barrio. Aunque muchos de estos indicadores requieren datos específicos que resultan inaccesibles como estudiante, se identificó que la renta media disponible es una variable recurrente en la mayoría de ellos y, además, está disponible públicamente. Por ello, se eligió este indicador como base para seleccionar el barrio con menor renta media de Barcelona. Una vez seleccionado, se estudiaron las tipologías constructivas predominantes y se escogió el edificio más representativo, junto con los materiales que lo componen. El estudio incorpora tecnologías de refrigeración asequibles para familias con bajos ingresos, priorizando el uso de estrategias pasivas, aplicadas a un modelo de edificio desarrollado con las plataformas OpenStudio y EnergyPlus. Los datos climáticos utilizados han sido generados siguiendo la metodología del Annex 80 para la ciudad de Barcelona, en vista del peor escenario esperado por el IPCC, la vía RCP8.5. Se han considerado tres escenarios temporales: presente (2006-2025), futuro a medio plazo (2041-2060) y futuro a largo plazo (2081-2100), con especial atención a los episodios de olas de calor extrema. Para evaluar la respuesta del edificio se han empleado diversos indicadores de confort y resiliencia. Además, se ha realizado un análisis de estudios similares centrados en los efectos del cambio climático sobre edificios en situación de vulnerabilidad económica, con el objetivo de identificar nuevos indicadores útiles y fácilmente aplicables en futuras investigaciones. Además, se ha realizado un análisis de estudios similares centrados en los efectos del cambio climático sobre edificios en situación de vulnerabilidad económica, con el objetivo de identificar nuevos indicadores que puedan ser útiles y fácilmente aplicables en futuros trabajos. Los resultados obtenidos buscan contribuir a la detección y mitigación de la pobreza energética, destacando cómo esta puede verse agravada por los efectos del cambio climático. Aunque las estrategias pasivas mantienen cierta eficacia en el escenario actual, su rendimiento disminuye considerablemente en escenarios futuros, poniendo en riesgo la salud de los ocupantes. Este hecho pone de manifiesto la necesidad de incorporar soluciones híbridas o activas de bajo coste. A pesar de las limitaciones propias de los programas de simulación y la incertidumbre asociada a las proyecciones climáticas, este estudio pretende sentar las bases para futuros análisis energéticos más completos, teniendo en cuenta además la dimensión socioeconómica, en la que los indicadores de pobreza serán de gran utilidad. Assessment of climate resilience in vulnerable buildings in the city of Barcelona Pág. 5 Abstract This thesis analyses the resilience and thermal comfort of the most representative building type in the lowest-income neighbourhood of Barcelona, located in a region with a continental Mediterranean climate. The main objective is to examine how a vulnerable and poorly adapted dwelling responds to rising temperatures and other extreme weather events associated with climate change. The selection of the building was the result of a thorough preliminary investigation into which energy poverty indicators can be applied at the neighbourhood scale. While many of these indicators require specific data that are not easily accessible to students, average disposable income was identified as a recurring and publicly available variable in most of them. Consequently, this indicator was used to select the neighbourhood with the lowest average income in Barcelona. Once it was selected, the predominant building typologies were studied, and the most representative structure, along with its construction materials, was chosen. The study incorporates cooling technologies affordable for low-income households, with a focus on passive strategies applied to a building model developed using OpenStudio and EnergyPlus platforms. The climate data used in the simulations were generated following the methodology proposed by Annex 80 for the city of Barcelona, given the worst expected IPCC scenario, the RCP8.5 pathway. Three temporal scenarios were considered: present day (2006-2025), mid-term future (2041-2060), and long-term future (2081-2100), with special attention given to extreme heatwave events. To assess the building's performance, several indicators of thermal comfort and resilience were used. Additionally, a literature review of similar studies was conducted, focusing on the impact of climate change on economically vulnerable buildings, aiming to identify new, practical, and applicable indicators for future work. Additionally, an analysis of similar studies focusing on the effects of climate change on buildings in situations of economic vulnerability has been carried out, with the aim of identifying new indicators that could be useful and easily applicable in future work. The results obtained seek to contribute to the detection and mitigation of energy poverty, highlighting how it may be exacerbated by the effects of climate change. Although passive strategies retain a certain degree of effectiveness under current conditions, their performance declines significantly in future scenarios, posing a risk to the health of occupants. This situation highlights the need to incorporate low-cost hybrid or active solutions. Despite the inherent limitations of simulation programs and the uncertainty associated with climate projections, this study aims to lay the foundations for more comprehensive future energy analyses, also considering the socioeconomic dimension, where poverty indicators will be of great help. Pàg. 6 Memòria CONTENTS RESUM ____________________________________________________ 3 RESUMEN _________________________________________________ 4 ABSTRACT _________________________________________________ 5 CONTENTS _________________________________________________ 6 ABBREVIATIONS AND SYMBOLS ______________________________ 8 LIST OF FIGURES __________________________________________ 10 LIST OF TABLES ___________________________________________ 14 1. PREFACE _____________________________________________ 15 2. INTRODUCTION ________________________________________ 16 2.1. Motivation ................................................................................................. 17 2.2. Scope ....................................................................................................... 17 2.3. Prerequisites ............................................................................................ 18 2.4. Objectives ................................................................................................ 19 3. THEORETICAL BACKGROUND ___________________________ 20 3.1. Concepts .................................................................................................. 20 3.1.1. Building resilience ....................................................................................... 20 3.1.2. Thermal comfort .......................................................................................... 20 3.1.3. Energy poverty ............................................................................................ 21 3.2. Indicators.................................................................................................. 22 3.2.1. Resilience indicators ................................................................................... 22 3.2.2. Comfort indicators ....................................................................................... 24 3.3. Cooling strategies .................................................................................... 28 3.4. State of the art .......................................................................................... 31 4. METHODOLOGY _______________________________________ 42 4.1. Weather data ........................................................................................... 43 4.1.1. Data collection ............................................................................................. 43 4.1.2. Studied scenarios ........................................................................................ 44 4.1.2.1. TMY ............................................................................................ 44 4.1.2.2. HW ............................................................................................. 44 4.2. Modelled building ..................................................................................... 45 4.2.1. Building’s location ....................................................................................... 45 4.2.2. Geometry and envelope .............................................................................. 47 Assessment of climate resilience in vulnerable buildings in the city of Barcelona Pág. 7 4.2.3. Loads and schedules .................................................................................. 54 4.3. Simulations .............................................................................................. 58 4.3.1. Simulation tools ........................................................................................... 59 4.3.2. Cooling technologies ................................................................................... 60 4.3.3. Studied scenarios ........................................................................................ 63 5. RESULTS _____________________________________________ 65 5.1. Resilience analysis base case ................................................................. 65 5.1.1. Resilience analysis ...................................................................................... 65 5.1.2. Comfort analysis ......................................................................................... 70 5.2. Resilience analysis with cooling technologies ......................................... 80 5.2.1. TMY Analysis .............................................................................................. 80 5.2.1.1. Resilience study ......................................................................... 80 5.2.1.2. Comfort study ............................................................................. 81 5.2.2. HW Analysis ................................................................................................ 85 5.2.2.1. Resilience study ......................................................................... 86 5.2.2.2. Comfort study ............................................................................. 87 6. DISCUSSION __________________________________________ 94 6.1. Resilience analysis, buildings comparison of the base cases ................. 97 6.2. Resilience analysis, buildings comparison with cooling technologies implemented .......................................................................................... 103 7. PLANNING ___________________________________________ 113 8. ECONOMIC ASSESSMENT ______________________________ 115 8.1. Costs of hours dedicated ....................................................................... 115 8.2. Costs of materials .................................................................................. 116 9. ENVIRONMENTAL ASSESSMENT ________________________ 117 10. SOCIAL AND GENDER EQUALITY ASSESSMENT ___________ 120 11. CONCLUSIONS _______________________________________ 122 12. LIMITATIONS AND FUTURE WORK _______________________ 124 13. BIBLIOGRAPHY _______________________________________ 126 Pàg. 8 Memòria Abbreviations and Symbols α: Alpha AC: Air conditioning AdWind: Advanced Windows AWD: Ambient Warmness Degree BC: Base Case BC_CM: Base Case building in Ciutat Meridiana BC_TY: Base Case Typical building in Catalunya BL: Blinds CHE: Cold Hours of Exceedance CM: Ciutat Meridiana’s building CORDEX: Coordinated Regional Climate Downscaling Experiment CTE: Código Técnico de Edificación DI: Discomfort Index GrRf: Green Roof HHE: Annual Hot Hours of Exceedance HI: Heat Index HVAC: Heating, Ventilation, and Air Conditioning HW: Heat Waves HW_LF_MI: Heat Wave, Long Future, Most Intense. HW_LF_LMS: Heat Wave, Long Future, Longest and Most Severe HW_MF_L: Heat Wave, Mid Future, Longest HW_MF_MIS: Heat Wave, Mid Future, Most Intense and Severe HW_P_L: Heat Wave, Present, Longest Assessment of climate resilience in vulnerable buildings in the city of Barcelona Pág. 9 HW_P_MI: Heat Wave, Present, Most Intense HW_P_MS: Heat Wave, Present, Most Severe IOD: Indoor Overheating Degree IPCC: Intergovernmental Panel on Climate Change L: Longest LF: Long Future LMS: Longest and Most Severe MF: Mid Future MI: Most Intense MIS: Most Intense and Severe MS: Most Severe NV: Natural Ventilation OG: Base Case OS: OpenStudio P: Present SCOP: Seasonal Coefficient of Performance SEER: Seasonal energy efficiency ratio TMY: Typical Meteorological Year TMY_LF: Typical Meteorological Year, Long Future TMY_MF: Typical Meteorological Year, Mid Future TMY_P: Typical Meteorological Year, Present TY: The most representative/typical building in Barcelona Pàg. 16 Memòria 2. Introduction Day by day, the impacts of climate change are increasingly evident in our lives, bringing severe weather events that devastate landscapes, buildings, and communities. This reality has been demonstrated repeatedly, yet some individuals continue to ignore or downplay its significance. Unfortunately, the most troubling fact remains: the worst impacts are still ahead. Major climate shifts will soon begin to disrupt towns, cultures, and the world as we know it. These changes will affect the most vulnerable populations first, those who are not well prepared and can’t afford to renovate their homes [69]. Moreover, people residing in already warm regions or tropical climates will experience the earliest and most profound effects [15] [19] [30]. This pressing issue underscores the urgent need to develop solutions that can either stop global warming or at least significantly mitigate its progression before it is too late. Meanwhile, solutions on how to mitigate the effects of climate change are also required for species to live comfortably during extreme weather events. Given the intensifying frequency of these abnormal events [45], dwellings and shelters must be prepared to withstand these changes and provide essential comfort to their inhabitants. Even the ones with not much financial income. This is exactly the focus of this paper, which aims to study and propose different technologies for a low-income household to lessen the effects of climate change in its resilience and comfort. The future of building design emphasizes energy efficiency and climate resilience, aiming to reduce CO₂ emissions and promote a low-emission future that includes “passive houses” [17]. A "passive house" does not rely on active technologies, which use electricity or energy to work; instead, it maintains comfortable temperatures through low-energy, renewable or passive technologies, the ones that don’t use energy to work [38]. Although this study does not focus specifically on passive houses, it presents a comprehensive analysis of the thermal performance of low-income household and the various cooling strategies available to enhance comfort. These strategies must be cost-effective for low-income residents, allowing them to achieve comfort with minimal or no additional energy consumption, such as the solutions used in passive houses [27]. By simulating current and future climate scenarios, this research seeks to identify and compare effective methods for improving indoor thermal comfort while reducing energy use and environmental impact. This study builds on previous works, particularly those by Enrico Tontodonati, Albert Massana, Marina Ariza, and Oswin Crespo [70] [46] [51] [48], who examined how the most common building type in Catalonia responds to the region’s four distinct climatic zones: Barcelona, Sant Salvador de Guardiola, Bages and Seu d’Urgell. By adapting these methodologies and focusing on a different building, this research provides new insights into the efficacy of passive and active cooling options for low-income Assessment of climate resilience in vulnerable buildings in the city of Barcelona Pág. 17 households. The core message of this paper is to highlight the gravity of the climate crisis if we fail to act, emphasizing that the least advantaged populations will be among the first to bear its consequences. 