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Combined Greening Strategies for Improved Results on Carbon-Neutral Urban Policies

Combined Greening Strategies for Improved Results on Carbon-Neutral Urban Policies buildings Article Combined Greening Strategies for Improved Results on Carbon-Neutral Urban Policies 1 , 2 1 Javier Orozco-Messana * , Milagro Iborra-Lucas and Raimon Calabuig-Moreno Institute for Materials Technology, Universitat Politecnica de Valencia, 46022 Valencia, Spain; raicamo@arqt.upv.es Built Environment Department, Universitat Politecnica de Valencia, 46022 Valencia, Spain; miborra1@upvnet.upv.es * Correspondence: jaormes@cst.upv.es Abstract: Starting from historical environmental records of the Benicalap neighbourhood in Valencia, this paper presents an energy model contributing to the assessment of carbon-neutral city policies for several nature-based solution (NBS) pilots extended to the neighbourhood level and combined with building façade renovation proposals. Accurate monitoring of several NBS pilot strategies was studied to validate a computational-fluid-dynamic (CFD) microclimate flux (both storage heat flux and latent heat flux) model, allowing a joint understanding of humidity and heat dynamics for the pilots under study. When expanded at a neighbourhood level, the combined effect of NBSs and energy dynamics (from buildings and vegetation) on neighbourhood microclimates is used to assess the optimal combination of urban renovation policies for energy efficiency and consequently carbon footprint reduction. Keywords: NBSs; CFD; urban carbon assessment model; PCM Citation: Orozco-Messana, J.; Iborra-Lucas, M.; Calabuig-Moreno, R. Combined 1. Introduction Greening Strategies for Improved The United Nations (UN) sustainable development goal 11 on “Making cities and Results on Carbon-Neutral Urban human settlements inclusive, safe, resilient and sustainable” [1] is becoming the greatest Policies. Buildings 2022, 12, 894. challenge for the implementation of the European Green Deal [2]. Cities are the key https://doi.org/10.3390/ driving element for achieving the UN Glasgow climate pact of carbon neutrality by 2050, as buildings12070894 developed by the key outcomes of the COP26 conference [3]. Although cities occupy only Academic Editor: Adrian Pitts 4% of the EU’s land area, they hold 75% of EU citizens, while consuming over 65% of the world’s energy with an impact on the world’s carbon footprint of higher than 70% [3]. Received: 3 June 2022 The covenant of EU mayors adopted on November 2021 the “100 Climate-Neutral Accepted: 20 June 2022 and Smart Cities” initiative [4] urging EU cities to take action on fast and effective carbon Published: 24 June 2022 reduction policies. Publisher’s Note: MDPI stays neutral Urban development policies have ignored climate effects and the accumulated elim- with regard to jurisdictional claims in ination of green surfaces together with the huge concentration of urban thermal storage published maps and institutional affil- continuously fed by urban heat emissions, which have consistently boosted the urban iations. heat-island effect (HIE) [5]. Although smart cities present a comprehensive historical record of relevant variables for these decision-making processes, few tools are available for facilitating the combined assessment of sustainability policies at the neighbourhood level. Urban Green Rating Systems (UGRSs) are the most widely accepted proposal from the Copyright: © 2022 by the authors. scientific community, although with a very fragmented approach, as can be seen in Figure 1 Licensee MDPI, Basel, Switzerland. (obtained using the VOS viewer v.1.6.18 [6] software on a full bibliography analysis through This article is an open access article the Dimensions bibliographic database [7]). The most advanced green rating systems have distributed under the terms and been developed in the US with a holistic approach to the problem. One of the most relevant conditions of the Creative Commons analyses on current systems has been developed by Elena Lucci [8], who concludes on the Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ interdisciplinary nature of urban sustainability for long-term environmental impact. The 4.0/). Buildings 2022, 12, 894. https://doi.org/10.3390/buildings12070894 https://www.mdpi.com/journal/buildings Buildings 2022, 12, x FOR PEER REVIEW  2  of  13  Buildings 2022, 12, 894 2 of 13 environmental impact. The heritage component calls for transdisciplinary actions based  heritage component calls for transdisciplinary actions based on a carbon reduction strategy on a carbon reduction strategy compatible with a participative social approach for deliv‐ compatible with a participative social approach for delivering a comprehensive approach. ering a comprehensive approach.  