Integrated urban regeneration for high-rise multi-family buildings by providing a multidimensional assessment model and decision support system

Journal of Building Engineering 76 (2023) 107359 Available online 17 July 2023 2352-7102/© 2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Integrated urban regeneration for high-rise multi-family buildings by providing a multidimensional assessment model and decision support system Pilar Mercader-Moyano a , * , Patricia Camporeale b , Antonio Serrano-Jim´ enez c a Departamento Construcciones Arquitect´ onicas I. University of Seville. Reina Mercedes Avenue, Nº2, 41012. Seville. Spain b Faculty of Architecture and Urbanism, National University of La Plata, 162, 47 st, Buenos Aires, Argentina c Departamento Construcciones Arquitect´ onicas. University of Granada, Doctor Severo Ochoa street. 18001. Granada, Spain ARTICLE INFO Keywords: Built environment Decision support system Multidisciplinary assessment model High-rise multi-family buildings Urban regeneration ABSTRACT Current urban challenges on promoting an integrated urban regeneration demand new decision support systems to adjust and optimise renovation strategies in the housing stock. This research aims to provide a multidimensional decision support system, specifically focused on high-rise multi-family buildings, which entails an added complexity in the design and decision-making stages of building renovation. The need to promote assessment protocols of key parameters that promote a viable and efficient renovation in high-rise multi-family buildings constitutes a research gap to be fulfilled in this study. This research contributes with an assessment and weighting model based on 12 indicators, both during and after renovation works, under 4 multidisciplinary dimensions: Technical; Social; Economic; and Environmental, in a -5 to 5 drawback-benefit index scale. An application procedure has been tested in two representative neighbourhoods in Argentina and Spain, identified by GIS resources, and demonstrating its operation and usefulness for vulnerable neighbourhoods due to global inflation. The implications of the graphic output of results, weighted for Mild, Moderate, Intense and Deep action strategies, allows us to identify drawbacks and benefits of each strategy independently, for each of the 12 indicators, visualising the trend, performance and variations between dimensions and strategies in large-scale buildings. Conclusions generate key recommendations and insights on decisionmaking patterns to urban policymakers by ensuring feasible and satisfactory renovation strategies in high-rise multi-family buildings. 1. Introduction Managing the built environment efficiently has become one of the key challenges of the 21st century, according to global reports that follow the Sustainable Development Agenda or the European Agenda for Sustainable Buildings [1,2], which highlight the need to develop new assessment and decision-making mechanisms related to building renovation [3]. In recent years, integrating multiple disciplinary sectors in the assessment and diagnosis of existing buildings, especially in the housing stock, has proven highly useful in order to decide what renovation strategies are the most optimal to implement in a given environment context [4]. Thus, providing support from technical, social, economic and environmental requirements becomes even more important due to current socioeconomic * Corresponding author. E-mail addresses: [email protected] (P. Mercader-Moyano), [email protected] (P. Camporeale), [email protected] (A. Serrano-Jim´ enez). Contents lists available at ScienceDirect Journal of Building Engineering journal homepage: www.elsevier.com/locate/jobe https://doi.org/10.1016/j.jobe.2023.107359 Received 13 January 2023; Received in revised form 9 July 2023; Accepted 11 July 2023 Journal of Building Engineering 76 (2023) 107359 2 Table 1 Main contribution, findings or gaps of related research on their subsequent topics. Topic Research paper Contributions/Gaps Housing renovation management – High-rise multifamily building Timur et al. [25] ⋅ Stated thermal retrofitting needs case-specific analyses and no generic solution ⋅ Final decision-making stage in renovation must consider thermal needs of users Serrano-Jim´ enez et al. [26] ⋅ Provide an accessibility assessment model for social housing for elderly users ⋅ Decision support system after identifying the main risks and the optimal actions Liu & Rodriguez [22] ⋅ Holistic approach to assess passive interventions and improve indoor quality ⋅ Based on a multi-objective optimization process in high-rise residential building Zhong et al. [23] ⋅Assessed energy and particular exposure in high-rise multi-fam- ily buildings ⋅ Control strategies with ventilation modes, filter efficiencies and ventilation rates Fars¨ ater et al. [14] ⋅ Evaluate how decisions are made in early stages of housing renovation projects ⋅ Show the need of a comprehensive analysis for all the viable aspects of concern Garrido & Mercader-Moyano [24] ⋅ Multidisciplinary rating of eco-efficient actions for implementing in housing ⋅ Difficulties of including users’ demands in the action proposal model Multidisciplinary approach for building renovation Love & Matthews [27] ⋅ Assess the risk and uncertainty of rework from a multidisciplinary approach ⋅ Addressing by using the line of inquiry of sense-making from different disciplines Ge et al. [28] ⋅ Combine experimental measurements and on-site surveys for reliable diagnosis ⋅ Propose multidisciplinary methods to identify efficient and sustainable solutions Mj¨ ornell et al. [29] ⋅ Sustainability and social awareness are moving up in proper renovation actions ⋅ Investigate how public and private housing owners deal with renovation levels Invidiata et al. [9] ⋅ Multidisciplinary approach should be implemented to select design strategies ⋅ Highlight the need of using multi-criteria approaches to ensure right decisions Li et al. [30] ⋅ Provide a comprehensive approach on the influence of stakeholders’ factors ⋅ Users offer a better addressing for stakeholder concerns in urban renovation Assessment model as decision support systems Moghtadernejad et al. [31] - Propose a decision-analysis methods to help identifying the best façade solution - Particularly focused on façades but interesting to extend to housing renovation Huang et al. [32] ⋅ Assessed 3 supporting processes for optimising project management and costs ⋅ Decision-making as reducing capital cost barrier to extend sustainable renovation Giretti et al. [33] ⋅ Stated decisions often are taken with limited knowledge in short time frames ⋅ Importance of developing tools for supporting fast and reliable assessments. Kamaruzzaman et al. [34] ⋅ Incorporate weighting system for decision-making applied on many themes. ⋅ Exploratory study that gathers certain survey responses with reduced sample (continued on next page) P. Mercader-Moyano et al. Journal of Building Engineering 76 (2023) 107359 3 limitations caused by inflation [5]. Regarding the conservation status and the increasing obsolescence in the built environment, high-rise multi-family