2.1. Motivation Climate change is increasingly evident, with extreme weather events such as heatwaves, floods, and droughts becoming more frequent. Rising sea levels, the accumulation of carbon dioxide in the soil, and higher temperatures are some of the notable consequences [21] . As the effects of climate change intensify, there is an urgent global need for energyefficient solutions to prevent these impacts from becoming irreversible. These solutions can be classified into active and passive categories. Active solutions, such as transitioning from fossil fuel-based energy sources to renewable alternatives, directly addressing the root causes of climate change. In contrast, passive solutions aim to mitigate its effects on existing structures and systems. The primary objective of this project is to examine how global warming may affect the comfort and well-being of individuals, particularly in relation to the buildings they inhabit. This study will explore potential solutions for retrofitting buildings to enhance their resilience and energy efficiency, following a simulation analysis. Furthermore, this research aims to highlight a vulnerable segment of the population that has often been overlooked: individuals with low-income housing. It is important to acknowledge the challenges faced by this group, especially in accessing additional energy resources during extreme weather events. These individuals are less likely to be able to mitigate discomfort in their homes, which can lead to significant health problems and a decline in overall quality of life. This study underscores the importance of addressing the needs of disadvantaged communities in the broader context of climate change adaptation and energy efficiency. 2.2. Scope The first phase of this paper involves defining a structured State of the Art, compiling previous research on energy poverty detection and assessment. A review of various indicators used to evaluate energy poverty both locally and globally is conducted. Additionally, other studies proposing solutions to mitigate the effects of climate change on Pàg. 18 Memòria vulnerable buildings are also discussed. However, this thesis will cover only the energy analysis through simulations; no surveys or detailed economic research will be carried out. The second phase of the research focuses on simulating and analysing different climatic scenarios to assess their impact on the most common type of building found in the most disadvantaged neighbourhoods of Barcelona. First, a thorough investigation is carried out to identify the poorest neighbourhood in Barcelona and determine the most typical building type located there. Once this information is obtained, the building is modelled from scratch, and the input variables for the simulations are defined. Climatic data of Barcelona is provided by previous studies [70] and will be used to simulate three distinct scenarios: the present (2009-2028), the mid-term future (2040-2060), and the long-term future (2080-2100). A primary constraint identified early in the study was the significant amount of time required to obtain and process the weather data in preparation for input into the Open Studio program. However, due to the several Python scripts to optimize the post-processing of the data, this problem was solved [61]. Additionally, the study will examine the predicted effects of increasingly intense and prolonged heatwaves in future years, as the main extreme weather event studied. A further limitation arises from the fact that the future weather data is based on statistical analysis and mathematical algorithms, meaning the results are subject to potential changes, as they represent projections of future conditions rather than certainties. The main objective of this study is to calculate a set of resilience indicators designed to address thermal comfort inside dwellings. These indicators will be applied to different building scenarios, starting with a base case building model without any cooling strategies. This base case will then be compared to the same building with various cooling strategies implemented to assess their effectiveness in improving energy resilience. 2.3. Prerequisites To initiate the project, it is essential to have a solid understanding of key concepts such as resilience, thermal comfort, and energy poverty, as these are fundamental to comprehending the impacts of climate change on people's lives. Additionally, a thorough review of the concept of energy poverty must be conducted, particularly because it is often confused with the concept of fuel poverty [40]. An extensive investigation is carried out to examine how different countries, within their respective contexts, identify energy poverty, the indicators they use, and the strategies they implement to mitigate it. This deeper understanding of energy poverty is needed to select the appropriate neighbourhood where the study will take place. Assessment of climate resilience in vulnerable buildings in the city of Barcelona Pág. 19 Furthermore, acquiring a solid understanding of cooling technologies is necessary to address the discomfort experienced in residential settings. Autonomous learning of OpenStudio and EnergyPlus is also necessary, as these tools have not been previously used. In addition, a foundational knowledge of Python is essential for effectively executing the scripts required to optimize the results of the simulation process. 2.4. Objectives As previously outlined, the main objective of this project is to study how climate change will affect the resilience and energy efficiency of the most common building type of the neighbourhood with the lowest income in Barcelona. To achieve this, several specific goals must be pursued: • Investigate the concept of energy poverty and develop an extensive State of the Art with studies on useful indicators for quantifying energy poverty and how this problem affects low-income households. • Analyse the poorest neighbourhood in Barcelona to identify the most common building type, structure, orientation, and the types of equipment implemented. • Model the building selected for this study using SketchUp and OpenStudio software. • Review which cooling technologies and strategies can be applied in a low-income household, such as the one that will be modelled in the study. • Simulate the building model under different climate scenarios, applying each of the identified cooling strategies to assess their effectiveness. • Calculate the comfort and resilience indicators, used in previous works [70] [46], to evaluate which cooling strategies provide the best resilience outcomes. • Combine the most efficient strategies and simulate the combination cases to optimize building resilience and energy efficiency. • Obtain resilience and comfort indicators results for the combinations chosen and analyse them. Pàg. 20 Memòria 3. Theoretical background In this section, the main concepts and definitions essential for understanding the present study, its methodology, and subsequent results are introduced. All information has been gathered from a review of academic articles, theses, and other research works within the fields of building resilience, energy efficiency, and thermal comfort. 3.1. Concepts 3.1.1. Building resilience Building resilience can be defined as the ability of a building to adapt to, respond to, and recover from adverse climatic conditions while ensuring the safety, comfort, and well-being of its occupants with minimal energy and environmental impact [66]. In a context increasingly marked by extreme weather events due to climate change, such as heatwaves, prolonged droughts, and intense rainfall, resilience plays a central role in both the design and retrofitting of the built environment [33]. This concept goes beyond mere structural resistance and encompasses the building's capacity to maintain habitable and comfortable indoor conditions, particularly during climate-related crises, without relying exclusively on energy-intensive mechanical systems [33]. Thus, resilience becomes an integrated measure of the building's passive thermal response, the energy efficiency of its systems, and its adaptability to future scenarios of greater climatic severity. The integration of passive cooling strategies such as natural ventilation, green roofs, solar shading, and high-performance windows contributes to enhancing a building’s climate resilience by reducing dependence on active cooling systems and, consequently, lowering energy consumption and associated emissions [25]. Therefore, resilience analysis in buildings is a key tool for sustainable design and for preparing the built environment to face both current and future climate challenges. 3.1.2. Thermal comfort Thermal comfort was defined by Hensen et al. [25] referred to the condition in which occupants perceive their thermal environment as satisfactory. It is influenced by a combination of environmental variables, indoor air temperature, relative humidity, air velocity as well as individual factors such as metabolic rate and clothing insulation [81]. As a critical metric in the assessment of indoor environmental quality, thermal comfort is directly tied to energy performance and occupant productivity. Assessment of climate resilience in vulnerable buildings in the city of Barcelona Pág. 21 In recent decades, anthropogenic climate change has significantly altered ecosystems and the built environment, particularly in Mediterranean regions, where the frequency and intensity of heatwaves have increased markedly [53]. These events pose serious risks not only to human health and productivity but also to energy infrastructures, often leading to surges in cooling demand and an increased probability of power outages. Such conditions can severely compromise thermal comfort and, in extreme cases, the survivability of vulnerable occupants within residential buildings. Thermal comfort is especially relevant when evaluating the performance and resilience of buildings under both current and future climatic scenarios. As demonstrated previous studies [70] [43], thermal comfort is closely linked to the building’s design, the effectiveness of passive and active cooling strategies, and the building envelope’s capacity to mitigate indoor overheating. During heatwaves or power outages, maintaining thermal comfort becomes particularly challenging, underscoring the importance of resilient architectural and technological solutions. In this context, Ensecu et Al. [28] said that thermal comfort is assessed not only by quantitative indicators such as operative temperature and hours of exceedance over established thresholds, but also by qualitative perceptions of the occupants. It is thus a multidimensional concept that intersects health, energy performance, and building resilience. Ensuring acceptable levels of thermal comfort under future climate conditions requires the integration of passive cooling techniques—such as natural ventilation, green roofs, blinds, and high-performance windows—as well as a strategic use of air conditioning systems, all of which contribute to reducing environmental impact and energy-related costs [33]. 3.1.3. Energy poverty Energy poverty is a multidimensional social and economic phenomenon that reflects a household’s inability to access essential energy services necessary to maintain a basic and decent standard of living [64]. According to the definition provided by the Energy Efficiency Directive, energy poverty refers to the lack of access to fundamental energy services such as adequate heating, hot water, cooling, lighting, and electricity for appliances within the context of national standards, social policies, and living conditions [4]. This condition arises from a confluence of structural and socioeconomic factors. Chief among those are the unaffordability of energy, insufficient disposable household income, excessive energy expenditures relative to income, and the poor energy efficiency of residential buildings. When energy costs consume a disproportionately high share of a household’s income, it can significantly impair the ability to meet other basic needs such as food, healthcare, and education. Moreover, in an effort to minimize costs, affected Pàg. 22 Memòria households may reduce their energy consumption to levels that are detrimental to their physical and mental health, leading to adverse outcomes such as thermal discomfort, respiratory illnesses, and social exclusion [55]. This situation is aggravated by the impacts of climate change. Global warming leads to a disproportionate increase in energy consumption, thereby worsening the economic conditions of households already in vulnerable situations. As a result, residents experience greater discomfort due to the inability to maintain comfortable indoor temperatures. Energy poverty, therefore, is not only an issue of energy access, but also of affordability, equity, and housing quality [64]. Addressing it requires an integrated policy approach that encompasses energy efficiency improvements, income support mechanisms, and targeted social interventions to ensure that all individuals can enjoy a minimum standard of energy services necessary for health, well-being, and social participation. 