Evaluation model of the economic-environmental impact on housing recovery. Application in the city of Seville, Spain

1 Evaluation model of the economic-environmental impact on housing recovery. Application in the city of Seville, Spain. Abstract The recovery of dwellings, understood as the repair of severely damaged housing, it is of great importance at a European level due to the large number of neighbourhoods in need of improvements in comfort, structural integrity, and accessibility. The proposed model, based on the ecological footprint (EF) indicator and the amounts of resources required as expressed in the project budget, provides the necessary quantification for the evaluation of the economic-environmental impact, while simultaneously evaluating the viability of the intervention. The model facilitates the comparison of the most sustainable and competitive solutions; hence it can be employed to establish resilient policies based on sustainable planning. Eight practical projects are analysed in Seville, Spain. At the level of material results, the impact can be reduced by 80%, depending on the potential for recycling and recovery of the materials. The foundation and structure chapters have the greatest impact with 30% of the total EF. With the energy improvements implemented, an average reduction of 60% in CO2 emissions is achieved. From the results produced by the model, it is possible to clearly identify the elements on which to act to achieve a significant reduction in the EF of the project. Keywords: ecological footprint; building recovery, economic-environmental assessment; policy planning. 1. INTRODUCTION The current need for regeneration of urban centres accentuates the debate on the choice between either the demolition and new construction of buildings or their recovery (Girardet, 2017). Specialists in the matter at a European level have entered this debate and focus not only on 1political and environmental factors, but also on social factors. Power (2010) highlights the benefits of the regeneration and rehabilitation of neighbourhoods, which manage to prevent cities from expanding unnecessarily and neighbourhoods from being abandoned and degraded, , and offers an alternative to the generation of large amounts of waste and emissions due to demolition and new construction. 2 Assefa and Ambler (2017), in a more recent study, analyse the reduction in the consumption of energy and resources that can be achieved by recovering building waste subsequent to demolition through material reuse and recycling. Another alternative concept involves understanding existing buildings as banks of materials (Rose & Stegemann, 2018); this system would enable the assessment of the value of materials that can be reused, repurposed, and/or upcycled in new projects. This gives rise to the concept of the circular economy which aims to implement a production model based on regeneration and restoration, through the recirculation of the resources in the production system with minimal or no loss of quality (Ellen MacArthur Foundation, 2013). The main strategy to close material loops for buildings at the end of their useful life is that involving reuse. For buildings, this implies that refitting and refurbishment are prioritised over demolition and a total rebuild (van den Berg, Voordijk, & Adriaanse, 2020). The European Commission (European Commission, 2018) focuses its current strategies and policies on the circular economy through the Circular Economy Action Plan. In said Plan, a series of actions focused on promoting the use of secondary raw materials as a basis for consumption, production and waste management are developed. Within these actions, the priority sectors of action are established, among which is the construction sector (European Commission, 2015). According to experts such as Geissdoerfer et al. (2017), the circular economy, based on the minimisation and closure of the cycles of materials and energy, is a regenerative system. Urban recovery and regeneration assume a fundamental role in the path towards sustainable construction, and hence authors such as Schwartz, Raslan and Mumovic (2018) have made a systematic review of 251 case study buildings from 19 different countries. This review is carried out by comparing carbon footprint calculations of rehabilitated and new construction buildings. The authors conclude that, on the current basis, it remains impossible to conclusively determine which is the best alternative. The results of this study justify the need to continue advancing in the creation of models for the evaluation of the sustainability of urban renewal. Almeida, Ramos, and Silva (2018) compare the contribution of various sustainability quantification models with the new European urban regeneration policies. Weiler et al. (2017) have developed a 3 comprehensive method for the evaluation of the life cycle of buildings and the comparison of demolition and reconstruction against refurbishment. Other researchers, through their methods related to the calculation of the impact throughout the life cycle of buildings, have obtained similar conclusions: that in order to reduce the energy consumed, it is necessary to raise the quality