buildings are highly representative, as a the most prevalent type of building in the second half of the 20th century to meet the urgent needs of housing [6]. The high-rise consideration may vary according to different countries and regulatory requirements, so considering different fire protection regulations and accessibility limitations along with other socio-economic criteria provided by promoters, for this research purpose these high-rise buildings are established as those with 5 or more storeys [7]. In fact, according to European stats, this high-rise multi-family typology represents 37% of the existing buildings [8], which entails the need to develop more research advances towards innovative construction and their efficient renovation management [9]. This building category, both in linear and in tower typologies, involves an added complexity in the design and decision-making stages of building renovation, since the intervention costs, the technical difficulties of the works, and even the number of owners involved are higher compared to low-density construction [10]. Additionally, the expected drop in public funding to face urban regeneration, due to the limitations caused by inflation and population ageing [11], has become a major challenge in the design of an optimised tool for decision-making. Therefore, this research field currently demands a standardised system that could be adapted to the tower or linear model, which could allow for the visualization of both the improvements and drawbacks that different renovation strategies imply [12]. Given this context, there is currently a potential line of research that focuses on providing renovation guidelines on the assessment and decision-making in building renovation strategies, as Napoli et al. [13] or Fars¨ ater et al. [14] focused on in their research. However, the specific literature review carried out in section 2 concludes what was highlighted by Ibarloza et al. [15], that there is a research gap on multi-criteria assessment systems specifically adapted to high-rise multi-family buildings to support the optimal decision-making of renovation strategies [16]. In addition, there is a lack of models that combine technical, social, economic and environmental perspectives, which would help to identify the advantages or disadvantages of each discipline and obtain an integral diagnosis according to each urban and socioeconomic context [17]. Thus, there is a need to promote protocols to decide which parameters are essential to cover in the high-rise multi-family building renovation process [18]. The fulfilment of this need would also comply with the Sustainable Development Goals and different national targets related to the built environment [19,20]. This would be accomplished by integrating more multidisciplinary factors and variables during the works, to promote feasible renovation strategies and appropriate retrofitting techniques [21]. This paper aims to design a multidisciplinary decision support system on housing renovation strategies whose originality is focused on high-rise multi-family buildings. As a key point regarding the novelty of this research, the provided approach fulfils the need to establish a multidisciplinary mechanism that integrates 4 dimensions: Technical, Social, Economic and Environmental, assessed from 12 identified parameters related to the during and after renovations works process. Consequently, this study expects to promote useful insights for decision-making through an assessment model that independently weights and shows the results by using data science, weighting and scoring a large amount of information, and GIS, for the identification and selection of the sample. The implications on the replicability of the model can allow us to optimise the renovation works in high-rise multi-family buildings and reduce the complexity of works. As key contributions, this research gathers renovation guidelines adapted to high-rise multi-family buildings, develops an assessment model based on weighted indicators, and provides a decision support system from a multidisciplinary approach based on four dimensions through an original graphical output of results for the decision-making on different renovation alternatives. To follow this aim, the research identifies potential renovation factors, gathering their assessment parameters under specific criteria for high-rise buildings, and adjusts and tests the model in two pilot neighbourhoods in such a way as to exemplify the graphic results obtained. The application of this model would weight information on the impact and benefit of each renovation strategy both during and after the renovation works for a proper decision-making. The following sections present a literature review of the research subtopics and relevant research gaps, thus defining and justifying the proposed multidisciplinary decision support system. Then, this model is applied and tested in two representative neighbourhoods from Argentina and Spain, countries with appropriate standards for the application of this model according to socioeconomic context and urban regeneration challenges. The main results and conclusions are finally discussed regarding its replicability and insights. 2. Literature review This section aims to define a sample of the most influencing papers to consider in this literature review, that will serve to highlight the recent advances and to identify the research gap that needs to be fulfilled. Each paper represents important motivations for the design, adjustment and development of the multidisciplinary decision support system for high-rise multi-family buildings. The documents broken down in Table 1 gather 3 main subtopics related to the goals of this research. There is a recent trend related to the management of housing renovation, as shown in the first subtopic, although there are very few studies that focus particularly on Table 1 (continued) Topic Research paper Contributions/Gaps Monz´ on & L´ opez-Mesa [35] ⋅ Decision support system based on performance indicators to detect priorities ⋅ Provided a graphical output of results to facilitate decisionmaking in renovation P. Mercader-Moyano et al. Journal of Building Engineering 76 (2023) 107359 4 high-rise multi-family buildings, highlighting the approaches made by Liu and Rodríguez [22] along with Zhong et al. [23]. In this sense, the work developed by Farsater et al. [14] demonstrates the usefulness of establishing mechanisms that guarantee the success of renovation strategies, as well as the difficulty of addressing user demands in action protocols, as Garrido and Mercader-Moyano [24] or Timur et al. [25] concluded. Fig. 1. General outline of the assessment and decision support system for high-rise multi-family buildings. P. Mercader-Moyano et al. Journal of Building Engineering 76 (2023) 107359 5 Regarding the following multidisciplinary subtopic, there is a research trend related to demonstrate the benefits of integrating social, economic and environmental dimensions of analysis into the technical discipline for promoting effective renovation works in the built environment, ensuring efficient and satisfactory interventions