3.2. Indicators In order to assess the resilience of buildings to the impacts of climate change, various indicators currently employed in other studies [70] [45]. These indicators provide valuable insights into a building’s ability to maintain indoor thermal comfort under increasingly extreme climatic conditions. 3.2.1. Resilience indicators One of the primary objectives of the Thermal Condition Task Force, established in April 2020 as part of EBC Annex 80, Resilient Cooling of Buildings [60], was to define a standardized benchmark that facilitates the global comparison of different cooling technologies. This framework not only accounts for energy efficiency but also incorporates occupant comfort in the evaluation of building resilience. It pursues two main goals [12]: 1. To define a standardized benchmark for comparing various cooling technologies worldwide. 2. To establish thermal conditions for assessing different cooling technologies. The framework proposes three distinct indicators to evaluate resilience: Indoor overheating degree (IOD) This indicator reflects the risk of overheating within the dwelling, considering all the distinct thermal zones and their specific comfort thresholds, which vary according to occupant behaviour in each space. As a result, it enables the evaluation of both the overall intensity and frequency of indoor overheating. The intensity of the overheating is calculated as the positive temperature difference between the indoor operative temperature and the defined comfort temperature limit, Assessment of climate resilience in vulnerable buildings in the city of Barcelona Pág. 23 established at 26ºC according to the UNE-EN-ISO 16798-1 standard [51]. The frequency is determined by integrating this temperature difference over the periods during which the zone is occupied. 𝐼𝑂𝐷 = ∑ ∑ [(𝑇𝑜𝑝,𝑖,𝑧−𝑇𝑐𝑜𝑚𝑓,𝑖,𝑧)+·𝑡𝑖,𝑧] 𝑁𝑜𝑐𝑐(𝑧) 𝑖=1 𝑧 𝑧=1 ∑ ∑ 𝑡𝑖,𝑧 𝑁𝑜𝑐𝑐(𝑧) 𝑖=1 𝑧 𝑧=1 (1) Where the variables represent: - 𝑖 = Index representing the occupied hours. - 𝑡 = The time step considered in the analysis. - 𝑁𝑜𝑐𝑐 = Index representing the occupied hours. - 𝑧 = The different thermal zones within the building. - 𝑇𝑜𝑝,𝑖,𝑧 = The operative temperature in zone z during the occupied hour i. - 𝑇𝑐𝑜𝑚𝑓,𝑖,𝑧 = The comfort temperature in zone z during the occupied hour i. As previously mentioned, this temperature is set to 26 °C during summer operational hours. Ambient warmness degree (AWD) This indicator quantifies the severity of outdoor air temperatures exceeding a defined reference temperature, Tb. The reference temperature should be selected based on the building type and local climatic conditions. In this study, it is set to 18 °C, which is considered the lower threshold for summer thermal comfort. Consequently, in any scenario where the outdoor air temperature exceeds 18 °C, the AWD indicator assumes values greater than zero [12]. 𝐴𝑊𝐷18º𝐶 = ∑[(𝑇𝑎,𝑖−𝑇𝑏)+·𝑡𝑖] 𝑁 𝑖=1 ∑𝑡𝑖 𝑁 𝑖=1 (2) Where the variables represent: - 𝑖 = Index representing the occupied hours. - 𝑁 = The number of occupied hours where 𝑇𝑎,𝑖 > 𝑇𝑏 - 𝑡 = The time step considered in the analysis. - 𝑇𝑎,𝑖 = The outdoor temperature during the occupied hour i. - 𝑇𝑏 = The lowest limit temperature for summer comfort. Overheating escalation factor (α) This indicator reflects the building’s sensitivity to overheating. The parameter α represents Pàg. 24 Memòria the slope of the regression line between the Indoor Overheating Degree (IOD) and the Air Warm Degree (AWD). It takes only positive values. A lower α value indicates greater resilience of the building to the impacts of climate change. 𝛼 = 𝐼𝑂𝐷 𝐴𝑊𝐷18º𝐶 (3) 3.2.2. Comfort indicators As this study aims to analyse the thermal resilience of buildings under climate change conditions, specifically their ability to maintain safe and comfortable indoor temperatures, additional comfort-related indicators will also be examined. Comfort indicators, whether based on direct or indirect temperature measurements, provide valuable insight into the effects of climate change on thermal conditions within a dwelling. These indicators assess the building’s capacity to withstand extreme weather events, such as heatwaves, by evaluating the thermal sensation experienced by occupants in various zones of the building. Thermal comfort is generally considered to be achieved when indoor temperatures range between 23°C and 27°C during summer, and between 20°C and 25°C in winter [80]. Following the methodology proposed by Tontodonati [70], the following indicators have been chosen for this study: Heat Index (HI) This index represents the temperature that the human body feels when air temperature is combined with relative humidity, this is the apparent temperature [78]. This HI values are calculated by a collection of equations that comprise a model yet, there is a reduced relationship between dry bulb temperatures at different humidity and the skin's resistance to heat and moisture transfer. The computation of HI is a refinement of a multiple regression analysis done by Lans P. Rothdusz [59]. The regression equation is as follows: 𝐻𝐼 = −42.379+ 2.04901523 · 𝑇 + 10.14333127·𝑅𝐻 − 0.22475541· 𝑇· 𝑅𝐻 −0.00683783·𝑇2−0.05481717·𝑅𝐻2· + 0.00122874· 𝑇2·𝑅𝐻 +0.00085282·𝑇·𝑅𝐻2−0.00000199·𝑇2·𝑅𝐻2 (4) Where the variables represent: Assessment of climate resilience in vulnerable buildings in the city of Barcelona Pág. 25 - 𝑇 = Air temperature in degrees Fahrenheit [°F] - 𝑅𝐻 = Relative humidity in percentage [%] However, this index has a limitation: when the calculated value is below 80°F, the Rothfusz regression is not considered appropriate. In such cases, a simplified formula is applied to ensure consistent and reliable values [46]: 𝐻𝐼 = 0.5·( 𝑇+ 61.0+[ {𝑇−68.0}· 1.2 ]+[𝑅𝐻·0.094] ) (5) If the value is even lower, below 26.66°F, the Rothfusz regression becomes unsuitable. In such cases, a simplified formula is employed to ensure consistent and accurate results [46]: 𝐻𝐼 = 1.1∙𝑇+0.0261∙𝑅𝐻−3.94 (6) This index classifies heat stress into four primary danger levels, based on the chart provided by the U.S. National Weather Service, as illustrated in Figure 1. Fig 1. Heat Index lookup table [50] Classification Heat index Effect on the body Safe conditions 26.7ºC or lower No effects Caution 26.7ºC – 32.2ºC Possible fatigue due to prolonged exposure or physical activity Extreme Caution 32.3ºC – 39.4ºC Prolonged exposure or physical activity may result in conditions such as heat stroke, heat cramps or heat exhaustion Pàg. 32 Memòria vulnerable households. Within the current socioeconomic context, not all households can bear the increased energy costs required to adequately climate their homes. Consequently, current research is focusing on passive strategies, those that do not consume energy to provide indoor thermal comfort [39]. Recent studies, such as that by Stasi et al. [67], explored the potential of natural ventilation as a strategy to address energy poverty, particularly in low-income settings. Nonetheless, to effectively tackle this challenge, it is essential to identify and locate households experiencing energy poverty, as this condition greatly limits access to adequate climatization technologies. Several studies have focused on the detailed analysis of many of the energy poverty indicators currently used by both the European Union and individual countries [65] [18]. These works compare various indicators to provide a comprehensive overview of existing methods for detecting energy poverty, ranging from objective and subjective indicators to Hidden Energy Poverty (HEP) indicators, as well as combinations with other resilience and comfort metrics. An example of this approach is the study conducted by Kez, Foley, Lowans, and Del Rio [6], in which the Thom’s Discomfort Index (defined in section 3.2.2) was also analysed and linked to energy poverty. At the national level, in April 2019, the Spanish Government approved the National Strategy against Energy Poverty 2019-2024, ENPE [1], which is a proposed a strategy that can be summarized in six points: 1. Official definition of energy poverty. 2. Proportion of the Spanish population experiencing energy poverty, according to the indicators established by the EU Energy Poverty Observatory. 3. Recommendation for an in-depth evaluation of household energy spending patterns in Spain. 4. Identification of the limitations of current social tariffs and suggestions to implement unified financial assistance covering all energy services. 5. Proposal for structural interventions in the short, medium, and long term, including energy efficiency upgrades in homes of vulnerable populations. 6. Initiatives aimed at increasing public awareness of energy poverty and enhancing households' access to relevant information. Apart from the proposal of the ENPE, several projects and studies have already been conducted in Spain aiming to develop local energy poverty indicators in order to identify and, where possible, prevent such situations. The COOLTORISE project, coordinated by researchers at the Universidad Politécnica de Madrid and funded by the European Commission through the Horizon 2020 program, aims to reduce cooling needs across Europe by raising awareness about summer energy poverty [55]. Its main objective is to lessen the incidence of summer energy poverty in European Assessment of climate resilience in vulnerable buildings in the city of Barcelona Pág. 33 households by improving indoor thermal conditions and lowering energy consumption during the hot season. To fulfil its mission, the project established six specific objectives to be addressed [55]: • Establish a common framework for understanding summer energy poverty. • Define effective solutions to mitigate summer energy poverty. • Train energy poverty agents to work directly with affected households. • Alleviate the situation of summer energy poverty for more than 7240 individuals. • Promote women's empowerment to help counteract the feminization of energy poverty. • Achieve widespread dissemination to raise awareness about the problem of summer energy poverty. The project started in Madrid in 2021 [73] and the intervention areas were selected based on an assessment of the urban heat island effect, focusing on zones where environmental vulnerability overlaps with significant social vulnerability. With the aim of addressing these challenges through gender empowerment, Cooltorise designed inclusive activities that facilitate work-life balance, such as flexible workshops and parallel childcare spaces. Additionally, the project implemented initiatives targeting families to reduce their energy needs and enhance thermal comfort during the summer. These included energy and heat literacy workshops promoting passive cooling strategies, such as the efficient use of natural ventilation and shading techniques, and sessions focused on understanding energy bills and improving energy consumption efficiency. In Seville, Alba-Rodríguez et al. [6] analysed 40 social housing units projected for the years 2050 and 2080, adopting a holistic approach that considers energy consumption, health, comfort, and monetary poverty. This study introduces an interesting indicator to determine energy poverty, the Vulnerable Household Index (IVH). Current indicators fail to account for future climate change scenarios, highlighting the need for a more holistic approach. A comprehensive analysis of energy management is essential to ensure minimum habitability standards in homes and the IVH indicator pretends to address this by combining social aspects, such as residents' health and household economic conditions, with energy efficiency factors and future projections. The existing indicator includes various social indices and parameters, scattered in four different parts. • Monetary Poverty Indicator (MPI): This component assesses the household's economic vulnerability by integrating region-specific indicators, specifically the Monetary Poverty Threshold (MPT) and the Severe Monetary Poverty Threshold (SMPT). Pàg. 34 Memòria • Energy Indicator (EnI): This indicator evaluates the required household energy consumption (EC) against a designated energy threshold established for the neighbourhood. The threshold is determined following the EN 16798-1:2019 standard [71]. • Comfort Indicator (CI): The thermal comfort model employed within the IVH framework examines the relationship between outdoor and indoor temperatures. If this relationship remains within the predefined comfort range, the occupants are assumed to be in a thermally comfortable state. The model calculates the percentage of hours during which the difference between indoor and outdoor temperatures exceeds the acceptable comfort range. A comfort threshold of 80% is applied, accounting for the assumption that the remaining 20% of the time corresponds to sleeping hours. The EN 16798-1:2019 standard [71] outlines four categories of acceptable indoor thermal conditions, depending on occupant expectations and the building's age. • Health-Related Quality of Life Cost (HRQLC): This component quantifies the health-related cost using the Quality-Adjusted Life Year (QALY) metric, which is linked to each level of vulnerability defined within the IVH, as illustrated in Figure 2. Figure 3 is a summary scheme of the methodology for calculating the implemented Index IVH obtained from Alba-Rodríguez et al. [6]. Fig 4. Methodology implied for calculating the IVH [6] Assessment of climate resilience in vulnerable buildings in the city of Barcelona Pág. 35 Sánchez-Guevara et al. [64] presented an index for identifying urban energy poverty at the municipal and district level, the High Energy Requirement Index (HER). This index is used to map the energy poverty issue through nine determining factors of energy poverty: Net income, gross disposable income per capita, average annual pension per capita, building year of construction, building maintenance, heating system availability, cooling system availability, electric heating system, dwelling surface by household member and Urban Heat Island (UHI). The level of the districts’ vulnerability towards energy poverty is assessed based on three different circumstances when analysing these determining factors [64]: - The number of variables analysed as determining factors of energy poverty: districts having the largest number of factors are worse than the city average. - The worst values: districts presenting the worst value for each factor. - Accumulation of several indicators: districts gathering critical values in several indicators, although they do not present the largest number of factors, nor are their indicator values the worst. A more specific explanation of this nine determining factors is conducted. Five of them are proxy indicators related to residential energy use: 1. Year of construction: % of buildings built before 1980, as those built before this year do not meet the first Spanish legislation related to insulation (NBE-CT79), and therefore exhibit a presumably deficient thermal behaviour. 