standards in the rehabilitation of buildings, otherwise, with the current quality standards, the consumption of both the rehabilitated buildings and the new buildings continue to be excessive (Alba-Rodríguez, 2016; Weiler et al., 2017; Ruiz-Pérez et al., 2019). In addition to the energy assessment, there is currently a tendency to apply environmental indicators based on simpler methodologies that are understandable by society in general, as is the case of the ecological footprint indicator (EF), introduced in the 90s by Wackernagel and Rees (1997). The EF is employed to quantify the pressure suffered in the environment due to human activity, by analysing the capacity of said environment to provide resources (feed, grain, fish, firewood, and urban land) and absorb CO2 (Galli, Wackernagel, Iha, & Lazarus, 2014; Wackernagel et al., 1999). However, several authors question its scientific value, since they consider it to be too complex a process to be subjected to such a drastic simplification (Giampietro and Saltelli, 2014; van den Bergh and Grazi, 2014; van den Bergh and Verbruggen, 1999). Despite these weaknesses numerous current studies continue to use EF as an indicator for the assessment of sustainability, which demonstrates its validity. Thus, Khan et al. (2022) and Bilal et al. (2022) used variables, such as government expenditures, inflation dynamics, and economic growth, to calculate the impact of alternative energy sources on Germany’s environmental quality (from 1971 to 2016). The results indicated that alternative energy sources, government expenditures, and inflation were negatively associated, while economic growth was positively associated with the level of environmental quality. Khan et al. (2021) investigated the impact of energy use and urbanisation on China's ecological footprint and CO2 emissions (1971-2016). The results revealed that both parameters deteriorated the level of environmental quality in China, while others, such as international trade, improved this quality. Zahoor et al. (2022) examined the impact of investment into clean energy, financial development, manufacturing added value, and of urbanisation 4 on environmental sustainability (EF and CO2 emissions) and economic growth in China, in the period 1970- 2016. The results determined that investment in clean energy was negatively associated with CO2 emissions and EF, while it was positively associated with economic growth in China. Moreover, financial development, manufacturing added value, and urbanisation were positively associated with CO2 emissions, EF, and economic growth. The EF of the construction sector is due to three basic components of every construction project: material, machinery, and workforce. The materials and machinery leave mostly an energy footprint, but workforce additionally generates a pastures, fisheries, and cropland footprint due to their energy source being that of food intake. For their corresponding resource inventory, the cost control that takes place in reconstruction or renovation projects can be used, and is accompanied by quantity surveying (Alba-Rodríguez, Machete, Glória Gomes, Paula Falcão, & Marrero, 2021; Pereira, Rivero, Gomes, Rodrigues, & Marrero, 2021). In an innovative way, it can be an instrument of environmental control (María Rocío Ruiz-Pérez, Alba-Rodríguez, Rivero-Camacho, Solís-Guzmán, & Marrero, 2021). To this end, it is necessary to define construction cost databases of the work units that also include the environmental assessment of the resources (Marrero, Rivero-Camacho, Martínez-Rocamora, Alba- Rodríguez, & Solís-Guzmán, 2022). The present work defines a recovery cost database which forms an integral part of the calculation model proposed. The building typology utilised as a reference in the creation of the work units or elements of the database is that of a residential building of three to four storeys above ground, which constitutes the most representative typology in the city of Seville (Salas, 2013). The model is applied in the evaluation of the impact of the recovery of a real-life case in Seville, Spain, upon which eight recovery projects are generated as theoretical scenarios. The methodology is sensitive to the damage levels of the building elements and to the materials employed or recovered, and it provides a tool for the prediction of cost and environmental impact of projects involving the recovery of buildings. The strongest aspect of the EF calculation methodology is that it 5 is based on the project budget, which is a contractual document that construction agents and technicians work with on a daily basis. Furthermore, the recovery cost database is a living document that can be adapted and transformed to cater for changes in typologies of building and technological changes. 