in the built environment [36]. In fact, recent studies have combined technical inspections of buildings, together with user questionnaires and environmental and economic simulations of renovation processes, which offered complete information to developers and public administrations, as Li et al. [30] demonstrated in their study. Finally, the third subtopic covers many disciplinary sectors but remarks the importance of generating mechanisms as decisionmaking support systems. Regarding housing renovation, this is becoming increasingly useful given the complexities that exist during the works and anticipating certain results that the renovation proposals will entail, as suggested by Kamaruzzaman et al. [34]. In addition, numerous studies, such as the one developed by Monz´ on and L´ opez-Mesa [35], agree on the importance of designing an understandable and intuitive graphical output of results to quickly determine the advantages and disadvantages of each process. Thus, multiple studies have contributed with numerous assessment and decision-making models to effectively design the proposals, by analysing multiple parameters [37]. Beyond all the implications and limitations from each referenced study, the literature review highlights a research gap on the absence of studies that specifically focus on high-rise multi-family buildings, carrying out a special analysis on particularities and variations that exist in this scope. For all these reasons, this study fulfils this research gap and contribute original research to the literature review by providing a multidisciplinary assessment procedure to quantify multiple variables from different dimensions, in order to distribute public funds and adjust resources for a proper city-wide management in urban regeneration, as United Nations [19] and GBCE [2] stated. 3. Methodology Aiming to design a multidisciplinary assessment model on housing renovation strategies, particularly focused on high-rise areas with a remarkable housing density, Fig. 1 presents a graphic outline of the different stages of the research, highlighting the scope and the corresponding motivations, providing the novelty and the insights and expected implications. The operation procedure is based on two main methodological concepts that can allow us to conduct the appropriate decisionmaking process in high-rise multi-family buildings, following two key concepts provided by Cinelli et al. [38], and subsequently adapted to this research approach: •Analytical Hierarchy Process (AHP), that compares and ranks different renovation alternatives according to quantitative and qualitative variables. •Multi-attribute utility theory (MAUT), which quantifies multiple parameters in a weighted calculation through a standardised scale (-5 to 5). The combination of these methodological concepts provide clear information for decision-making and graphical output of results regarding the advantageous and disadvantageous character for each index [39]. In fact, the use of these theoretical concepts is becoming a trend in recent research studies, seeking to structure different influencing factors and to develop a multidisciplinary quantification of 12 main identified variables, considered as key original elements in this model. Additionally, the corresponding variables are divided into 6, related to the impact generated during the works and another 6 related to the result. These 12 variables will be weighted independently from a multidisciplinary scale from 4 dimensions: Technical, Social, Economic and Environmental. The methodology defined here complies with different scenarios to convert it into a standardised process, gathering technical information from inspections, and social demands from participatory surveys, that can be replicated in other urban and socioeconomic contexts: •1. Carry out an integral diagnosis of the building typology. •2. Identify complexities, demands and requirements of high-rise multi-family building. •3. Design different renovation strategies according to the multidisciplinary diagnosis. •4. Data process and weighting in this preliminary assessment model on 12 key variables, quantifying in advance advantages and disadvantages during and after works. •5. Graph the results output and discuss through a sensitivity analysis for the correct decision-making from different dimensions to identify the most efficient and feasible actions. To test the operation of this standardised procedure, this model is going to be applied in representative neighbourhoods in Argentina and in Spain, to verify the testing results and their corresponding implications. Throughout the following subsections, the different stages of the proposed model will be broken down, defining the incorporated dimensions and the 12 key variables related to the process during and after the works, as well as their weighting parameters, which help to facilitate decision-making for owners, promoters and even government entities with useful policy-making insights. P. Mercader-Moyano et al. Journal of Building Engineering 76 (2023) 107359 6 Table 2 Variables, assessment parameters and weighting procedure of the multidisciplinary assessment. Variable Assessment Parameters Weighting Procedure 1] Duration | Execution period (During works) - Quantitative - Progressive range |X: [No. of weeks] [Min.: 2 weeks | Max.: 156 weeks (3 years)] ⋅ Limit between Benefits | Drawbacks: [Tec]:1 year; [Soc]:6 months; [Ec]:1 year; [En]:6 months ω : 1.2 [>9 storeys.]; 1 [7-9 storeys.]; 0.8 [5-6 storeys.] V1B|D= ± ω XB|D XRangeB|D∗5 2] Scale | Scope of action (During works) - Scale and intensity |X: [Item-Value] Outside (O); Common areas (C); Inside Apartm. (I): [0] Null [1]; Punctual [2]; Partial [3]; Total ⋅ Benefits | Drawbacks: [Tec]:I |O,C; [Soc]: O |I,C; [Ec]: I |O,C; [En]: I, C |O ω : 1.2 [>9 storeys.]; 1 [7-9 storeys.]; 0.8 [5-6 storeys.] xO,C,I=∑x∗ni xmax ∗nt V2B|D= ± ω ∑(xO,xC,xI) ∑xt∗5 3] Construction system | Serialisation (During works) - Elements, Techniques, Complexity |X: [Item-Value] - Elements: (A) Distribution and partitions; (B) Technical systems; (C) Vertical communication core; (D) Building envelope; (E) Structure and foundation; (F) Facilities - Techniques: [P] Prefabricated construction; [S] On-site components. - Complexity [1]: Low [2]; Medium [3]; High ⋅ Benefits | Drawbacks: P |S; 1| 2,3. [Tec]: A,B,F |C,D,E; [S]: B,D,F | A,C,E; [Ec]: A,B |C,D,E,F; [En]: B,C,F | A,D,E. ω : 1.2 [>9 storeys.]; 1 [7-9 storeys.]; 0.8 [5-6 storeys.] wA,B,C,D,E,F=∑x∗ni xmax ∗nt - - - - - - - - - - - - - - -V3B|D= ± ω ∑(wA,B,C,D,E,F) ∑wt∗5 4] Concerns | Risks (During works) - Risks, Concerns, Impact |X: [Item-Value] - Risks & Concerns: (A) Rent increase; (B) Quality of the information; (C) Organisation of the Renovation plan; (D) Nuisance; (E) Noise; (F) Dust. - Impact: [0] No impact - [5] High impact ⋅ Benefits | Drawbacks: [Tec]: B,C |E,F; [S]: B,C | A,D,E,F; [Ec]: C | A, F,D; [En]: C |D,E,F. ω : 1.2 [>9 storeys.]; 1 [7-9 storeys.]; 0.8 [5-6 storeys.] wA,B,C,D,E,F=∑x∗ni xmax ∗nt - - - - - - - - - - - - - - -V4B|D= ± ω ∑(wA,B,C,D,E,F) ∑wt∗5 5] C&D | Waste (During works) - Quantitative - Volume, Weight |X: [m 3 & Tonnes] - Volume (m 3 ): [1 … 300 m 3 ] - Weight (tonnes): [1 … 300 tonnes] ⋅ Limit between Benefits | Drawbacks: [Tec]: 40m 3 | 100t; [Soc]: 20m 3 | 70t; [Ec]: 20m 3 | 70t; [En]: 10m 3 | 30t ω : 1.2 [>9 storeys.]; 1 [7-9 storeys.]; 0.8 [5-6 storeys.] Vol. = ± ω x xmax ∗2.5 Weig. = ± ω x xmax ∗2.5 V5B|D= ±Vol.