2. Maintenance conditions: % of buildings with dilapidated, bad, or deficient maintenance conditions 3. Heating availability (differentiating those using electric heating systems as they use the most expensive energy): % of households in dwellings without any heating systems. Those who may use portable or electric heaters, which are less efficient and more expensive 4. Cooling availability (differentiating those using electric heating systems, as they use the most expensive energy): % of buildings that lack cooling systems. That may involve a higher expenditure and risk of thermal discomfort. 5. Welling surface by household member: the 𝑚2 surface area to be conditioned directly influences the house’s energy needs. A higher Surface means a higher impact on the energy expenditure. Three additional indicators reflecting the household’s income were added: 6. The net income: Average household disposable income calculated from the total members’ income after taxes and social security contributions. 7. The gross disposable income per capita: Average income per capita, excluding the transfer of capital, profits, and loss of actual possessions, and the consequence of natural disaster events. 8. The average annual income: Average annual income disaggregated by sex and member. Pàg. 36 Memòria This last factor is included to reflect the influence of the rising temperatures issue due to climate change on energy poverty detection: 9. The UHI intensity was also included because temperature differences at the suburban scale might significantly increase the number of cooling degree hours and thus, the vulnerability towards summer energy poverty. The Urban Heat Island (UHI) intensity is the difference between each district’s temperature and Madrid’s city average (15.5 ºC) [64]. The HER index is calculated as follows: At first, the numerical value of all nine determining factors is assigned to a degree of severity, calculated through a comparison of the factor at the district and city levels. The following values are obtained from the third Table in Sánchez-Guevara Sánchez et al. [64] study: • No severity – value = 0: Means that there is no difference between the district value and the city average. • Low degree of severity – value = 1: When the district value is slightly worse than the city level. • Medium degree of severity – value = 1.5: When the district value is worse than the city level. • High degree of severity – value = 2: When the district value is much worse than the city level. There is an exception to this rule due to the different nature of one of the indicators: the UHI intensity. The rule for this indicator is the following: • No severity – value = 0. When the UHI intensity is equal to or below 0ºC, meaning this district would have the same temperature as Madrid’s average. • Low degree of severity – value = 1. When the UHI intensity is between 0ºC and 2ºC. • Medium degree of severity – Value = 1.5. When the UHI intensity is between 2ºC and 4ºC. • High degree of severity – Value = 2. When the UHI intensity is higher than 4ºC. Secondly, the final HER value is calculated by attributing values to each determining factor. 𝐻𝐸𝑅 = 𝑉𝑎+𝑉𝑏+𝑉𝑐+𝑉𝑑+𝑉𝑒 (14) • Va is the value assigned to the indicator ‘Buildings built before 1980’; • Vb is the value assigned to the indicator ‘Buildings with dilapidated, bad or deficient maintenance conditions’. • Vc is obtained from the assigned values related to the indicators ‘Buildings with no heating systems’, ‘Buildings with no cooling systems’ and ‘Buildings with electric heating systems’: Assessment of climate resilience in vulnerable buildings in the city of Barcelona Pág. 37 𝑉𝑐 =( 𝑣1+𝑣2+𝑣3 ) 3 (15) o V1: value assigned to the indicator ‘Buildings with no heating systems’. o V2: value assigned to the indicator ‘Buildings with no cooling systems’. o V3: value assigned to the indicator ‘Buildings with electric heating systems’. • Vd is the value assigned to the indicator ‘Dwelling surface by household member’. • Ve is the value assigned to the indicator ‘Urban Heat Island intensity’. On a larger scale, Europe presented an interesting proposal to identify and eradicate energy poverty, serving as a foundation for future projects. In December 2016, the Energy Poverty Observatory (EPOV) was launched by the European Commission with the objective of monitoring and addressing energy poverty throughout the European Union member states [52]. The Observatory provides a range of resources, data, and insights related to energy poverty [52]: - National Indicators Dashboard: It offers the latest official data on energy poverty across European countries. - Publications Database: A collection of reports, studies, and case studies that analyse the best practices and policies aimed at combating energy poverty. - Local Indicators: A variety of local indicators is also provided to assist municipalities in creating informed local social climate plans, tracking impacts, and setting goals for effective interventions. These resources are designed to provide policymakers, researchers, and stakeholders with a comprehensive tool to understand and address energy poverty effectively across Europe. The EPOV currently has defined four primary and twenty-four secondary indicators that can be used, alone or in combination, to define energy poverty [11] [79] [47]. The primary indicators are: • 2M: Twice the National Median Indicators • M/2: Low absolute energy expenditure • Late payment of utility bills • Inability to keep the home adequately warm in winter In a study conducted by the ‘Cátedra de Energía y Pobreza’ [6], a closer look at the official EPOV indicators and others currently being used in other countries is taken, in addition to a classification of those as objective or subjective. Before analysing this study, it is important to introduce another concept used to classify energy poverty indicators: the HEP indicators. This is particularly relevant, as one of the objective indicators introduced later is specifically designed to identify hidden energy poverty. These indicators measure hidden energy poverty based on either relative or absolute energy expenditure thresholds. Relative indicators compare a household’s energy Pàg. 38 Memòria spending to the median or average expenditures of similar households within the same country. In contrast, absolute indicators identify households as energy poor if their actual energy expenditures fall below their required or modelled energy needs [13]. Objective indicators are based on quantifiable household data, such as income and energy expenditure. These are further divided based on the form of energy poverty they assess whether they measure disproportionate energy expenditure or insufficient energy expenditure. - The disproportionate energy expenditure is a household experiencing energy poverty due to overspending: • 10% Indicator: This early metric identifies a household as energy poor if it spends more than 10% of its income on energy bills [65]. While it was a pioneering approach, it has not been adopted by the EPOV due to its reliance on a fixed threshold. By setting energy poverty at a strict 10% of income, the measure risks misclassifying high-income households that spend heavily on energy as energy poor [6]. • 2M Indicator: A household is considered energy poor if it spends more than twice the national median on energy. Unlike the 10% indicator, this one uses a relative and dynamic threshold. It is officially recognized by the EPOV and included in ENPE. • LIHC (Low Income, High Cost): Proposed in the UK to address the shortcomings of the 10% indicator, this measure identifies energy poverty when a household's income (after energy costs) is below 60% of the national median, and its energy expenditure is above the national median. While influential in the UK, it has not been officially adopted by the EPOV. • LILEE (Low Income, Low Energy Efficiency): This is the current official metric in England. A household is fuel poor if it has low energy efficiency (rated D or lower) though robust in design, it is not part of the EPOV’s selected indicators as it doesn’t assess energy poverty but assesses fuel poverty [40]. • MIS (Minimum Income Standard): This indicator considers a household to be energy poor if high energy costs force it to forgo other basic needs. It offers a promising accurate measure of energy poverty it addresses the problem from its very economic root: the income available for energy needs after the basic needs have been met. Unfortunately, it also presents a technical difficulty: the determination of the minimum income on an objective basis [18]. - The insufficient energy expenditure is a household experiencing energy poverty due to not meeting basic needs: • M/2 Indicator: A household is considered energy poor if it spends less than half of the national median on energy. It has been officially recognized by both the EPOV and the ENPE, considered as a relative HEP indicator, but is criticized for ignoring income levels, which may lead to misclassification of non-vulnerable households. • HEP (Hidden Energy Poverty) developed by “Cátedra de Energía y Pobreza de la Universidad Pontificia Comillas”: This HEP indicator improves upon M/2 by using a theoretical energy expenditure model and applying an Assessment of climate resilience in vulnerable buildings in the city of Barcelona Pág. 39 income filter to exclude higher-income households. While more precise, it is not officially recognized by the EPOV. Subjective indicators rely on households’ self-reported experiences, typically collected through surveys. They capture less visible aspects of energy poverty, such as discomfort or economic stress, not reflected in expenditure data. • Late payment of utility bills: Based on whether a household reports being late on basic utility bill payments in the past 12 months. This is a subjective indicator officially recognized by the EPOV. • Inability to keep home adequately warm in winter: This indicator is based on whether the household reports being unable to maintain a comfortable indoor temperature during the winter months. The EPOV also recognizes this indicator. In 2023, the measurement was expanded with additional questions about thermal comfort in winter and summer. To sum up this study [6], Table 3 is presented to get a graphical visualization of which indicators are official and which kind of indicators are: Indicator Official EPOV Objective Disproportioned 10% X 2M ✓ LIHC X LILEE X MIS X Insufficient M/2 ✓ HEP X Subjective Late payment of utility bills ✓ Inability to keep the home adequately warm in winter ✓ Table 4. Classification of energy poverty indicators according to their type and official state in the EPOV [6] Traditionally, most studies on energy poverty focused on the heating consumption during winter. Pàg. 40 Memòria Hernandez-Cruz et al. [32] proposed an innovative strategy to guarantee a minimum indoor temperature in social housing, an essential aspect in contexts where winter conditions can pose serious health risks to vulnerable individuals. Realini et al. [11] introduced in their paper on the problem of Hot Homes the "EPi" indicator. This indicator offered an approach in the sense of “Low Income High Costs” (LIHC) indicators, where the ratio between energy expenses and household income (or, in the Italian case, the average monthly expenses of the household, since the few statistics about income cannot be related to energy expenditures) is compared to a statistically determined threshold to assess whether a subject is in an energy poverty situation or not. However, with the ongoing increase in global temperatures, cooling needs are beginning to take on a central role. In Italy, numerous studies have been conducted. As previously mentioned, the study by Maracchini et al. [45] showed a drastic increase in energy consumption during heatwaves. Similarly, Vurro et al. [75] analysed the impact of climate change on energy consumption in public housing in Bari, identifying a correlation between the occupants' age and energy consumption, and highlighting specific vulnerability patterns. Around the world, several studies have also been carried out, particularly in South American and African countries. Those studies usually analyse a building or a set of dwellings in economically vulnerable neighbourhoods with warm climates. In Ecuador, Gutiérrez et al. [30] employed energy simulations to assess the thermal behaviour of a social housing from the Ecuadorian program “Casa para Todos” across four cities up to the year 2050, aiming to reduce energy poverty. In Montevideo, the study by Pereira-Ruchansky and Pérez-Fargallo [54] emphasized the importance of passive design in improving thermal comfort in social housing. In Peru, Gutierrez and de Angelis [31] compared low-cost passive strategies in rural housing to combat energy poverty, proposing interventions adapted to similar climates. After all this research, one key observation is the absence of an indicator capable of identifying or classifying a dwelling affected by energy poverty based solely on building characteristics, such as its structure, materials, or internal loads. In Spain, the only tool that partially addresses this is the building energy certification system, regulated by the Código Técnico de la Edificación (CTE). This certification assigns an energy label to buildings ranging from A (most efficient) to G (least efficient). However, this certification assesses only the theoretical energy demand and efficiency of a building, without accounting for actual energy consumption, household behaviour, or