2. METHODOLOGY 2.1. System borders The recovery of the buildings is required when the buildings face such a level of deterioration of their functionality that it exerts a negative impact on the quality of life of the tenants. For a clear definition of the extent of the problem, it is necessary to indicate the sources of impact used in the economic and environmental evaluation of the recovery phase. According to the UNE-EN 15978 standard (Sustainability of construction works. Assessment of environmental performance of buildings. Calculation Method (UNE-EN15978, 2012)), a building ceases to be habitable when it requires major recovery intervention, whereby significant changes must be made in its facilities, energy demand, materials, and even in its use. With these changes made, the building should be considered a reclaimed building that has begun a new useful life (M.E.Spain, 2003). The transversal limits are defined as the elements that interact with the building in the recovery phase, which are the industry, the occupants and the building itself (Fig. 1). The manufacture of effects, such as electrical appliances, furniture, and decoration, belongs to the industry, and hence the impacts produced by their manufacture must be allocated when calculating the impact of the industry to which it corresponds. The impacts caused by mobility, food consumption, and municipal solid waste (MSW) produced by the occupants are not attributable to the building, since they are not a direct consequence thereof, but rather belong to the impact of the occupants themselves. Finally, utility consumption (water, electricity), the workforce, materials, the construction and demolition waste (CDW), and the machinery necessary for the recovery work to be carried out are attributed to the building project. 6 Figure 1. System boundaries, adapted from (Marrero et al., 2020) During the year that the recovery work is undertaken, it is considered that the only activity carried out on the site is that of the recovery work. The electric power and water consumptions are estimated from empirical data of similar building projects. Other impacts are determined from the quantification of resources of the project budget. 2.2. EF Methodology in recovery For the quantification and subsequent evaluation of the environmental impact, the quantification with which each basic resource intervenes in the project is extracted from the budget. These include: construction materials (electricity or fuel of production processes, wooden materials, and the transport of materials); construction and demolition waste (CDW), which is generated by losses in the materials used in the recovery (due to breakage, cut-outs, etc.) and in demolition processes; the workforce (food and the generation of MSW); and machinery (fuel or electricity consumption). In Fig. 2, the different 7 types of EF (forests, pastures, fisheries, cropland, fossil energy, and built land), which, by aggregation, enable the total EF of the recovery project to be determined. The EF indicator has been used by other researchers to study the impact of buildings (Bin & Parker, 2012; Zhao & Mao, 2013). The authors Solís-Guzmán, Marrero, & Ramírez-de-Arellano (2013) and their team have developed a methodology to determine the EF to assess the life cycle of the building, starting with the land urbanisation (Marrero, Puerto, Rivero-Camacho, Freire-Guerrero, & Solís- Guzmán, 2017), continuing with the use (Martínez-Rocamora, Solís-Guzmán, & Marrero, 2016), maintenance (Alejandro Martínez-Rocamora, Solís-Guzmán, & Marrero, 2016) and rehabilitation phases, and/or the eventual demolition of the building (Alba-Rodríguez et al., 2017). For the evaluation of the construction materials, the Ecoinvent (A. Martínez-Rocamora, Solís-Guzmán, & Marrero, 2016) database is used herein to extract the life cycle assessment data (LCA) of the materials, due to its wide variety of data from the construction industry and its consistency and transparency in process development. Since LCA databases provide data on CO2 emissions per kg of material, in order to obtain the EF, the basic units (m2, m3, metres, thousands of units, tons, etc.) are transformed into weight (kg). The IPCC 100a (IPCC, 2006) methodology is employed to analyse the life cycle inventory of materials and to obtain the amount of CO2 emissions. Once all the CO2 emissions of each material have been calculated, then Equation 1 of Figure 2 is applied in order to determine the EF. Wood productivity defines the EF of forests which is generated by the consumption of wooden materials, and is calculated according to its typology and the forest equivalence factor (Freire- Guerrero, Alba-Rodríguez, & Marrero, 2019) (Equation 2 of Fig. 2). For the calculation of the EF for the transport of materials, a series of specific assumptions are established regarding transport distances, consumption, and emissions (Alba-Rodríguez et al., 2017) (see Equation 3 of Fig. 2). The evaluation of the machinery utilised in the handling of CDW and its transport to the treatment plant is included in the analysis of the remaining machines as described in the previous section (Equation 3 of Fig. 2) (Marrero et al., 2020). 