±Weig. 6] Relocation | Disablement (During works) - Area | Restriction period |X: [Yes/No-Time] - Limitation: (A) Complete users’ relocation; (B) Temporary restricted – apartments | rooms; (C) Temporary restricted – building envelope; (D) Temporary restricted – communal spaces; (E) Temporary restricted – facilities - Period: (0) No; (1) <1 week; (2) 1 week-1 month; (3) 1–3 months; (4) 3–6 months; (5) Over 6 months ⋅ Benefits | Drawbacks: [Tec; Soc; Ec; En]: No relocation | Limitations & Time ω : 1.2 [>9 storeys.]; 1 [7-9 storeys.]; 0.8 [5-6 storeys.] Yes: V6B|D =-5 - 0 →x = ± ω ⋅[time] No: V6B|D =0-5 →± ω ⋅x[b,b,c,d,e] 7] Liveability | Comfort (After works) - Parameter, Impact, Frequency |X: [Item-Value] - Scope: (A) Distribution; (B) Thermal comfort; (C) Air quality; (D) Lighting - Impact: [0] No impact - [5] High impact - Frequency: (a) Occasional; (b) Seasonal; (c) Permanent ⋅ Benefits | Drawbacks: [Tec; Soc; Ec; En]: Benefit based on higher Impact & Permanence ω : 1.2 [>9 storeys.]; 1 [7-9 storeys.]; 0.8 [5-6 storeys.] V7B|D= ± ω ∑(xA,xB,xC,xD) ∑xt∗5 8] Accessibility – Mobility (After works) - Parameter, Impact, Frequency |X: [Item-Value] - Scope: (A) Accessibility common spaces; (B) Accessibility inside apartments; (C) Safety; (D) Mobility widths - Impact: [0] No impact - [5] High impact - Frequency: (a) Occasional; (b) Seasonal; (c) Permanent ⋅ Benefits | Drawbacks: [Tec; Soc; Ec; En]: Benefit based on Higher Impact & Permanence ω : 1.2 [>9 storeys.]; 1 [7-9 storeys.]; 0.8 [5-6 storeys.] V8B|D= ± ω ∑(xA,xB,xC,xD) ∑xt∗5 9] Energy demand | Savings (After works) - Quantitative - Reduction, % Saving |X: [kWh/m 2 & %] - Demand (kWh/m 2 ): [1 … 100] - Savings (%): [1 … 80] Dem. = ± ω x xmax ∗2.5 Sav. = ± ω x xmax ∗2.5 V9B|D= ±Dem.±Sav. (continued on next page) P. Mercader-Moyano et al. Journal of Building Engineering 76 (2023) 107359 7 3.1. High-rise multi-family building assessment model for decision-making The usefulness of this multidisciplinary assessment model focuses on its particular approach on high-rise multi-family buildings, a representative building typology with particular complexities in the built environment. A combined model is presented that considers, according to calculations that are presented in Table 2, both the information from the technical diagnosis made by architects and engineers through inspection sheets, along with demands and other social parameters obtained from carrying out questionnaires and interviews with the residents. The research establishes 4 main dimensions of assessment, highlighting the importance of a multidisciplinary approach as in recent studies such as Invidiata et al. [9] or Mercader-Moyano et al. [10], which allow us to quantify renovation parameters during and after the works, from different fields. Thus, the four dimensions are: 1. Technical; 2. Social; 3. Economic and 4. Environmental, in each of which the results of 12 parameters will be organised and quantified in their values taking the whole building into account, as well as enabling a discussion of the results according to the benefits and drawbacks of each renovation strategy. The definition is as follows: ⋅ Technical (D1) brings together the architectural, technological, and spatial patterns for the diagnosis of high-rise multi-family buildings. Each typology receives technical visits and inspections to carry out an evaluation regarding the materials, thermal and environmental performances, conservation status and other conditions related to complying with the habitability conditions and guaranteeing the comfort of users in housing environments. This dimension provides a quantification of the impact and benefits of the different renovation strategies proposed from parameters that meet the technical perspective. ⋅ Social (D2) represents a particular assessment of different parameters from the perceptions, reactions and benefits introduced towards the users in the built environment, measuring direct and indirect consequences of the way in which the works are carried out. Other subsequent aspects are considered, including their feasibility, management and other issues that contribute to improving the success of planning a renovation strategy. These are weighted based on preferences established in interviews, questionnaires and other community reports. As an original contribution, each of the selected parameters is considered from the social discipline, so as to ensure successful and satisfactory decided strategies. ⋅ Economic (D3) incorporates a financial vision to each one of the processes and stages of the high-rise multi-family building renovation, by considering investments, direct and indirect costs from the renovation works, maintenance fees and the subsequent increase in the valuation of the properties. This assessment allows for the decision-making to be adjusted in housing renovation strategies from a viable and defrayable guarantee of expenses both from the promoters and the homeowners. This evaluation provides important insights and variations from the economic perspective in each assessed variable, even more so in the case of large-scale buildings in which the number of properties and owners is too high to pay for certain common costs. ⋅Environmental (D4) represents a particular assessment perspective based on a quantitative and qualitative perspective on sustainability and resource efficiency in the renovation process in high-rise residential buildings. This dimension focuses on environmental parameters in indoor/outdoor conditions, along with adding an environmental approach to the results outcomes including the optimization of resources, the adjustment of renovation mechanisms and other transversal patterns aimed towards achieving higher levels of efficiency during the works and its decision-making. Table 2 (continued) Variable Assessment Parameters Weighting Procedure ⋅ Limit between Benefits | Drawbacks: [Tec]: 20 kWh/m 2 | 15%; [Soc]: 10 kWh/m 2 | 10%; [Ec]: 10 kWh/m 2 | 10%; [En]: 30 kWh/m 2 | 20% ω : 1.2 [>9 storeys.]; 1 [7-9 storeys.]; 0.8 [5-6 storeys.] 10] Visual change | Aesthetics (After works) - Parameter, Impact |X: [Item-Value] - Scope: (A) Outside visual change; (B) Inside visual change; (C) Aesthetic improv.; (D) Heritage protection - Impact: [0] No impact - [5] High impact ⋅ Benefits | Drawbacks: [Tec; Soc; Ec; En]: Benefit based on higher Impact [Tec]: D; [S]: A, B,C,D; [Ec]: A,B,C,D; [En]: A,B. ω : 1.2 [>9 storeys.]; 1 [7-9 storeys.]; 0.8 [5-6 storeys.] V10B|D= ± ω ∑(xA,xB,xC,xD) ∑xt∗5 11] Revaluation | Payback (After works) - Quantitative - Revaluation, Payback |X: [ Item-% Value] - Revaluation (% of increase): [0 … 100] - Payback (% of payback in 15 years): [1 … 100] ⋅ Benefits | Drawbacks: [Tec; Soc; Ec; En]: Benefit based on Higher Percentage ω : 1.2 [>9 storeys.]; 1 [7-9 storeys.]; 0.8 [5-6 storeys.] Rev. = ± ω x xmax ∗2.5 Payb. = ± ω x xmax ∗2.5 V11B|D= ±Rev.