socioeconomic conditions. While it can serve as a useful technical reference, particularly when combined with household income and expenditure data, it is insufficient as a standalone indicator of energy poverty. It becomes meaningful in composite indicators such as LILEE, which aim to identify energy-poor households by correlating energy performance with income data. Assessment of climate resilience in vulnerable buildings in the city of Barcelona Pág. 41 Although the energy certification can highlight dwellings with high vulnerability potential, such as those rated E, F, or G, it cannot independently determine energy poverty. A dwelling with a high efficiency rating (A, B, or C) may still house a family that cannot afford to maintain adequate thermal comfort due to low income. Conversely, a poorly rated building may be occupied by individuals who can afford energy costs and thus are not in a state of energy poverty. Pàg. 48 Memòria Fig 3. Surrounding buildings of the building studied, CM This orientation poses potential thermal comfort challenges. Southwest-facing façades are particularly vulnerable to excessive solar gains during late summer afternoons—the hottest period of the day. As a result, interior spaces facing this direction are prone to overheating, thereby increasing the cooling demand. Additionally, if windows on this façade are unshaded or poorly insulated, the problem is further exacerbated, contributing to thermal discomfort and higher energy use. The geometry of the building analysed in this study is based on the typological classification of three primary typologies previously established by Ravetllat et al. [57]: • Linear blocks with ground floor +5 or +6 floors: These are the most common, comprising approximately 85% of the buildings, although they account for just over 60% of the total housing units. These buildings exhibit minimal variation in floor plans, typically featuring two dwellings per landing. Variations are limited to the width and configuration of laundry spaces, and the presence or absence of balconies. • Tower blocks with ground floor +10 or +6 floors: Excluding three unique tower typologies, each represented by a single building, this type constitutes nearly 10% of the buildings and accommodates more than 24% of the housing units. • Double linear H-blocks with ground floor +8 floors: Although this typology represents only 4.8% of the buildings, it includes more than 10% of the dwellings in the neighborhood. Assessment of climate resilience in vulnerable buildings in the city of Barcelona Pág. 49 Fig 4. Linear block building structure (left structure) and double linear H-block building structure (right structure) [57] Based on the previously established classification, the building selected for simulation in this study corresponds to the first typology, due to its widespread presence and its representative nature of typical low-income households in the area. This typology refers to linear blocks constructed around 1960, during a period when no formal technical building code was in place. The most common configuration is a six-storey building plus ground floor, without an elevator. The chosen block forms part of a series of four consecutive buildings, all sharing the same structural design. Each floor, including the ground level, contains two apartments facing each other, resulting in a purely residential structure without any commercial units. The building exhibits bilateral symmetry along both the xand y-axes, indicating that the two apartments per floor are structurally identical. Each unit has an approximate area of 50 m² and includes three double bedrooms, a kitchen, a bathroom, and a restroom. Pàg. 50 Memòria Fig 5. View of the south and west façade of the building model in Open Studio The row of blocks is separated by approximately 18 meters from other buildings on the same street. The building is modelled individually in the software for simulation purposes, representing just one of the four identical blocks. However, additional constraints are introduced to replicate a more realistic urban setting. The model situates the building within 10-meter-wide streets, flanked by buildings of the same typology on both sides as shown in Figure 6. Furthermore, the terrain is modelled with a 15-meter elevation gain along the main (south-facing) façade and a 15-meter descent on the rear (north-facing) façade, accurately reflecting the topography of Ciutat Meridiana. To assess the impact of rising temperatures and the performance of various cooling strategies, the building is subdivided into 18 thermal zones. These include one entrance zone, five stairwell zones, and twelve apartments, with each apartment treated as an individual thermal zone for the sake of simplification. The zones are grouped into two functional categories: apartments and stairs (the entrance is included in the stairs space type) Assessment of climate resilience in vulnerable buildings in the city of Barcelona Pág. 51 Fig 6. View of the different thermal zones in the building model in Open Studio Each zone has distinct dimensions, materials, load characteristics, and schedules. These details are outlined in the following Tables 5 and 6. Floor Area [𝒎2] Volume [𝒎3 ] Unit Floor Total Unit Floor Total Apartments 50.53 101.06 606.34 151.58 303,16 1818,96 Stairs 6.63 6.63 39.78 19.89 19.89 119.34 Total - 107.69 646.12 503,05 1938,3 Table 5. Floor area and space volume Constructive element’s surface [𝒎2 ] Total South West East North Wall Area 670.32 270.72 128.88 0 270.72 Window Area 144.25 70.33 13.8 0 60.12 Roof Area 107.69 - - - - Table 6. Window and roof area Pàg. 52 Memòria The characteristics of the construction elements have been derived from a previous study on common building typologies in Catalonia, using local regulations as a reference for selecting values related to thermal resistance, thermal transmittance, and thermal conductivity, as well as the density and thickness of the constituent materials [8] [11]. Thermal conductivity values from the classification above have been compared with those provided by the Cype program [24], a structural design and calculation software used in civil engineering and architecture to perform structural designs, electrical installations and energy certificates. The values provided by the Cype software are based on the UNE EN ISO 6946:2012 standard [35], a more recent and comprehensive document regarding the calculation of thermal resistance for various materials and construction elements. In several cases, the original values were adjusted to align with those used by Cype. The structure and materials of the construction elements adhere to the guidelines defined for typologies F/G in the previously mentioned study [8]. This choice is based on the findings of Ravetllat et al. [57], which stated that the most common residential buildings in the neighborhood were constructed between 1950 and 1960, typically consisting of six floors with two apartments per floor. This description corresponds with typology F/G as outlined in Annex 2 of the study [8]. A summary of the thermal conductivity and density for each constructive element is provided in Table 7. Assessment of climate resilience in vulnerable buildings in the city of Barcelona Pág. 53 Constructive element Material Thickness Density Thermal conductivity Thermal Resistance Thermal transmittance Units [m] [𝑘𝑔 𝑚3 ⁄] [𝑊𝑚 ·𝐾 ⁄] [𝑚2·𝐾 𝑊 ⁄] [𝑊𝑚2·𝐾 ⁄] Exterior walls Lime mortar 0.02 1125 1.3 0.01538 1.7809 Ceramic brick 0.14 825 0.7 0.2714 Air chamber 0.1 - - 0.19 Ceramic brick 0.05 825 0.7 0.0714 Gypsum mortar 0.02 1600 1.5 0.01333 Wall in between buildings Cement mortar 0.02 1800 1.7 0.01176 3.37 Ceramic brick 0.14 825 0.7 0.2714 Gypsum mortar 0.02 1600 1.5 0.01333 Internal walls Gypsum mortar 0.02 1600 1.5 0.01333 4.4119 Ceramic brick 0.05 825 0.7 0.2 Gypsum mortar 0.02 1600 1.5 0.01333 Interior floors Gypsum mortar 0.02 1600 1.5 0.01333 2,407 Hollow slab 0.3 1240 0.8 0.375 Cement mortar 0.02 1800 1.7 0.01176 Interior flooring 0.02 2300 1.3 0.01538 Pàg. 54 Memòria Floor in contact with the ground Interior flooring 0.02 2300 1.3 0.01538 3.96 Cement mortar 0.02 1800 1.7 0.01176 Concrete 0.2 2243 1.7296 0.225 Windows Simple window glass 0.004 2500 0.016 0.25 4.1 Exterior door Metal structure 0.0008 7824 50 0.000016 62.5 Ceilings Interior flooring 0.02 2300 1.3 0.01538 2.407 Cement mortar 0.02 1800 1.7 0.01176 Hollow slab 0.3 1240 0.8 0.375 Gypsum mortar 0.02 1600 1.5 0.01333 Roof Exterior flooring 0.03 2300 1.7 0.01764 2.225 Ceramic chain 0.05 1800 1.15 0.04347 Hollow slab 0.3 1240 0.8 0.375 Gypsum mortar 0.02 1600 1.5 0.01333 Table 7. Table of parameters for each constructive element. 4.2.3. Loads and schedules Schedules and internal loads can be defined in OpenStudio for various thermal zones and space types. In this thesis, these parameters have been primarily established by several standards and guidelines commonly applied in previous studies on resilient cooling in buildings, as well as those issued by Spain’s Standardization Organization (UNE) [70] [71] Assessment of climate resilience in vulnerable buildings in the city of Barcelona Pág. 55 [72] [10]. However, certain parameters have been adapted to reflect conditions associated with energy poverty in residential buildings, based on findings from various studies addressing thermal comfort in residential conditions like this type of housing. Occupancy values from previous studies [70] [46] [51] [48] were generally set at 3 persons per apartment and 1 person in the stairwell, based on UNE EN 16798-1:2020 [71], which regulates occupancy at 28.3 m²/person for residential spaces and 17.0 m²/person for common areas such as stairs. However, based on the findings of Ravetllat et al. [37] [57],occupancy levels in the Ciutat Meridiana neighborhood are typically higher due to the socioeconomic conditions of its residents. It is common to find families of 4 to 5 members living in each apartment, and not uncommon to find groups of 5 to 6 unrelated individuals sharing a unit. The level of physical activity assumed for occupants in residential spaces is low to medium, corresponding to a metabolic rate of approximately 100–150 W/person. For common circulation areas such as entrances and stairwells, higher activity levels are assumed, with a rate of 180 W/person. These values are based on UNE-EN ISO 7730:2006 and UNE-EN ISO 8996:2021 [72] [10]. . Fig 10. Occupancy schedules for apartments and stairs 0 0,2 0,4 0,6 0,8 1 1,2 012345678910 11 12 13 14 15 16 17 18 19 20 21 22 23 Occupancy schedules Occupancy Apartments Occupancy Stairs Pàg. 56 Memòria Fig 11. Activity schedules for apartments and stairs Infiltration rates and internal electrical loads are established according to UNE EN 167981:2020 [71] [16]. Infiltration is set at 0.5 L/s·m² for residential units. Electrical equipment loads are defined as 3 W/m² for apartments and 1 W/m² for entrances and stairwells. Regarding lighting, the interior lighting load is limited to 1 W/m² across all spaces (apartments, entrances, and stairwells) in compliance with the Spanish Building Code DB HE 2019 [42]. Fig 12. Lights schedules for apartments and stairs 0 20 40 60 80 100 120 140 160 180 200 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Activity schedules Activity Apartments Stairs Apartments 0 0,05 0,1 0,15 0,2 0,25 0,3 0,35 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Light schedules Lights Apartments Lights Stairs Assessment of climate resilience in vulnerable buildings in the city of Barcelona Pág. 57 Fig 13. Electrical equipment schedules for apartments and stairs Schedules are introduced in the software as amount per unit or element. For example, when occupancy is set to 0.5, it means that half of the people set to belong to a thermal zone will be there at that moment. The load of apartments is 3 inhabitants per each, meaning that when the schedule is set to 1, 3 people will be in that apartment. Cooling and heating schedules, along with their corresponding set point temperatures, are incorporated into the model. In previous studies [70] [46] [51] [48], these values were typically set at 26 °C for cooling during the main hours of activity in apartments, increasing to 28 °C at night to reduce energy demand. For heating, set points were fixed at 20 °C all day long, following the recommendations outlined in UNE-EN 16798-1:2020 [71]. In contrast, this study adopts a different approach based on the findings of Reallini et al. [11], which defined “minimum comfort” conditions in the context of energy poverty. According to this research, indoor temperatures should be maintained at a minimum of 18 °C during the day and 16 °C at night for heating, while cooling set points should not be lower than 28 °C during the day and may rise to 30 °C at night. Entrance halls and stairwells are not thermally conditioned in this study, as these areas are considered to have minimal impact on occupant comfort due to the limited time spent in them. Nevertheless, it is necessary to assign set point values in the simulation model. To ensure that no active heating or cooling systems are triggered in these zones, extreme set points of 60 °C for cooling and 1 °C for heating have been assigned. These values effectively exclude these spaces from conditioning in the model. The complete set point schedule values are summarized in Table 8. 0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Electric equipment schedules Activity Apartments Stairs Apartments Pàg. 64 Memòria • Indoor Overheating Degree (IOD) • Ambient Warmness Degree (AWD) • Overheating Escalation Factor (α) These metrics have been applied in previous research and are thus well-suited for this study, as they enable comparative analysis with existing literature if required. For the evaluation of thermal comfort, the following indicators are calculated for most cases: • Heat Index (HI) • Discomfort Index (DI) • Annual Hours of Exceedance (HHE or CHE) These indices provide a more comprehensive understanding of indoor environmental quality and enhance the validity and robustness of the results. No energy poverty indicator was studied in this work, as most existing indicators rely heavily on detailed economic data, and the analysis of such economic data was beyond the scope of this study. Assessment of climate resilience in vulnerable buildings in the city of Barcelona Pág. 65 5. Results This evaluation follows the same methodology proposed in prior studies [70] [68]. Before evaluating the impact of resilient cooling technologies on the modelled building, it is necessary to assess its initial thermal performance, which will serve as the baseline for upcoming comparisons. Afterwards, each cooling technology will be individually analysed to determine how it affects the building’s resilience compared to the base case. Additionally, a scenario combining all cooling technologies is simulated and examined with the same objective: to evaluate improvements in resilience. Finally, the results obtained from this building are compared to those of a separate study [43] that assessed the resilience of the most representative dwelling type in Catalonia, also modelled under Barcelona's climate conditions. 