Figure 1. Author document references clustered network (Dimensions and VOS viewer).  Figure 1. Author document references clustered network (Dimensions and VOS viewer). Although UGRSs are excellent for assessing existing implementations, they have Although UGRSs are excellent for assessing existing implementations, they have lim‐ limited scope for evaluating a future evolution of ratings on specific urban development ited  scope  for  evaluating  a  future  evolution  of  ratings  on  specific  urban  development  plans whose impact on all rated dimensions is very difficult to simulate. plans whose impact on all rated dimensions is very difficult to simulate.  Urban heritage environments pose an additional challenge that is becoming increas- Urban heritage environments pose an additional challenge that is becoming increas‐ ingly relevant for old cities wishing to advance Sustainable Development Goal SDG 11 [1]. ingly relevant for old cities wishing to advance Sustainable Development Goal SDG 11  The need to protect heritage at the urban level requires a widened perspective where a [1]. The need to protect heritage at the urban level requires a widened perspective where  neighbourhood focus contributes to an enhanced cost-effectiveness of urban policies [8]. a neighbourhood focus contributes to an enhanced cost‐effectiveness of urban policies [8].  City planning becomes more efficient when designed from a synergetic approach, avoiding City planning becomes more efficient when designed from a synergetic approach, avoid‐ the constraints emanating from building-based approaches [9]. ing the constraints emanating from building‐based approaches [9].  Following the detailed analysis of UGRSs performed by Lucci et al. [8], none of the Following the detailed analysis of UGRSs performed by Lucci et al. [8], none of the  current systems ensure a sustainable urban development respectful to historical city mor- current systems ensure a sustainable urban development respectful to historical city mor‐ phology. This paper introduces a blended approach based on a comprehensive Life Cycle phology. This paper introduces a blended approach based on a comprehensive Life Cycle  baseline (established from the neighbourhood carbon footprint), evaluating the climate baseline (established from the neighbourhood carbon footprint), evaluating the climate  neutrality layer and including the influence of NBSs and traffic and proposing strategies neutrality layer and including the influence of NBSs and traffic and proposing strategies  for building refurbishment that complies with existing city heritage preservation policies. for building refurbishment that complies with existing city heritage preservation policies.  NBSs are an excellent approach to urban climate resilience [10]. However, their effect NBSs are an excellent approach to urban climate resilience [10]. However, their effect  can be enhanced by tuning climate variables with the buildings’ response to them for a joint can be enhanced by tuning climate variables with the buildings’ response to them for a  contribution to the neighbourhood energy balance [11]. Joint impact assessment of urban joint contribution to the neighbourhood energy balance [11]. Joint impact assessment of  development strategies should consider the combined contribution on climate mitigation urban development strategies should consider the combined contribution on climate mit‐ from street-level physical climate parameters and their influence at the micro level on igation from street‐level physical climate parameters and their influence at the micro level  building performance. A joint balance on moisture content, latent heat, thermal storage on building performance. A joint balance on moisture content, latent heat, thermal storage  dynamics, and operational energy building requirements can optimise latent heat fluxes dynamics, and operational energy building requirements can optimise latent heat fluxes  from evaporation and transpiration as well as thermal energy dynamics [12]. from evaporation and transpiration as well as thermal energy dynamics [12].  