±Payb. 12] Management | Maintenance (After works) - Parameter, Impact, Frequency |X: [Item-Value] - Scope: (A) Management; (B) Maintenance; (C) Added services; (D) Permanent cost - Dependence: [0] None - [5] Essential - Frequency: (a) Occasional; (b) Seasonal; (c) Permanent ⋅ Benefits | Drawbacks: [Tec]: A, B |C; [Soc]: A |B,C,D; [Ec]: |B, C, D; [En]: A,B |C. ω : 1.2 [>9 storeys.]; 1 [7-9 storeys.]; 0.8 [5-6 storeys.] V12B|D= ± ω ∑(xA,xB,xC,xD) ∑xt∗5 P. Mercader-Moyano et al. Journal of Building Engineering 76 (2023) 107359 8 The design of this multi-criteria assessment system in housing renovation, specifically designed for high-rise multi-family buildings, introduces and breaks down as a novelty 12 assessment variables, organised into two types: 6 variables related to the development of the works, and 6 variables that value the result of the intervention for each renovation strategy. The criteria to identify and select 12 variables to be assessed from the 4 dimensions mentioned earlier (6 pertaining to the process of renovation works and the other 6 pertaining to the post-renovation process) follows basic housing principles established in world cities reports on the efficient management of the built environment [1,40]. The guidelines for selecting the variables are based on technical regulations, housing renovation patterns and other relevant demands from users and policy-makers [41,42]. These 12 variables are weighted independently from the 4 determined dimensions, and subsequently each variable is scaled in a range from -5 to 5, with negative values representing adversities and positive values representing benefits. When making decisions, this range allows for a much more visually comprehensible identification of what would be advantageous and disadvantageous, and a graphic output of results in which the benefits and downsides of a strategy can be seen with the visual guide of the X axis as a neutral contribution. The 12 variables are defined as follows: V1] Duration | Execution period is the total time spent in carrying out the rehabilitation works, from its start to its delivery and set-up. The execution time is quantified in a progressive range through a number of weeks from the start of the works, weighing the different dimensions according to whether they are considered benefits or drawbacks. V2] Scale | Scope of action focuses on the places where the works are carried out and correspondingly on the size and scale of said works, distinguishing between outside, the interior common spaces and the interior of the apartments, and subsequently distinguishing the level of intensity in each area. V3] Construction system | Serialisation assesses the construction system, the elements of the building in which it works and the different construction techniques, taking into account the complexity of carrying out the works. It distinguishes the benefit that serialisation and precast elements entail in large-scale buildings, and the added complexity of certain buildings that highly exceed more than 5 storeys. An evaluation of the elements of the building, construction techniques and complexity are considered and a final weighted value (wf3) is obtained. V4] Concerns | Risk Relocation considers the perceptions, preferences and worries of users during the renovation works in order to facilitate the decision-making between renovation strategies and their impact on users. Possible effects of the works, noise, inconveniences and the frequency with which they are carried out, as well as other economic consequences along with other considerations, weighting a final value that includes the advantages and disadvantages of each strategy. V5] C&D | Waste quantifies the generation of C&D waste during the works through two fundamental parameters, volume and weight, both equally weighted to obtain a final value. This variable allows us to visualise the optimization of resources according to each defined strategy. The weighting ranges chosen are based on specific databases and studies [43]. V6] Relocation | Disablement considers the impact generated in the different dimensions by the possibility of relocating users during the works, either completely to another dwelling, or by limiting the use of certain spaces or basic services and supplies for a certain period of time. This variable distinguishes between the capping frequency, which causes the variation towards higher drawbacks the longer the caps and relocations are. V7] Liveability | Comfort evaluates the impact generated after the rehabilitation works by each strategy regarding design and distribution parameters, thermal comfort, lighting, and air quality and subsequently quantifying the impact generated in each of these areas. The final weighting considers the improvement of the user’s standards as well as whether the benefits of the renewal are permanent, seasonal or occasional according to the service, the use and the day-to-day operation of the users. V8] Accessibility | Mobility assesses the improvement, or the impact generated by the works regarding mobility and accessibility conditions, both in the common spaces of the building and inside the apartments, as well as regarding safety conditions and risks in the face of accidents and falls. Being a specific tool for high-rise buildings, the guarantee of accessibility conditions must be evaluated with essential and fundamental criteria. The final value considers the impact generated in each of the highlighted areas as well as the frequency with which they are perceived from the different perspectives. V9] Energy Demand | Savings quantifies the reduction in energy demand and the percentage of energy and economic savings that the rehabilitation works entail according to each action strategy. For these energy calculations, the original state and the calculation of the demand after rehabilitation are compared and percentages of demand and savings in consumption are obtained by “DOE2” engine [44], which are equally weighted according to the ranges established below. V10] Visual change | Aesthetics focuses on assessing the visual changes to the building (pertaining to the design and its aesthetics) after the renovation works, both indoors and outdoors, as well as its possible impact on its heritage value. This parameter provides information on the quality of the final result, even more so in large-scale buildings, and the acceptance when making decisions between strategies. The quantification of this factor may be positive (benefit) or negative (drawback) depending on the final result, the type of changes, and the patrimonial protection of the building. V11] Revaluation | Payback quantifies the revaluation values of the property after the selected renovation strategy, through percentages, allowing for the incorporated benefit or drawback to be verified from different dimensions, as well as considering the possible defrayment of the costs of the works in a period of 15 years, either through energy savings, revaluation or amortization of costs. V12] Management | Maintenance considers the weighting of the benefits and drawbacks that the renovation strategy introduces in terms of