8 Figure 2. EF methodology in recovery projects The impacts produced by the workers involve the consumption of food and the generation of urban solid waste. One of the novelties of the model applied in this work is that the food footprint is updated based on new studies and publications on food EF in Spain, and the data from the Ministry of the Environment in relation to the environmental impacts and aspects necessary for the calculation of EF of MSW is applied (Almasi & Milios, 2013; EUROSTAT, 2015; IDAE, 2011). The EF generated according 9 to the type of food (pasture, sea, and cropland) is calculated by taking into account the diet (Grunewald, Galli, Katsunori, Halle, & Gressot, 2015), natural productivity, and the factor of equivalence of each productive territory (Equation 4 of Fig.2) (María Rocío Ruiz-Pérez, Alba-Rodríguez, Castaño-Rosa, Solís-Guzmán, & Marrero, 2019). To obtain the total weight (t) of the MSW produced, a coefficient is applied that indicates the average generation of MSW per worker (EUROSTAT, 2015) (Equation 5 of Fig.2). The use of machinery, depending on the power of its engine, generates a footprint derived from its consumption of fossil fuel and/or electricity. The EF of the fossil-fuel consumption is obtained from Equation 6 of Fig. 2. The total kW consumed from electrical machinery is obtained from the analysis of motor power and hours of use. CO2 emissions from machinery consumption (Equation 6 of Fig. 2) are obtained using as a reference the emissions produced in the Spanish electricity system by the production of 1kWh (REE, 2014). The energy and water consumption of the work is obtained from the indirect costs of the project, and hence the quantification of consumption together with the annual hours of labour allocated to the indirect costs generate the total kWh/year needed to calculate the EF. In the calculation of the EF of electricity consumption, the reduction of emissions due to carbon capture in the oceans (GFN, 2014) and the emissions of the energy mix of Spain (REE, 2014) are included (Equation 7 of Fig. 2) (Freire Guerrero & Marrero, 2015). The calculation for the water consumed is transformed in terms of energy and CO2 emissions, as indicated in Equation 8 of Fig. 2, by using official data (EMASESA, 2005). The occupied land is taken into account in the calculation of the EF, because once the land is urbanised, it is considered biologically unproductive. Of the two possible types of soil that are defined herein for the EF indicator, forests and farmland, the area occupied by the building is considered as corresponding to farmland (Solís-Guzmán et al., 2013), in accordance with Equation 9 of Fig. 2. The area of land occupied corresponds to that indicated in the project. Finally, all productive land in hectares is translated into global hectares (gha) by means of equivalence factors that allow different territories to be combined in one single result. 16 Lime 8.17 3.08 6.9E-04 2.61 0.98 2.2E-04 Aggregates 7.21 1.11 2.5E-04 12.59 26.10 5.9E-03 Polyethylene 1.52 9.44 2.1E-03 1.69 10.74 2.4E-03 PVC 1.17 6.14 1.4E-03 1.04 5.63 1.3E-03 Rubber 0.95 6.82 1.5E-03 0.65 4.60 1.0E-03 Aluminium 0.86 12.37 2.8E-03 1.53 22.44 5.1E-03 Wood 0.79 0.97 2.2E-04 0.01 0.01 2.3E-06 Polystyrene 0.74 6.82 1.5E-03 0.00 0.01 2.3E-06 Others 9.00 0.08 1.8E-05 3.12 0.11 2.5E-05 TOTAL 425.60 190.52 4.3E-02 286.22 242.15 5.5E-02 D_02 D_04 Material Weight/m2 (kg/m2) EF energy (gha) EF energy/m2 (gha/m2) Weight/m2 (kg/m2) EF energy (gha) EF energy/m2 (gha/m2) Sand 144.52 0.68 1.53E-04 82.60 0.39 8.78E-05 Cement 90.41 27.60 6.22E-03 54.89 16.25 3.66E-03 Concrete 48.81 0.87 1.96E-04 11.69 0.07 1.58E-05 Ceramics 46.81 53.16 1.20E-02 20.29 22.61 5.09E-03 Steel 16.00 85.21 1.92E-02 8.41 43.29 9.75E-03 Paints 1.16 8.57 1.93E-03 1.82 9.15 2.06E-03 Lime 1.70 0.64 1.44E-04 2.07 0.78 1.76E-04 Aggregates 7.51 0.67 1.51E-04 8.83 0.35 7.88E-05 Polyethylene 1.17 7.59 1.71E-03 0.05 4.14 9.32E-04 PVC 1.20 6.67 1.50E-03 0.58 3.19 7.18E-04 Rubber 0.75 5.34 1.20E-03 0.47 3.37 7.59E-04 Aluminium 0.54 7.83 1.76E-03 0.47 6.81 1.53E-03 Wood 0.13 0.14 3.15E-05 0.01 0.10 2.25E-05 Polystyrene 0.74 6.82 1.54E-03 0.00 0.10 2.25E-05 Others 2.20 0.08 1.80E-05 1.14 0.05 1.13E-05 TOTAL 363.66 211.87 4.77E-02 193.31 110.65 2.49E-02 Table 3. Reduction of the energy EF of the most significant material families (GF + 02) D_1 D_3 Material W EFe/m2 % EF total EFe/m2 reduced % reduction W EFe/m2 % EF total EFe/m2 reduced % reduction Steel 13.26 0.01238 29 0.00740 40 17.70 0.02364 43 0.01838 22 Cement 92.30 0.00613 14 0.00508 17 83.74 0.00588 11 0.00532 9 Ceramics 26.32 0.00283 7 0.00055 81 28.73 0.00635 12 0.00353 45 Result with respect to total 0.02134 50 0.0359 66 D_2 D_4 Material W EFe/m2 % EF total EFe/m2 reduced % reduction W EFe/m2 % EF total EFe/m2 reduced % reduction Steel 16.00 0.01919 40 0.01312 32 8.41 0.00975 39 0.00826 15 Cement 90.41 0.00622 13 0.00538 13 54.89 0.00366 15 0.00342 6 Ceramics 46.81 0.01197 25 0.00440 63 20.29 0.00509 20 0.00354 31 Result with respect to total 0.0374 78 0.0185 74 W: weight (kg/m2) Efe/m2: EF energy (gha/m2) 3.1. Economic-environmental comparison: As a validation of the results obtained after the application of the model developed in the present work, we proceed to compare the total EF (gha/m2) and the economic cost (€/m2) of the recovery projects analysed with the results from research carried out by various authors. Ruiz-Pérez et al. (2019) analyse 50 rehabilitated homes in the historical centre of Seville and obtain an average impact in terms of an EF of 0.068 gha/m2 (Table 4). The EF results obtained in the present work lie within