5.1. Resilience analysis base case This section presents the analysis of the base case (OG), which refers to the building without any implemented cooling technologies. The evaluation is based on the graphical results obtained from resilience and thermal comfort indicators. The study is conducted for three different periods: present (P), mid-future (MF) and long future (LF). For each of these timeframes, two meteorological conditions are examined: a Typical Meteorological Year (TMY) and extreme heat events, commonly referred to as Heatwaves (HW). The calculations performed by EnergyPlus allow us to determine the values of the indicators for the entire building and each of its thermal zones. All graphs included in this section have been generated using the Python scripts developed by Baucells et al. [71]. 5.1.1. Resilience analysis The first analysis focuses on evaluating the resilience of the base case (OG). The initial step involves studying the general parameter α. Fig 16. Alpha (OG) in TMY Long-future, Mid-Future and Present Pàg. 66 Memòria Fig 17. Alpha (OG) in the different HW (from Long-future to Present (left to right)) The parameter α represents the building's resistance to increasing outdoor temperatures. A lower α value signifies greater resilience to the impacts of climate change. If the risk of indoor overheating remains low despite high outdoor air temperatures, the alpha value will remain low. As observed in Figures 16 and 17, the average α across the building during both the Typical Meteorological Year (TMY) and heatwave (HW) scenarios never exceeds a value of 1. An alpha value above 1 indicates that the building is unable to effectively mitigate the effects of climate change. In such cases, indoor conditions may become comparable to or worse than outdoor conditions in terms of health risk. In this study, the building consistently maintains α values below 1, suggesting it is generally capable of counteracting the adverse effects of climate change on indoor temperatures. However, α never falls below 0.73, implying that the building's ability to protect its occupants from climate-related thermal stress is moderate, rather than optimal. Let’s take a closer look now to the alpha parameter across individual interior spaces, Figures18 and 19, to identify the thermal zone with the poorest performance. Assessment of climate resilience in vulnerable buildings in the city of Barcelona Pág. 67 Fig 18. Alpha for each thermal zone (OG) in TMY Long-future, Mid-Future and Present Fig 19. Alpha for each thermal zone (OG) in the different HW (from Long-future to Present (left to right)) From a more accurate perspective, where all the thermal zones are studied individually, the alpha distribution map reveals that in certain zones, alpha exceeds 1. This phenomenon is primarily observed during present-day heatwaves (HW_P) and under future climate scenarios projected (TMY_LF). A preliminary review of the Figures 18 and 19 indicates that Apartment 6 L demonstrates the poorest thermal performance, with α values ranging from 0.89 to 1.11 throughout the year. Conversely, Apartment 1R exhibits the best thermal Pàg. 68 Memòria performance, with alpha values between 0.0844 and 0.1784. During the most prolonged, intense, and severe current heatwaves, alpha values are notably high in apartments located on the 4th and 5th floors, reaching values up to 1.03. To better interpret the obtained alpha values, we examine the two indicators used in its calculation: IOD and AWD. Fig 20. AWD (OG) in TMY Long-future, Mid-Future and Present Fig 21. AWD (OG) in the different HW (from Long-future to Present (left to right)) Assessment of climate resilience in vulnerable buildings in the city of Barcelona Pág. 69 Fig 22. IOD (OG) for each thermal space in TMY Long-future, Mid-Future and Present Fig 23. IOD (OG) for each thermal space in the different HW (from Long-future to Present (left to right)) The IOD indicator is particularly high in apartments from the second floor upwards, as seen in Figures 22 and 23, with the maximum value recorded at 12.95 during the longest and most severe heatwave in the long-term future (HM_LF_LMS). Additionally, the IOD values in Figure 23 shows that the stairwells on the 4th to 6th floors experience the highest values. This can be attributed to their southern orientation and the presence of fixed windows on the south-facing surfaces. Since these windows cannot be opened, air exchange is minimal, resulting in an elevated risk of indoor overheating in these areas. Pàg. 70 Memòria Figures 20 and 21 display the AWD value, and as expected, the Ambient Warmness in the TMY_LF increases by 69.47% compared to its initial value in TMY_P, rising from 7.47 to 12.66. 5.1.2. Comfort analysis The Heat Index (HI) is the first comfort indicator evaluated. It represents the apparent temperature felt by the human body when air temperature is combined with relative humidity [77]. This index is widely used to assess the level of risk associated with exposure to actual ambient temperatures [29]. Fig 24. HI (OG) in TMY Long-future, Mid-Future and Present Fig 25. HI (OG) in the different HW (from Long-future to Present (left to right)) Assessment of climate resilience in vulnerable buildings in the city of Barcelona Pág. 71 The results of Figures 24 and 25 indicate that, over time, the proportion of occupied hours categorized under the Caution level will significantly decline in the long future. Specifically, this level accounts for only 17.2% of occupied hours in the present (P) and 19.02% in the mid-future (MF). In the long future (LF), nearly half of all occupied hours will fall under the Extreme Caution category, with 0.32% reaching the Extreme Danger level. Conditions worsen considerably during heatwaves: in the most prolonged and intense present-day events, a maximum of 14.88% of occupied hours fall within the Caution level, while the remaining hours fall within the Extreme Caution category. In long-future heatwave scenarios, between 79.17% and 91.25% of occupied hours are classified under the Danger level, with 1.39% reaching Extreme Danger. As these HI values represent averages across the entire building, and considering the heterogeneity in thermal performance between different spaces, we now compare Apartments 1R and 6L, Figures 26 and 27, which have demonstrated the best and worst thermal performance, respectively. Fig 26 and 27. HI (OG) values for every TMY period for Apartment 1 R and 6 L Under the Typical Meteorological Year scenario, notable differences emerge between ground-floor apartments and those on higher levels. For instance, residents of first-floor units adjacent to neighbouring buildings benefit from better thermal insulation and protection. In contrast, units such as Apartment 6L (Figure 27), located on the top floor with direct west-facing solar exposure, experience far more intense heat conditions. On the first floor, safe HI conditions can still be observed even in long-future scenarios (Figure 28); however, this is not the case on the sixth floor (Figure 29). Pàg. 72 Memòria Fig 28. HI (OG) values for every HW (from Long-future to Present (left to right)) for Apartment 1 R Fig 29. HI (OG) values for every HW (from Long-future to Present (left to right)) for Apartment 6 L The second indicator examined is the Discomfort Index (DI), which estimates the percentage of occupied hours during which residents experience thermal discomfort due to heat. Assessment of climate resilience in vulnerable buildings in the city of Barcelona Pág. 73 Fig 30. DI (OG) in TMY Long-future, Mid-Future and Present Fig 31. DI (OG) in the different HW (from Long-future to Present (left to right)) Analysis of this index (Figures 30 and 31) reveals that, even in the present, significant thermal discomfort is already occurring, with severe heat conditions affecting 3.31% of occupied hours, as seen in Figure 30. This DI value increases sharply to 59.19% in the long-future scenario. During heatwaves in long-future conditions (Figure 31), severe discomfort is expected during 87.5% to 96.63% of all occupied hours. Pàg. 80 Memòria between Apartments 1 R and 6 L. In the present (P), the warmest day is August 12, with an outdoor temperature of 35.5ºC at 16:00. Inside Apartment 1R (Figure 40), the temperature on that same day is the highest of the year, 27.3ºC. In contrast, inside Apartment 6L (Figure 41), the temperature on that same day is 34.24ºC, though it is not the warmest day; that occurs on June 25, with a peak of 36ºC. In the mid-future (MF), the warmest day is July 25, with an outdoor temperature of 34.9ºC at 15:00. Inside Apartment 1R (Figure 42), the temperature on that day is again the highest of the year, 28.3ºC. Meanwhile, in Apartment 6L, the temperature is 34.24ºC, but this is not the hottest day either; that again falls on June 25, reaching 36ºC (Figure 43) In the long-future (LF), the warmest day is July 24, with an outdoor temperature of 43.6ºC at 15:00. This is also the warmest indoor day of the year in both apartments. In Apartment 1R (Figure 44), the indoor temperature reaches 29.96ºC. In contrast, Apartment 6L records 39.91ºC on the same day, although its highest temperature occurs on August 10, reaching 40.1ºC (Figure 45). 5.2. Resilience analysis with cooling technologies This section focuses on how the selected cooling technologies influence the building’s resilience. Each cooling strategy will be analysed separately, followed by their combined effect, and finally, a comparison will be made with the base case results. Comparing all simulated technologies is essential to identify the most effective solutions in response to climate variation. For clarity, the analysis is divided into two parts: Typical Meteorological Year (TMY) and Heatwaves (HW). The first step is to assess the energy use of the base case (OG) versus the building equipped with additional cooling technologies: Natural Ventilation (NV), Blinds (BL), and their combination (NV_BL). It’s important to note that all cooling technologies analysed in this study are passive strategies, meaning they do not result in any additional energy consumption. 5.2.1. TMY Analysis Studying the effect of cooling technologies during the most typical meteorological years for the present, mid-future, and long-future offers insight into the building’s response to rising temperatures due to climate change. 5.2.1.1. Resilience study To begin the analysis, we observe how the alpha indicator responds to each cooling Assessment of climate resilience in vulnerable buildings in the city of Barcelona Pág. 81 strategy and its combinations. We will examine results for three cooling strategies: Natural Ventilation (NV), Blinds (BL) and Natural Ventilation combined with Blinds (NV_BL). Fig 46. Alpha with cooling technologies during TMY From Figure 46, we see that for each cooling strategy, the alpha value increases over time, and a predictable outcome given the strong impact of climate change in the long-future scenario. Among the strategies, natural ventilation alone provides the greatest improvement in Alpha, although not consistently across all time periods. In the present, with lower outdoor temperatures, natural ventilation works well to refresh indoor air and maintain thermal comfort. However, as outdoor temperatures rise in the future, its effectiveness significantly diminishes, causing the alpha value to more than double, from 0.29 to 0.7. Overall, the best performance is observed when combining both technologies (NV_BL), though even this combination sees a substantial drop in effectiveness in the long future due to extreme heat. 5.2.1.2. Comfort study The HI and DI indicators provide insight into the thermal comfort experienced by residents. The values presented represent the average per apartment across the building. 0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 OG NV BL NV_BL Alpha (α) per cooling technology/TMY periods TMY_P TMY_MF TMY_LF Pàg. 82 Memòria Fig 47. HI with cooling technologies during TMY For the HI in Figure 47, danger and extreme danger levels are absent in the present. However, these percentages increase significantly over time. The best HI values are obtained with the combination of natural ventilation and blinds, while using blinds alone produces the least favourable results. 0 10 20 30 40 50 60 70 80 90 Safe conditions Caution Extrem caution Danger Extreme danger Safe conditions Caution Extrem caution Danger Extreme danger Safe conditions Caution Extrem caution Danger Extreme danger TMY_P TMY_MF TMY_LF DI per cooling technology/TMY periods in the building OG NV BL NV_BL Assessment of climate resilience in vulnerable buildings in the city of Barcelona Pág. 83 Fig 48. DI with cooling technologies during TMY The DI results in Figure 48 reflects the perceived discomfort due to heat and humidity, expressed as a percentage of occupied hours. In the long future, extreme conditions of severe heat are recorded, leading to highly unfavourable DI values. Even with the implementation of natural ventilation or its combination with blinds, the results remain inadequate. A stifling and uncomfortable indoor environment is anticipated under future climate conditions. 