management, maintenance, additional services to be contracted and other costs and expenses in rates and fees incurred by the rehabilitation works. This variable considers the dependence of the works and their necessity, from none to essential, as well as the frequency with which they occur, which may be benefits or drawbacks according to the different assessment perspectives. P. Mercader-Moyano et al. Journal of Building Engineering 76 (2023) 107359 9 Fig. 2. Urban and socioeconomic GIS maps for the selection of cases A) Argentina and B) Spain. Source: Authors. P. Mercader-Moyano et al. Journal of Building Engineering 76 (2023) 107359 16 during said works, due to the complexity in undertaking them. These variations and adjustments represent the usefulness and operability of the model designed in high-rise multi-family buildings, in such a way that small movements between dimensions and variables can be visualised in the different case studies applied by developers, public administrators or policymakers. The usefulness of showing the results on this scale and the graphic style is based on visualising the trend, variation and jumps between strategies and values for the same variable. These graphic results allow for the decision-making in high-rise buildings to be adjusted and renovation to be redirected from a multidisciplinary vision to guarantee the effective management of the built environment, as demanded by Riera et al. [55] in their study. This graphic output lets us visualise the prevalent variable among the ones Fig. 6. Results on the multidisciplinary decision-making tool according to a deep strategy. Fig. 5. Results on the multidisciplinary decision-making tool according to an intense strategy. P. Mercader-Moyano et al. Journal of Building Engineering 76 (2023) 107359 17 suggested, and the degree of benefits/drawbacks when establishing the selected renovation strategies, in line with the goals of the Sustainable Development Agenda or the European Agenda for Sustainable Buildings and concluded as a research gap in the review article developed by Nielsen et al. [56]. Lastly, the testing procedure of this multidisciplinary decision support system in both neighbourhoods from Argentina and Spain, demonstrates the operation of the assessment model for vulnerable multi-family housing, with large scale and densely occupied. This application procedure displays the weighted results between different dimensions for each of the during and after variables, thereby filling the research gap highlighted towards ensuring the decision-making and proper renovation guidelines in high-rise multi-family buildings, which was demanded by Bolis et al. [57] and Moghtadernejad et al. [58] in their research studies. 5. Conclusions This paper presents an original multidisciplinary decision support system especially focused on high-rise multi-family buildings, which supports the fulfilment of an identified lack of assessment and weighting mechanisms for the set of variables and dimensions involved when deciding which strategy or level of renovation is the most appropriate. This study also satisfies the global challenges of optimising the management of the built environment, especially demanded in high-rise multi-family buildings, considering the urban, architectural, environmental and socio-economic conditions of the building and also the increasing vulnerability scenario due to global inflation. The research contributes with a renovation assessment model that independently weights 12 variables specifically related to highrise multi-family buildings, as scope of action, classified into 6 variables linked to the process during and 6 variables linked to the process after the renovation works. The corresponding valuation parameters are based in Data Science resources, using weighting expressions to obtain normalised ranges between -5 and 5 index values. Furthermore, the originality of the model is also based on the design patterns incorporated in the graphical output of results, which allows promoters, construction firms or policy makers to visualise and identify which renovation strategy is the most optimised and viable according to the different circumstances. Additionally, the graphic output of results permit to develop a sensitivity analysis to distinguish results between the 4 established dimensions: Technical, Social, Economic and Environmental. The value of the methodological proposals is based on filling a research gap in which there are no evaluation, weighting and support mechanisms for decision-making in the urban regeneration of high-rise buildings, considering the scale of construction and the number of owners. This decision support system establishes a new way of quantifying all these variables through a particular weighting procedure for obtaining multidisciplinary indexes through 12 variables from 4 dimensions, in order to ensure a feasible and proper decision-making of different renovation proposals. In fact, the graphical output of results is already considered one of the key outcomes of this assessment model since it allows us to detect drawbacks and benefits according to dimensions and strategies. The application and testing of the model designed in 2 representative neighbourhoods with high-rise multi-family buildings, in Argentina and Spain, has generated important results outcomes that demonstrate the usefulness of the research and the validity of the model to obtain diverse, adapted and visual results, which facilitate decision-making in urban regeneration. The results have shown trends in benefits and drawbacks according to the chosen strategy in each case, and have varied, in some cases by up to 5 points of difference, between the different variables, both during and after the works, depending on the study and state of conservation of the buildings in question. As insights from the use of this model, it allows us to adequately adjust and determine the optimal cost for rehabilitation strategies, ensuring a holistic management of the built environment with application to vulnerable areas. Based on the obtained results, as the proposed intensity of the works increases, the drawbacks-benefits relationship is inverted, with an established balance in moderate strategies, and a progressive and absolute inversion in intense and deep strategies correspondingly (higher drawbacks are obtained while maximum benefits are obtained at the same time). This new assessment model and decision support system introduces important implications for policymaking in housing renovation, especially in highly occupied buildings, aiming to ensure a higher satisfaction and feasibility in the selected renovation works. As overall implications, the incorporation of this decision support system for urban administration entities, promoters and homeowners would entail important insights towards a viable, sustainable and satisfactory management of high-rise multi-family buildings. As for its limitations and future developments, this research presents and tests a model that still lacks a systematised mechanism, through digital tools or applications, that allows for the promotion of urban regeneration. However, this