this range, since the EF values in the eight recovery projects studied herein range between 0.039 gha/m2 and 0.087 gha/m2. Regarding the economic results, these are compared with those obtained by other authors from the application of the EF calculation model to a residential building affected by structural 17 damage (Alba-Rodríguez et al., 2017). In this case, the economic cost of recovery was 417.02 €/m2, and hence the economic results obtained in this study for the eight projects analysed (between 588.01 euros/m2 and 321.13 euros/m2) (Table 4) are in the same order of magnitude. Table 4. Total EF (gha/m2) and total economic cost (€/m2) of the recovery projects in terms of floor area. Economic cost and total EF of the recovery projects Project GF+02 GF+03 Total cost (€/m2) Total EF (gha/m2) Total cost (€/m2) Total EF (gha/m2) D_01 577.81 0.076 521.80 0.064 D_02 588.01 0.082 530.32 0.070 D_03 516.39 0.087 470.35 0.060 D_04 321.13 0.046 289.27 0.039 In Figure 6, a comparison is made, both economic and environmental, of the results per constructionsite chapter of the eight scenarios analysed. The chapter on foundations stands out in terms of environmental impact given the high consumption of cement, derived from the high demand for grouting during the micropiling process. In contrast, the economic results are not significant in this chapter. Figure 6. Economic-environmental comparison per chapter per m2. 18 The correlation of damage can be seen in the results since the high levels of damage to the foundation translate into the need for intervention to repair fissures and cracks both in interior partitions and in exterior enclosures. This can be clearly observed in Figure 6, where the masonry chapter shows one of the highest EF impacts in the scenarios analysed. The high consumption of energy necessary for the production of ceramic materials, used in the repair of damaged masonry elements, explains the high impacts of the masonry chapter. In the D_01 scenario, for both GF+03 and GF+02 building typologies (GF: ground floor), the installations cause the greatest economic impact: this is the third most important chapter with respect to the energy EF. The chapters with the highest environmental impact are those of foundations and masonry. In the D_02 scenario, the chapter that causes the greatest environmental impact in GF+03 is that of masonry; however, in GF+02, it is the foundations (Figure 6). However, in the D_03 project, for both typologies (GF+03 and GF+02), it is the installations that have the highest economic impact, (Figure 6), which is due to the installation of a new heating system with radiators powered by solar thermal energy. In the D_04 project, with less pathological damage than those above, both typologies (GF+03 and GF+02) have a high environmental impact in the foundation chapter (Figure 6). The large consumption of cement grout required in the foundation micropiling process is the cause of this high consumption. Moreover, the roof repair is significant in terms of EF due to its importance in the building damage. 3.2. Energy-efficiency improvement comparison: recovery vs. initial state Another important effect of building rehabilitation involves the new energy consumption in the new stage of the building life cycle: the building usage. The aim of this section is to comparatively analyse the energy consumption of the damage level repair. The DesignBuilder building energy simulation software (Tindale, 2005) was used for this purpose. It integrates the EnergyPlus calculation engine, which is internationally the most widely used engine for running dynamic simulations (Haves et al., 2019). On defining the building (construction characteristics, location, orientation, climate, internal 19 loads, mechanical systems, installations, etc.), the program calculates the loads necessary to maintain thermal comfort. The results obtained from the energy simulation are shown in Figure 7. The results obtained in the simulation have yielded heating and cooling data in terms of energy consumed and EF emissions. In their initial states (both for D_01 & D_03 and for D_02 & D_04), they are represented in the graph of Figure 7 as 100% of consumption in order to compare how consumption and EF vary in their recovered states subsequent to the energy improvement actions. It can be observed that after the energy improvement actions, cases D_01 and D_02 manage to reduce their annual heating consumption by approximately 19% and the refrigeration by around 22%. Cases D_03 and D_04 manage to reduce their annual heating consumption by 35% and 38%, respectively; however, the reduction in refrigeration consumption remains practically negligible. This is due to the fact that in cases D_01 and D_02, a comprehensive rehabilitation of all their systems and envelope elements (the enclosures and roofs are fully improved) has been carried out, while in cases D_03 and D_04, there is only the repair of the exterior enclosures, without improving the efficiency. The focus was on actions to improve energy efficiency placed only on the exterior carpentry and installations. These results are reflected in the proportionality of the resulting annual EF. These results