0 10 20 30 40 50 60 70 80 Not heat Mild heat Heavy heat Severe heat Not heat Mild heat Heavy heat Severe heat Not heat Mild heat Heavy heat Severe heat TMY_P TMY_MF TMY_LF DI per each cooling technology/TMY periods OG NV BL NV_BL Pàg. 84 Memòria Fig 49. HHE with cooling technologies during TMY_P Fig 50. HHE with cooling technologies during TMY_MF 0 100 200 300 400 500 600 700 800 900 1000 Apart 1 L Apart 1 R Apart 2 L Apart 2 R Apart 3 L Apart 3 R Apart 4 L Apart 4 R Apart 5 L Apart 5 R Apart 6 L Apart 6 R HHE indicator during TMY_P OG NV BL NV_BL 0 100 200 300 400 500 600 Apart 1 L Apart 1 R Apart 2 L Apart 2 R Apart 3 L Apart 3 R Apart 4 L Apart 4 R Apart 5 L Apart 5 R Apart 6 L Apart 6 R HHE indicator during TMY_MF OG NV BL NV_BL Assessment of climate resilience in vulnerable buildings in the city of Barcelona Pág. 85 Fig 51. HHE with cooling technologies during TMY_LF The final comfort indicator HHE, measures how many hours per year indoor temperatures surpass comfortable limits per each apartment. In the present (Figure 49), for apartment spaces, the most efficient strategy is NV + BL, while using only blinds is the least effective, causing a peak of around 932 hours of hot exceedance in most of the apartments above the second floor. In the long future (Figure 51) we can see how not even the apartment with best thermal performance, 1 R, have a HHE level lower than 400 hours, approximately 50 hours more than those allowed by this same indicator in the CTE. A notable observation is the declining performance of the NV + BL combination in both the midand long-future scenarios, where the HHE values of all strategies converge and become similar. Based on this study of the TMY, the optimal cooling strategy is the combination of Natural Ventilation and Blinds (NV + BL), despite its diminishing effectiveness over time. 5.2.2. HW Analysis Heat waves represent the periods when a building’s resilience is most critically tested. However, the HW data refers to the entire year in which the heat wave occurred, spanning from January 1st to December 31st. In this section, we analyse seven different heatwaves, selected as the longest, most intense, and most severe across the three time periods studied. Three are from the present, two from the mid-future, and two from the long future. Each period is analysed separately for clarity and better understanding. 0 100 200 300 400 500 600 700 800 Apart 1 L Apart 1 R Apart 2 L Apart 2 R Apart 3 L Apart 3 R Apart 4 L Apart 4 R Apart 5 L Apart 5 R Apart 6 L Apart 6 R HHE indicator during TMY_LF OG NV BL NV_BL Pàg. 86 Memòria 5.2.2.1. Resilience study To assess the building’s resilience, the first parameter considered is the alpha. Fig 52. Alpha with cooling technologies during Present HW (P) Fig 53. Alpha with cooling technologies during Mid-future HW (MF) 0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 OG NV BL NV_BL Alpha (α) per each cooling technology/ HW present HW_P_MS HW_P_MI HW_P_L 0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 OG NV BL NV_BL Alpha (α) per each cooling technology/ HW mid future HW_MF_MIS HW_MF_L Assessment of climate resilience in vulnerable buildings in the city of Barcelona Pág. 87 Fig 54. Alpha with cooling technologies during Long-future HW (LF) As previously observed in Figure 46, in the TMY results, α values increase over time. The most severe heatwaves tend to yield the highest alpha values, particularly in the present (Figure 52) and mid-future (Figure 53). However, in the long future (Figure 54), the worst results correspond to the longest heatwave rather than the most intense. Even so, natural ventilation combined with blinds remains the most effective passive strategy for mitigating rising temperatures, though its effectiveness significantly drops in the long term. In that scenario, blinds alone appear to be the only cost-effective passive solution for reducing Alpha and protecting apartments from solar radiation. Importantly, Alpha never exceeds 1 in any period, which is a positive sign, indicating that while mitigation is weak, it is still functioning to some extent. 5.2.2.2. Comfort study The following steps of the analysis are to observe the results from the HI, DI and HHE index. 0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 OG NV BL NV_BL Alpha (α) per each cooling technology/ HW long future HW_LF_MI HW_LF_LMS Pàg. 88 Memòria Fig 55. HI with cooling technologies during Present HW (P) Fig 56. HI with cooling technologies during Mid-future HW (MF) 0 10 20 30 40 50 60 70 80 90 100 Safe conditions Caution Extrem caution Danger Extreme danger Safe conditions Caution Extrem caution Danger Extreme danger Safe conditions Caution Extrem caution Danger Extreme danger HW_P_MS HW_P_MI HW_P_L HI per cooling technology/HW in the present OG NV BL NV_BL 0 10 20 30 40 50 60 70 80 90 100 Safe conditions Caution Extrem caution Danger Extreme danger Safe conditions Caution Extrem caution Danger Extreme danger HW_MF_MIS HW_MF_L HI per cooling technology/HW in the mid future OG NV BL NV_BL Assessment of climate resilience in vulnerable buildings in the city of Barcelona Pág. 89 Fig 57. HI with cooling technologies during Long-future HW (LF) There is minimal difference in the HI results between TMY (Figure 47) and HW scenarios (Figures 55, 56 and 57). In the present, Figure 55, there are almost no hours classified as dangerous, except in the base case (OG) with only 4.75%. During the mid-future (Figure 56), extreme caution hours begin to rise to 86.31% during HW_MF_MIS, and by the long future (Figure 57), most occupied hours fall within extreme caution and danger categories, achieving 84.33% during HW_LF_LMS. A few extreme danger hours appear, especially in simulations of the base case and natural ventilation alone. This outcome confirms a previously discussed problem: as time progresses, thermal discomfort worsens, making natural ventilation less effective. The outside air, which would typically help cool the indoor environment, becomes equal to or hotter than indoor air, offering little to no cooling benefit. 0 10 20 30 40 50 60 70 80 90 100 Safe conditions Caution Extrem caution Danger Extreme danger Safe conditions Caution Extrem caution Danger Extreme danger HW_LF_MI HW_LF_LMS HI per cooling technology/HW in the long future OG NV BL NV_BL Pàg. 96 Memòria Fig 65. Materials of TY [43] Both buildings were constructed in the post Spanish Civil War period, during a time when there were no building regulations like the Código Técnico de la Edificación (CTE) in place. As a result, construction were not specially focused on energy efficiency or thermal comfort, leading to buildings with limited thermal performance. While the construction materials and thermal properties of both buildings are relatively similar, with neither offering highperformance insulation, the key differentiating factor lies in the implementation of cooling technologies. In the typical Catalan building, the use of active cooling systems helps improve indoor comfort and resilience to climate change. In contrast, the building located in the most socioeconomically disadvantaged area, despite having comparable construction, has more difficulties implementing such systems due to energy poverty. A more specific analysis of the resilience of the two buildings has been carried out. The indicators that have been compared are the ones used in the two studies: alpha for resilience and HI and DI for comfort. Assessment of climate resilience in vulnerable buildings in the city of Barcelona Pág. 97 6.1. Resilience analysis, buildings comparison of the base cases At first, the base case of both buildings was compared to conclude how the surroundings and the structure of these affected the resilience and comfort indicators studied. With this information, conclusions can be drawn on how the buildings of families in a situation of energy poverty behave towards the most typical building of Catalunya, initially of families without significant financial problems. Subsequently, these data will be compared with the results of these same indicators but for buildings with applied cooling technologies. With this last comparison, we will be able to see how necessary cooling technologies are for the comfort of the inhabitants of a building and how those affected by a situation of energy poverty will feel. Fig 66. Alpha comparison during TMY between CM and TY buildings Fig 67. Alpha comparison during HW between CM and TY buildings By observing Figures 66 and 67, we can already see the difference in the resilience of both 0,73 0,76 0,74 0,56 0,65 0,66 0 0,2 0,4 0,6 0,8 TMY_P TMY_MF TMY_LF Alpha comparison during TMY BC_CM BC_TY 0,77 0,77 0,75 0,75 0,74 0,78 0,74 0,63 0,63 0,61 0,65 0,64 0,71 0,67 0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 HW_P_L HW_P_MI HW_P_MS HW_MF_L HW_MF_MIS HW_LF_LMS HW_LF_MI Alpha comparison during HW BC_CM BC_TY Pàg. 98 Memòria buildings. During heat waves and the typical meteorological year, the building with the highest alpha is the one studied in this paper, the Ciutat Meridiana’s building, being up to 22.2% higher than the TY values during HW and up to 30.04% during TMY. These results can be explained by the small difference in the building’s materials. CM building was built with higher thermal transmittance materials than the TY building, which suggests less insulation and higher heat loss/gain. Fig 68. HI comparison during TMY between CM and TY buildings 0 10 20 30 40 50 60 70 80 90 100 Safe conditions Caution Extrem caution Danger Extreme danger Safe conditions Caution Extrem caution Danger Extreme danger Safe conditions Caution Extrem caution Danger Extreme danger TMY_P TMY_MF TMY_LF HI comparison during TMY BC_CM BC_TY Assessment of climate resilience in vulnerable buildings in the city of Barcelona Pág. 99 Fig 69. HI comparison during HW_P between CM and TY buildings 0 10 20 30 40 50 60 70 80 90 Safe conditions Caution Extrem caution Danger Extreme danger Safe conditions Caution Extrem caution Danger Extreme danger Safe conditions Caution Extrem caution Danger Extreme danger HW_P_L HW_P_MI HW_P_MS HI comparison during HW_P BC_CM BC_TY 0 10 20 30 40 50 60 70 80 Safe conditions Caution Extrem caution Danger Extreme danger Safe conditions Caution Extrem caution Danger Extreme danger HW_MF_L HW_MF_MIS HI comparison during HW_MF BC_CM BC_TY Pàg. 100 Memòria Fig. 70. HI comparison during HW_MF between CM and TY buildings Fig 71. HI comparison during HW_LF between CM and TY buildings By observing Figures 68,69,70 and 71 we can see that the Heat Index of the CM building has a more elevated % of occupied hours classified as with the extrem caution, danger and extrem danger levels than the TY building. In Figure 68, it’s visible how the TY building only has a 34.58% of dangerous occupied hours in the Long future while the CM building has, sinse the very beginning of the study, 3.31% of dangerous occupied hours during the present and 52.8% of dangerous occupied hours during the long future. In the longest and most severe heat wave of the long future, Figure 71, where the worst HI values are obtained, the CM building, apart from the 1.39% of extrem dangerous occupied hours, has 93.25% of dangerous occupied hours while the TY building only has 34.88% of those. 0 10 20 30 40 50 60 70 80 90 100 Safe conditions Caution Extrem caution Danger Extreme danger Safe conditions Caution Extrem caution Danger Extreme danger HW_LF_LMS HW_LF_MI HI comparison during HW_LF BC_CM BC_TY Assessment of climate resilience in vulnerable buildings in the city of Barcelona Pág. 101 Fig 72. DI comparison during TMY between CM and TY buildings Fig 73. DI comparison during HW_P between CM and TY buildings 0 10 20 30 40 50 60 70 80 Not heat Mild heat Heavy heat Severe heat Not heat Mild heat Heavy heat Severe heat Not heat Mild heat Heavy heat Severe heat TMY_P TMY_MF TMY_LF DI comparison during TMY BC_CM BC_TY 0 10 20 30 40 50 60 70 80 90 Not heat Mild heat Heavy heat Severe heat Not heat Mild heat Heavy heat Severe heat Not heat Mild heat Heavy heat Severe heat HW_P_L HW_P_MI HW_P_MS DI comparison during HW_P BC_CM BC_TY Pàg. 102 Memòria Fig 74. DI comparison during HW_MF between CM and TY buildings Fig 75. DI comparison during HW_LF between CM and TY buildings Figures 72,73,74 and 75 show that the Discomfort Index of the CM building has a more elevated % of occupied hours classified as with heavy heat and severe heat levels than the TY building. In Figure 72, during the present period, CM building has a higher rate of heavy 0 10 20 30 40 50 60 70 Not heat Mild heat Heavy heat Severe heat Not heat Mild heat Heavy heat Severe heat HW_MF_L HW_MF_MIS DI comparison during HW_MF BC_CM BC_TY 0 10 20 30 40 50 60 70 80 90 100 Not heat Mild heat Heavy heat Severe heat Not heat Mild heat Heavy heat Severe heat HW_LF_LMS HW_LF_MI DI comparison during HW_LF BC_CM BC_TY Assessment of climate resilience in vulnerable buildings in the city of Barcelona Pág. 103 heat occupied hours than TY, yet in the long future period this situation is reversed when TY heavy heat occupied hours are 8.45% higher than th CM heavy heat hours. In the longest and most severe heat wave of the long future, Figure 75, where the worst DI values are obtained, the CM building reverts the situation again with 96.03% of severe heat occupied hours versus the 62.81% severe heat occupied hours of the TY building. 