In the proposed strategy for urban heritage preservation policies, NBSs must be In the proposed strategy for urban heritage preservation policies, NBSs must be com‐ combined with heritage-compliant strategies, such as new materials for façades (phase bined with heritage‐compliant strategies, such as new materials for façades (phase change  change materials) that maintain the heritage requirements while contributing notably to materials) that maintain the heritage requirements while contributing notably to climate  climate mitigation strategies. Therefore, the carbon-footprint evolution of the combination mitigation strategies. Therefore, the carbon‐footprint evolution of the combination (street  (street climate model + energy assessment of the heritage compliant urban policy) will climate model + energy assessment of the heritage compliant urban policy) will render a  render a comprehensive approach to the problem. comprehensive approach to the problem.  The main physical climate variables are wind, temperature (air and black bulb), and humidity. Together, they configure a complex scenario for modelling urban environments at Buildings 2022, 12, 894 3 of 13 the street level. They must also incorporate radiant and convective heat loss for completely evaluating street microclimates through a simplified CFD balance [13]. Cities develop a distinctive, human-driven environment where vegetation and natural materials are replaced by buildings and service elements that render unique microclimates. The first analysis of urban environments was performed by Luke Howard in his book “the Climate of London” [14]. In this volume, urban microclimates are evaluated and presented in detail, showing higher temperatures than their rural equivalents. Their ability to store and retain more heat was identified for the first time. This phenomenon was defined for the first time by Manley [15] in 1958 as the Heat Island Effect (HIE). HIE is related to local humidity and temperature evolutions controlled by the urban materials’ physical ability to store and release heat and humidity. The range of associated effects is driven by convection exchanges at the surface level, which can be controlled by vegetation for better living conditions [16]. Meteorological evolution can be used to ensure efficient building energy demands [17] with a well-engineered built environment. This paper implements the full process described for the Benicalap neighbourhood in Valencia (Spain) starting from a heritage analysis of the neighbourhood; combined NBSs/traffic interventions are simulated at a neighbourhood level (from pilot evidence), and a refurbishment strategy is simulated for obtaining the carbon-footprint evolution for the joint neighbourhood model. Under the H2020 Grow-Green (EC grant 730283) project, climate records were used as input for the street CFD model developed for a reshaped street morphology, implementing the selected NBSs pilots at neighbourhood level (after verification of the model for each NBS from pilot impact using detailed climate monitoring). Building interaction is then evaluated jointly (as boundary conditions) through the LCA balance of the final carbon footprint for the relevant façade refurbishment solutions obtained (according to heritage constraints) using a digital model of the neighbourhood. 2. Materials and Methods 2.1. Benicalap Neighbourhood Model The proposed neighbourhood scenario corresponds to the Benicalap district in the city of Valencia. The model, or digital twin, of the physical neighbourhood starts with the cadastral digital information which is developed into a digital entity using the ArcGIS Pro 2.9 software from ESRI (Redlands, CA, USA) and following the process developed by the authors of [18]. This model has been adapted to include all building-related parameters (according to their heritage status) for a simplified LCA analysis [19,20], which is required for predicting building energy performance from environmental conditions. The proposed approach provides adequate results driven by detailed street physical parameters obtained from hourly climatic information of the neighbourhood. This informa- tion provides the necessary input parameters to the “digital twin” of the neighbourhood for determining street thermal variations from wind, latent heat (from NBSs), and heat from traffic, thanks to the CFD simulation. Simplified 3D building models allow an accurate ra- diation input to buildings through supporting metadata, established in ISO 19115:2003 [21] standard and further implemented by the EU directive “Infrastructure for Spatial Informa- tion in the European Community” (INSPIRE) [22]. The last step simulates home energy requirements using the City Energy Analyst (CEA, E.T.H. Zürich, Switzerland) [23]. The digital twin facilitates an automated virtual testbed, handling all steps from monitoring to virtual modelling of real interactions using metadata and software routines, and the actual neighbourhood digital twin is built using the procedure described in [18]. 