presents an opportunity to convert this mechanism into a digital tool that can sequence said process. This future development would also imply a mechanisation of the graphic output of results, to allow for a global use, given the flexible and open nature of the outlined methodological patterns. In addition, it would be an object of a particular study to focus more on other different types of buildings, adjust the omega ( ω ) parameter in a more exhaustive valuation study on the impact of this parameter, varying not linearly from 5 to n number of storeys, or specify different economic management models according to other ownership models and inflation rate per year for each country. Author statement Pilar Mercader-Moyano (Corresponding author): Conceptualization, Investigation, Funding acquisition, Project Administration, Supervision, Validation, Writing – Review & Editing. Patricia Camporeale: Resources, Investigation, Writing – Review & Editing. Antonio Serrano-Jim´ enez: Conceptualization, Investigation, Methodology, Data curation, Software, Formal analysis, Validation, Visualization, Writing - Original draft, Writing – Review & Editing. P. Mercader-Moyano et al. Journal of Building Engineering 76 (2023) 107359 18 Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Data availability Data will be made available on request. Acknowledgements This work was supported through funds, materials, and technical equipment as part of the 68/83 LOU contract through the project entitled: Multidimensional evaluation model for integrated urban regeneration as climate change mitigation strategies. (Contract art.68/83 with project code FIUS23/0210 and CGT:0632). Finally, the authors would also like to thank David Dolbear for the native English proofreading. References [1] United Nations, World cities report 2020, in: The Value of Sustainable Reurbanization, 2020. https://unhabitat.org/sites/default/files/2020/10/wcr_2020_ report.pdf. [2] GBCe, European Agenda for Sustainable Buildings, 2020. https://gbce.es/documentos/Agenda-de-la-UE-para-la-edificacion-sostenible.pdf. [3] D. Fatourehchi, E. Zarghami, Social sustainability assessment framework for managing sustainable construction in residential buildings, J. Build. Eng. 32 (2020), 101761, https://doi.org/10.1016/j.jobe.2020.101761. [4] P. Femenías, K. Mj¨ ornell, L. Thuvander, Rethinking deep renovation: the perspective of rental housing in Sweden, J. Clean. Prod. 195 (2018), https://doi.org/ 10.1016/j.jclepro.2017.12.282. [5] M. Nasereddin, A. Price, Addressing the capital cost barrier to sustainable construction, Dev. Built Environ. 7 (2021), 100049, https://doi.org/10.1016/j. dibe.2021.100049. [6] S.E. Bibri, J. Krogstie, M. K¨ arrholm, Compact city planning and development: emerging practices and strategies for achieving the goals of sustainability, Dev. Built Environ. 4 (2020), https://doi.org/10.1016/j.dibe.2020.100021. [7] European Commission, Building Renovation: a Kick Starter for the EU Recovery, Renov. Eur. (2020). https://www.renovate-europe.eu/wp-content/uploads/ 2020/06/BPIE-Research-Layout_FINALPDF_08.06.pdf. [8] Eurostats, Newsrealease-Euroindicators February 2019, 2019. https://ec.europa.eu/eurostat/documents/2995521/9967985/3-10072019-BP-EN.pdf/ e152399b-cb9e-4a42-a155-c5de6dfe25d1. [9] A. Invidiata, M. Lavagna, E. Ghisi, Selecting design strategies using multi-criteria decision making to improve the sustainability of buildings, Build. Environ. 139 (2018) 58–68, https://doi.org/10.1016/j.buildenv.2018.04.041. [10] P. Mercader-Moyano, O. Morat, A. Serrano-Jim´ enez, Urban and social vulnerability assessment in the built environment: an interdisciplinary indexmethodology towards feasible planning and policy-making under a crisis context, Sustain. Cities Soc. 73 (2021), https://doi.org/10.1016/j.scs.2021.103082. [11] OECD, GDP, (2021). https://data.oecd.org/gdp/gross-domestic-product-gdp.htm. [12] T. Bl´ azquez, R. Su´ arez, S. Ferrari, J.J. Sendra, Addressing the potential for improvement of urban building stock: a protocol applied to a Mediterranean Spanish case, Sustain. Cities Soc. 71 (2021), https://doi.org/10.1016/j.scs.2021.102967. [13] G. Napoli, M. Bottero, G. Ciulla, F. Dell’Anna, J.R. Figueira, S. Greco, Supporting public decision process in buildings energy retrofitting operations: the application of a Multiple Criteria Decision Aiding model to a case study in Southern Italy, Sustain. Cities Soc. 60 (2020), 102214, https://doi.org/10.1016/j. scs.2020.102214. [14] K. Fars¨ ater, P. Strandberg, Å. Wahlstr¨ om, Building status obtained before renovating multifamily buildings in Sweden, J. Build. Eng. 24 (2019), 100723, https:// doi.org/10.1016/j.jobe.2019.02.015. [15] A. Ibarloza, E. Malles, E. Ibarloza, I. Heras-Saizarbitoria, The needs and effects of housing renewal policies in Spain: implications for sustainability and accessibility, Sustain. Cities Soc. 40 (2018) 244–253, https://doi.org/10.1016/j.scs.2018.01.037. [16] E. L´ opez-García, J. Lizana, A. Serrano-Jim´ enez, C. Díaz-L´ opez, ´ Angela Barrios-Padura, Monitoring and analytics to measure heat resilience of buildings and support retrofitting by passive cooling, J. Build. Eng. 57 (2022), https://doi.org/10.1016/j.jobe.2022.104985. [17] F. Taillandier, L. Mora, D. Breysse, Decision support to choose renovation actions in order to reduce house energy consumption ??? An applied approach, Build. Environ. 109 (2016) 121–134, https://doi.org/10.1016/j.buildenv.2016.09.019. [18] I.E. Aigwi, T. Egbelakin, J. Ingham, R. Phipps, J. Rotimi, O. Filippova, A performance-based framework to prioritise underutilised historical buildings for adaptive reuse interventions in New Zealand, Sustain. Cities Soc. 48 (2019), 101547, https://doi.org/10.1016/j.scs.2019.101547. [19] United Nations, Transforming Our World: the 2030 Agenda for Sustainable Development, 2021, https://doi.org/10.1201/b20466-7. A/RES/70/1. [20] Government of Spain, Spanish Regulation, 09/2022 Law, 14th June, on Quality in the Architecture, 2022. https://www.boe.es/buscar/act.php?id=BOE-A- 2022-9837. [21] P. Perera, K. Hewage, M.S. Alam, W. M` erida, R. Sadiq, Scenario-based economic and environmental analysis of clean energy incentives for households in Canada: multi criteria decision making approach, J. Clean. Prod. 198 (2018) 170–186, https://doi.org/10.1016/j.jclepro.2018.07.014. [22] B. Liu, D. Rodriguez, Application of multi-objective optimization model to assess the energy efficiency measures for the cases of Spain, J. Build. Eng. 38 (2021), 102144, https://doi.org/10.1016/j.jobe.2020.102144. [23] X. Zhong, W. Wu, I.A. Ridley, Assessing the energy and indoor-PM2.5-exposure impacts of control strategies for residential energy recovery ventilators, J. Build. Eng. 29 (2020), 101137, https://doi.org/10.1016/j.jobe.2019.101137. [24] J. Garrido-pi˜ nero, P. Mercader-moyano, EIAMUO methodology for environmental assessment of the post-war housing estates renovation : practical application in Seville (Spain), Environ. Impact Assess. Rev. 67 (2017) 124–133, https://doi.org/10.1016/j.eiar.2017.09.004. [25] B.A. Timur, T. Bas¸aran, B. ˙ Ipeko˘ glu, Thermal retrofitting for sustainable use of traditional dwellings in Mediterranean climate of southwestern Anatolia, Energy Build. 