show how the energy improvement actions carried out during the recovery of buildings should seek comprehensive energy improvement, both of its envelope elements and of the heating and cooling systems that serve the building, in order to achieve a significant annual reduction in heating and cooling consumption together with an annual reduction in the EF of energy. The main strength of the proposed methodology is that it allows the creation of environmental budgets, based on the same classification and systematisation of economic budgets, with the incorporation of indicators such as the EF. However, a specific aspect of building recovery, one of the major weaknesses of the model proposed, is that the work unit definition is singular in many cases and is not included in typical construction cost databases. The work units can be specific to a period of construction, usage, severity of damage, etc. Other influential aspects include structure composition 20 and construction materials and previous renovations undergone during distinct periods. This complicates the establishment of general work units that would be valid for many buildings. Other work units, such as the replacement work of electric, domestic hot water or air-conditioning installations, are similar to those that take place in new or renovated buildings, which enables the adaptation of their databases. Figure 7. Energy consumption and CO2 emissions comparison: recovery vs. initial state. 4. CONCLUSION The proposed model, based on the ecological footprint (EF) indicator and the amounts of resources expressed in the project budget, provides the necessary quantification for the evaluation of the economic-environmental impact. The methodology facilitates the comparison of the most sustainable and competitive solutions; hence it can be used to establish policies of a more resilient nature based on sustainable planning. The proposal integrates the environmental variable in a highly intuitive way through the structure of the construction budget. To this end, it is necessary to generate a construction cost database of the 21 recovery-renovation activities that include the environmental impacts of its three components, materials, machinery, and workforce. This integration provides the key to this decision-making process for the reduction of the impact of the intervention. In addition, the calculation model facilitates the rapid identification of the materials that generate the greatest impact, this makes the proposed model a key tool for the promotion of growth based on the circular economy, which optimises the use of available resources, thereby encouraging the revaluation and reuse of waste. From the application of the proposed model to eight cases, various conclusions can be drawn. On analysing the results of the materials used in the different cases there are three materials that show a higher EF impact in all cases: cement, steel, and ceramic materials. Of the results in terms of chapter (construction-site stage), three of the eight cases studied show the most economically important chapter to be that of installations, with percentages at approximately 30%. The total impact can be reduced by 80% depending on the potential for recycling and recovery of the materials. The foundation and structure chapters exert the greatest impact with 30% of the total EF. With the energy improvements implemented, an average reduction of 60% in CO2 emissions is achieved. From the results produced by the model, it is possible to clearly identify the elements on which to act to achieve a significant reduction in the EF of the project. The application of the model reveals that it is capable of analysing the viability of the recovery of the building from a technical, economic, and environmental perspective, and demonstrates sensitivity to modifications in the degrees of pathological severity and to modifications in the characteristics of the constructive solutions. The model is robust, since it is supported by reliable and proven tools: the system of the construction cost databases and the EF methodology adapted to building construction. Together, these methodologies render the evaluation simple and schematic. Research is currently being carried out on the combination of the EF with other indicators, such as HH (water footprint) in buildings, always throughout the entire life cycle of buildings. The future objective involves the creation of an evaluation model that, through the work breakdown classification, enables 22 the development of environmental budgets that, in a simplified way, lead to the best and most complete environmental analysis of projects. The development of this model as an open-access computer tool constitutes the next step, thereby facilitating the integration of environmental assessment into building recovery processes. In the future, the objective will involve the development of an economic-environmental indicator for the evaluation of the impacts throughout the entire life cycle of the building. 5. REFERENCES Alba-Rodríguez, M. D. (2016). Modelo de evaluación de la viabilidad económica y ambiental de la recuperación de edificios. 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