6.2. Resilience analysis, buildings comparison with cooling technologies implemented As with the indicators, the technologies that have been compared are those studied in both works: Natural Ventilation (NV) and Blinds (BL). The other technologies simulated in the TY building are: Green Roof (GrRf), Advanced Windows (AdWind) and Air Conditionate (AC). It should be noted that these cooling technologies have not been applied in this work for several reasons: • Both the GR and the AdWind are solutions that modify the building's construction elements. Today, rehabilitating a building implies that this rehabilitation complies with the CTE, which is much stricter and more specific than the regulations that existed when the two buildings studied in this work were built. In addition, rehabilitating a multi-family residential building implies that all neighbours must be able to afford to pay for this reform. In the case of the CM building, the aim is to simulate the situation of a vulnerable building in the worst case, therefore the objective is to study how the building reacts without any technology that requires money. • The last technology proposed for the TY building is AC. This strategy has been discarded since it is a technology considered active, which implies an extra consumption of energy to operate [45]. Observing the case of the TY building, it can be seen how the electricity consumption to cool the building is more than double the initial consumption during the TMY_LF (123.38 MJ/m² with AC vs. 58.69 MJ/m² without AC) [43]. This expense is not acceptable in the case of the CM building, where solutions are intended to be studied that do not cause extra expenses for the tenants. Pàg. 104 Memòria Fig 76 and 77. Electricity consumption without and with AC in the TY during TMY_LF With that being said, it has been decided to compare the passive solution with the best results obtained in the CM building, the combination of Natural Ventilation (NV) together with Blinds (BL), in order to compare the buildings in the best-case scenario possible with the cheapest cooling technologies. The indicators studied are Alpha for resilience and HI and DI for comfort analysis. The values of this indicators for the TY building are taken from another previous study of this swelling, where all of the possible combinations of cooling technologies are simulated and studied [70]. Fig 78. Alpha comparison during TMY between CM and TY buildings with NV & BL 0,21 0,43 0,57 0,12 0,22 0,38 TMY_P TMY_MF TMY_LF 0 0,1 0,2 0,3 0,4 0,5 0,6 Alpha comparison NV&BL during TMY NV & BL_CM NV & BL_TY Assessment of climate resilience in vulnerable buildings in the city of Barcelona Pág. 105 Fig 79. Alpha comparison during HW between CM and TY buildings with NV & BL As its been mentioned before, the effect of a better envelope and insulation in the TY building is what is causing this small but noticeable difference between CM and TY alpha results during the TMY. In Figure 78, during the present, the alpha value decreased 71.23% for CM and 78.57% for TY, compared to the value of the base case (BC) of each building. During the mid-future, the passive technologies lose some of their effectiveness, as reflected in the alpha values which now decreases 43.4% for CM and 65.15% for TY. For future scenarios, we can already see the increasing tendency of this indicator, which means that in the future, the indoor overheating combined with the ambient warmness will worsen the resilience of this dwellings. A potential inconsistency has been identified in this study related to the analysis of resilience indicators during heat wave (HW) periods. Specifically, Figure 79 reveals that the TY building exhibits poorer performance than the CM building in terms of the alpha indicator. This outcome is unexpected and raises concerns, as throughout the study, the CM building has consistently demonstrated lower resilience compared to the TY building. Such results suggest the possibility of an error in the data for one of the two buildings. It is particularly puzzling that the CM building records such low alpha values, especially during the present and mid-future scenarios when passive cooling strategies remain relatively effective. For instance, as illustrated in Figure 79, the alpha value during the mid-future HW period increases by only 4.65%, which appears disproportionately low. In contrast, the TY building presents unusually high alpha values. In the present HW scenario (Figure 79), the lowest alpha value reaches 0.37, more than triple the value of 0.12 recorded in the present TMY scenario. Similarly, in the mid-future, Figure 78, the TMY alpha value of 0.38 increases by approximately 50%–52.63%, reaching values between 0.57 and 0.58 during HW periods. These discrepancies are repeated in the results of the other indicators. 0,28 0,31 0,35 0,45 0,45 0,37 0,41 0,48 0,57 0,58 0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 HW_P_L HW_P_MI HW_P_MS HW_MF_L HW_MF_MIS Alpha comparison NV&BL during HW NV & BL_CM NV & BL_TY Pàg. 112 Memòria 7. Planning This section presents the planning for the thesis, as shown in Figures 89 and 90. The task name column outlines an organized list of tasks that needed to be addressed in order to achieve the project’s objectives. Although project planning was a fundamental part of ensuring the thesis's success, the process did not go entirely as intended. Fig 89. Gantt’s diagram 2024 Fig 90. Gantt’s diagram 2025 During the development of this final degree project, sticking to the initial plan proved difficult due to a number of unforeseen challenges. To begin with, the research on various energy poverty studies took longer than anticipated, largely because the focus of the investigation was unclear at first. One of the major setbacks was the malfunction of one of the computers intended for use throughout the project. Due to its outdated condition, the device was unable to run essential software such as SketchUp and the OpenStudio plug-in. As a result, an alternative computer had to be secured in order to proceed with the building modelling process. This caused significant delays in the original timeline and was a key factor in requesting an extension for the project. Lastly, once the new computer was equipped with the necessary programs, designing the building model also took more time than expected. Additional time was needed to become familiar with the software, which involved watching several YouTube tutorials. 8. Economic assessment This section provides an evaluation of the economic costs associated with the development of this study. The analysis is divided into two main categories. Firstly, the costs related to the modelling of the building, the simulation hours, and the time devoted to research and analysis of results are examined. Secondly, an evaluation is conducted of the expenses associated with the materials and software used throughout the development of this thesis. 8.1. Costs of hours dedicated Human resource costs encompass both the contributions of the student and the supervising professor. These include hours spent in meetings, project supervision, evaluation of the student's work, and problem-solving. The estimated cost for these contributions is 20 €/h. The cost of electricity has been calculated as the average between the electricity rates of the studio, where part of the work was conducted, and the office, where the building modelling took place. Energy consumption by computers the project was determined based on the voltage and amperage specifications of the power adapters (20V and 3.25A, respectively). These values were multiplied and then divided by 1,000 to obtain the power consumption in kilowatts (kW). This result was then multiplied by a typical power usage efficiency factor of 0.8 [57]. The total energy consumption was finally calculated by multiplying this value by the total number of hours dedicated to the project. Table 15. Cost of the project, worked hours Concept Value Units €/h Total € Research hours (student) 85 h 13 1105 Modelling hours (student) 60 h 13 780 Simulation hours (student) 120 h 13 1560 Analysis hours (student) 65 h 13 845 Project drafting 70 h 13 910 Dedicated hours (professor) 40 h 30 1200 Energy consumed 56 kWh 0.12 6.7 Total 6406.7 € Pàg. 116 Memòria 8.2. Costs of materials The software utilized throughout the development of this project was either open-source or available through trial versions, thereby incurring no direct financial cost. Two computers were employed during the course of the study. The initial device experienced technical issues related to its processor and graphics card, necessitating its replacement with a new, functional computer to ensure the continuity and quality of the work. Concept Value Units € per unit Total € Program licenses 3 Licenses 0 0 Electronic devices 2 Computers 1560 + 2450 4110 Total 4110 € Table 16. Cost of the project, material costs The total cost of the project amounts to 10516.7€. Assessment of climate resilience in vulnerable buildings in the city of Barcelona Pág. 117 9. Environmental assessment This work aims to analyse the thermal response of a vulnerable building to climate change and how this response varies depending on the passive cooling technologies applied. These technologies are entirely passive [39], meaning they do not consume additional energy to operate. The objective of this section of the study is to assess whether the implementation of these technologies can contribute to reducing the building's energy consumption and thus promote a more sustainable cooling model. The technologies analysed are Natural Ventilation and External Blinds, and they have been selected for two main reasons: • The first is to avoid further contributing to climate change. Tables 17 and 18 show the annual heating and cooling energy demands of the base case building and the building with the best cooling technology simulated for each of the periods studied. The objective is to assess whether the most promising solution can effectively reduce the environmental impact of this building and promote more sustainable practices for other buildings in similar conditions. TMY Periods Heating demand [ 𝒌𝑾𝒉 𝒎𝟐 ⁄] Cooling demand [ 𝒌𝑾𝒉 𝒎𝟐 ⁄] BC NV_BL Reduction (%) BC NV_BL Reduction (%) TMY_P 14.73 4.83 67.19 27.31 19.85 27.32 TMY_MF 11.19 2.38 78.72 38.81 26.72 31.14 TMY_LF 3.09 0.62 80.20 57.17 44.43 22.27 Table 17. Heating and cooling demand of the building with and without cooling technologies during TMY Pàg. 118 Memòria HW Periods Heating demand [ 𝒌𝑾𝒉 𝒎𝟐 ⁄] Cooling demand [ 𝒌𝑾𝒉 𝒎𝟐 ⁄] BC NV_BL Reduction (%) BC NV_BL Reduction (%) HW_P_L 12.23 2.35 80.80 32.77 22.05 32.69 HW_P_MS 11.44 2.21 80.65 27.23 18.98 30.27 HW_P_MI 13.14 2.59 80.32 24.25 16.09 33.67 HW_MF_MIS 5.37 2.17 59.57 43.79 33.29 23.961 HW_MF_L 6.43 1.73 73.13 40.21 29.82 25.84 HW_LF_LMS 3.06 0.26 91.57 80.28 67.93 15.38 HW_LF_MI 2.16 0.25 88.65 71.10 62.97 11.42 Table 18. Heating and cooling demand of the building with and without cooling technologies during HW As shown in Tables 17 and 18, the cooling demand increases over time when comparing the base case (BC) of TMY_P to that of TMY_LF. However, the implementation of passive cooling technologies results in a noticeable reduction in cooling demand. Nevertheless, the effectiveness of these technologies diminishes progressively over time, as the reductions they achieve become less significant under future climate conditions. Implementing the combination of natural ventilation and blinds in the building allows for annual savings of 67.19% - 80.20% in heating and 22.27% - 31.14% in cooling during the long future (Table 17). • The second reason is what has already been mentioned before, that the analysed case involves a building where the residents have low-income levels. Therefore, the goal is to improve their thermal comfort without increasing their energy bills. It is important to highlight that what has been analysed in this section is the building’s thermal demand, not its actual energy consumption. This distinction is based on two main reasons: • First, the energy consumption associated with air-conditioning a dwelling ultimately depends on the behaviour and decisions of its occupants. Residents with high Assessment of climate resilience in vulnerable buildings in the city of Barcelona Pág. 119 incomes might choose not to invest in cooling or heating their spaces, while conversely, individuals living in energy poverty may prioritise spending a significant portion of their limited income towards maintaining indoor comfort. • Secondly, the energy consumption of conditioning systems is highly dependent on the specific equipment installed. Factors such as the nominal cooling and heating power, seasonal performance factors SEER and SCOP, and the average operational efficiency vary significantly between different models. Although a proposal for a low-cost air-conditioning system could have been developed for the studied building, doing so would have been put of scope for this project. It would have required extensive technical knowledge regarding HVAC systems, as well as compliance with the requirements set by the Spanish Technical Building Code, CTE. Pàg. 120 Memòria 10. Social and gender equality assessment It is important to highlight that this thesis has a social dimension that has been considered throughout the entire project. The aim was to carry out a comprehensive energy study focused on the most vulnerable social groups, while evaluating various passive strategies designed to be accessible and implementable by anyone, thanks to their low cost. This section aims to discuss how the implementation of passive cooling technologies impacts the most vulnerable groups, with a particular focus on the neighbourhood of Ciutat Meridiana, where this study has been carried out, and its residents. Valuable demographic, social, and economic information about the area has been obtained from the study by Ravetllat Mira, P. J., Díaz Gómez, C., Cornadó Bardón, C., & Vima Grau, S. [57]. Population analysis: The population of Ciutat Meridiana is characterised by a high percentage of immigrants of diverse nationalities, significantly exceeding the average for the city of Barcelona. The remaining population, composed largely of elderly individuals and younger generations who are returning to the neighbourhood due to the housing emergency and ongoing economic crisis, also exhibits a very low economic status. Despite the broader demographic trend towards ageing, Ciutat Meridiana maintains a high birth rate, largely driven by the immigrant population. The area is marked by high unemployment rates and a predominance of residents with very low income levels. Daily life in the neighbourhood is significantly affected by issues such as evictions and unemployment, which represent major social conflicts for its residents. Furthermore, evidence of overcrowded housing conditions highlights the severe residential vulnerability faced by many households. Access to Implemented Technologies: Among the two passive cooling technologies analysed, NV and BL, natural ventilation does not create any form of discrimination based on gender or social group, as it requires no investment or installation. However, the installation of blinds is not cost free, so it may pose an economic barrier for vulnerable households. Nevertheless, once implemented, both technologies have minimal to no maintenance costs, making them accessible in the long term for low-income families. Impact on Vulnerable Groups: Passive cooling technologies are particularly beneficial for low-income groups and for the most vulnerable members of society, such as the elderly and children, who, according to the demographic data, represent a significant percentage of the population in Ciutat Assessment of climate resilience in vulnerable buildings in the city of Barcelona Pág. 121 Meridiana. These groups are especially sensitive to extreme temperatures, and the implementation of passive cooling solutions can substantially improve their living conditions, enhancing both their health and well-being. Gender Balance in the Project Team: Efforts towards gender equality have also been considered in this project. In this case, the team consists of two members, both female, contributing inclusive perspectives throughout the research process. 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