256 (2022), 111712, https://doi.org/10.1016/j.enbuild.2021.111712. [26] A. Serrano-Jim´ enez, B. Bland´ on-Gonz´ alez, ´ A. Barrios-Padura, Towards a built environment without physical barriers: an accessibility assessment procedure and action protocol for social housing occupied by the elderly, Sustain. Cities Soc. 76 (2022), https://doi.org/10.1016/j.scs.2021.103456. [27] P.E.D. Love, J. Matthews, When ‘less is more’: the rationale for an adaptive toolbox to manage the risk and uncertainty of rework, Dev. Built Environ. 12 (2022), 100084, https://doi.org/10.1016/j.dibe.2022.100084. [28] J. Ge, S. Li, S. Chen, X. Wang, Z. Jiang, C. Shen, Energy-efficiency strategies of residential envelope in China’s Hot Summer–Cold Winter Zone based on intermittent thermal regulation behaviour, J. Build. Eng. 44 (2021), 103028, https://doi.org/10.1016/j.jobe.2021.103028. [29] K. Mj¨ ornell, P. Femenías, K. Annadotter, Renovation strategies for multi-residential buildings from the record years in Sweden—profit-Driven or socioeconomically responsible? Sustainability 11 (2019) 6988, https://doi.org/10.3390/su11246988. [30] H. Li, X. Zhang, S.T. Ng, M. Skitmore, Quantifying stakeholder influence in decision/evaluations relating to sustainable construction in China – a Delphi approach, J. Clean. Prod. 173 (2018) 160–170, https://doi.org/10.1016/j.jclepro.2017.04.151. P. Mercader-Moyano et al. Journal of Building Engineering 76 (2023) 107359 19 [31] S. Moghtadernejad, L.E. Chouinard, M.S. Mirza, Enhanced façade design: a data-driven approach for decision analysis based on past experiences, Dev. Built Environ. 5 (2021), 100038, https://doi.org/10.1016/j.dibe.2020.100038. [32] Z. Huang, Z. Gou, B. Cheng, An investigation of outdoor thermal environments with different ground surfaces in the hot summer-cold winter climate region, J. Build. Eng. 27 (2020), 100994, https://doi.org/10.1016/j.jobe.2019.100994. [33] A. Giretti, A. Corneli, B. Naticchia, A decision support system for scenario analysis in energy refurbishment of residential buildings, Energies 14 (2021), https:// doi.org/10.3390/en14164738. [34] S.N. Kamaruzzaman, E.C.W. Lou, P.F. Wong, R. Wood, A.I. Che-Ani, Developing weighting system for refurbishment building assessment scheme in Malaysia through analytic hierarchy process (AHP) approach, Energy Pol. 112 (2018) 280–290, https://doi.org/10.1016/j.enpol.2017.10.023. [35] M. Monz´ on, B. L´ opez-Mesa, Buildings performance indicators to prioritise multi-family housing renovations, Sustain. Cities Soc. 38 (2018) 109–122, https://doi. org/10.1016/j.scs.2017.12.024. [36] S. Chang, D. Castro-Lacouture, Y. Yamagata, Decision support for retrofitting building envelopes using multi-objective optimization under uncertainties, J. Build. Eng. 32 (2020), 101413, https://doi.org/10.1016/j.jobe.2020.101413. [37] S. Olsson, T. Malmqvist, M. Glaumann, An approach towards sustainable renovation-A tool for decision support in early project stages, Build. Environ. 106 (2016) 20–32, https://doi.org/10.1016/j.buildenv.2016.06.016. [38] M. Cinelli, S.R. Coles, K. Kirwan, Analysis of the potentials of multi criteria decision analysis methods to conduct sustainability assessment, Ecol. Indicat. 46 (2014) 138–148, https://doi.org/10.1016/j.ecolind.2014.06.011. [39] R. Alawneh, F. Ghazali, H. Ali, A.F. Sadullah, A Novel framework for integrating United Nations Sustainable Development Goals into sustainable non-residential building assessment and management in Jordan, Sustain. Cities Soc. 49 (2019), 101612, https://doi.org/10.1016/j.scs.2019.101612. [40] United Nations, New Urban Agenda, 2017. www.habitat3.org. [41] European Commission, Proposal for an European Climate Law: Establishing the Framework for Achieving Climate Neutrality and Amending Regulation (EU) 2018/1999, Eur. Com (2020). https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:52020PC0080. [42] IEA, Global, Energy and CO2 status report 2017, Glob. Energy CO2 Status Rep. 2017 (2017). 1. 6-10.https://www.iea.org/publications/freepublications/ publication/GECO2017.pdf. [43] WBDG, Construction Waste Management Database, 2018. https://www.wbdg.org/additional-resources/tools/construction-waste-management-database. [44] DOE2, (2017). www.doe2.com. [45] N. Vafaei, R. Ribeiro, L. Camarinha-Matos, Selection of normalization technique for weighted average multi-criteria decision making, in: Technol. Innov. Resilient Syst. DoCEIS 2018, 2018, https://doi.org/10.1007/978-3-319-78574-5_4. [46] C. Delmastro, G. Mutani, S.P. Corgnati, A supporting method for selecting cost-optimal energy retrofit policies for residential buildings at the urban scale, Energy Pol. 99 (2016) 42–56, https://doi.org/10.1016/j.enpol.2016.09.051. [47] C. Díaz-L´ opez, A. Serrano-Jim´ enez, K. Verichev, ´ A. Barrios-Padura, Passive cooling strategies to optimise sustainability and environmental ergonomics in Mediterranean schools based on a critical review, Build. Environ. 221 (2022), https://doi.org/10.1016/j.buildenv.2022.109297. [48] P. Mercader-Moyano, M. Flores-García, A. Serrano-Jim´ enez, Housing and neighbourhood diagnosis for ageing in place: multidimensional assessment system of the built environment (MASBE), Sustain. Cities Soc. 62 (2020), 102422, https://doi.org/10.1016/j.scs.2020.102422. [49] V. Equere, P.A. Mirzaei, S. Riffat, Definition of a new morphological parameter to improve prediction of urban heat island, Sustain. Cities Soc. 56 (2020), https://doi.org/10.1016/j.scs.2020.102021. [50] J. Lizana, ´ A. Barrios-Padura, M. Molina-Huelva, R. Chacartegui, Multi-criteria assessment for the effective decision management in residential energy retrofitting, Energy Build. 129 (2016) 284–307, https://doi.org/10.1016/j.enbuild.2016.07.043. [51] CONEVAL, Indicators Report on Social Development Programme 2020, 2020. https://www.coneval.org.mx/Paginas/principal.aspx. [52] AENOR, Norma UNE 41805, Building Diagnosis, Espa˜ na, Madrid, 2015, 2015. [53] IECA, Instituto Nacional de Estadística y Cartografía de Andalucía, Distribuci´ on espacial de la poblacion en Andalucía, 2016. [54] World Urban Database and Access Portal Tools, World Urban Database, 2022. https://www.wudapt.org/. [55] M.G. Riera P´ erez, M. Laprise, E. Rey, Fostering sustainable urban renewal at the neighborhood scale with a spatial decision support system, Sustain. Cities Soc. 38 (2018) 440–451, https://doi.org/10.1016/j.scs.2017.12.038. [56] A.N. Nielsen, R.L. Jensen, T.S. Larsen, S.B. Nissen, Early stage decision support for sustainable building renovation - a review, Build. Environ. 103 (2016) 165–181, https://doi.org/10.1016/j.buildenv.2016.04.009. [57] I. Bolis, S.N. Morioka, L.I. Sznelwar, Are we making decisions in a sustainable way? A comprehensive literature review about rationalities for sustainable development, J. Clean. Prod. 145 (2017) 310–322, https://doi.org/10.1016/j.jclepro.2017.01.025. [58] S. Moghtadernejad, L.E. Chouinard, M.S. Mirza, Multi-criteria decision-making methods for preliminary design of sustainable facades, J. Build. Eng. 19 (2018) 181–190, https://doi.org/10.1016/j.jobe.2018.05.006. P. Mercader-Moyano et al.