2.2. Grow-Green Pilots The relevant Grow-Green pilots for building the Benicalap Digital Twin include the following (see Figure 2 for detail): a green roof ecosystem and blue/green corridor. Pilots are described in Figure 2, as well as their location within the intervention area. Monitoring of the selected KPIs (Table 1) was performed through one monitoring station Buildings 2022, 12, x FOR PEER REVIEW  4  of  13  2.2. Grow‐Green pilots  The relevant Grow‐Green pilots for building the Benicalap Digital Twin include the  following (see Figure 2 for detail): a green roof ecosystem and blue/green corridor.  Buildings 2022, 12, 894 4 of 13 Pilots are described in Figure 2, as well as their location within the intervention area.  Monitoring of the selected KPIs (Table 1) was performed through one monitoring station  (also in Figure 2) per pilot located in the geometrical centre of the intervention area. Data  (also in Figure 2) per pilot located in the geometrical centre of the intervention area. Data monitoring started once the pilot intervention was finished and followed for one full year.  monitoring started once the pilot intervention was finished and followed for one full Raw data for the year  pilot . Raw s (and data pa forra the llelpilots  locat(and ions parallel for base locations line defin forition) baseline  were definition)  used for wer  dee‐ used for developing and validating the CFD models to be used later. Monitoring data for the pilots veloping and validating the CFD models to be used later. Monitoring data for the pilots  correspond to 2019 and the pre-greening info dates back to 2017. correspond to 2019 and the pre‐greening info dates back to 2017.  Figure 2. Benicalap neighbourhood location, monitoring station and NBS pilots. Green roof eco‐ Figure 2. Benicalap neighbourhood location, monitoring station and NBS pilots. Green roof ecosystem system (1), Blue/Green corridor (2).  (1), Blue/Green corridor (2).     Buildings 2022, 12, x FOR PEER REVIEW  5  of  13  Buildings 2022, 12, 894 5 of 13 Table 1. KPIs for Valencia NBS pilots.  Responsible Service  Table 1. KPIs for Valencia NBS pilots. KPI  Policy  (All Links Accessed 22nd June 2022)  Temperature (°C)  Responsible Service KPI Policy Climate adaption  (All Links Accessed on 22 June 2022) Humidity (% RH)  http://climate‐adapt.eea.europa.eu/  (hourly records) Radiant T emperatur black‐bulb e (temper C) ature (°C)  Climate adaption Humidity (% RH) http://climate-adapt.eea.europa.eu/ NWRM platform  (hourly records) Water retention (L/day)  http://www.nwrm.eu/  Radiant black-bulb temperature ( C) (daily records)  NWRM platform Oppla NBS platform  Water retention (L/day) http://www.nwrm.eu/ Top wind speed (m/s)  https://oppla.eu/nbs/case‐studies  (daily records) (hourly records)  Oppla NBS platform Top wind speed (m/s) https://oppla.eu/nbs/case-studies (hourly records) Data  monitoring  from  the  NBSs  pilots  and  its  management  require  additional  measures described below:  Data monitoring from the NBSs pilots and its management require additional measures • Open Data standards following the “Open cities” platform developed by the Fraun‐ described below: hofer Institut in Fokus [24]. This platform supports the Open Data lifecycle process.  • Fiware  foundation  [25]  open  standards  used  for  device  communication  and  data  Open Data standards following the “Open cities” platform developed by the Fraun- gathering. This open standard provides an enhanced OpenStack‐based cloud envi‐ hofer Institut in Fokus [24]. This platform supports the Open Data lifecycle process. Fronment iware fo  ufor nd athe tion Internet [25] ope nofs tThings andard sdev useidcefs o ron de re vical e‐cti om me m appl unicic atat ion ion ansd while data g incor atherp ino g‐. T rating his op geo en sloc tanal dis arat dion. pro vides an enhanced OpenStack-based cloud environment for the Internet of Things devices on real-time applications while incorporating geolocalisation. • Monitoring data support the metadata INSPIRE Directive [22] proposed as the cod‐ Monitoring data support the metadata INSPIRE Directive [22] proposed as the coding ing standard of geolocalised data for the open display of relevant information from  standard of geolocalised data for the open display of relevant information from the the project. This will allow the joint publication of relevant NBSs data at the EU‐NBS  project. This will allow the joint publication of relevant NBSs data at the EU-NBS Think‐Nature cluster [26].  Think-Nature cluster [26]. • The selection of relevant Key Performance Indicators (KPIs) for the Valencia pilots  The selection of relevant Key Performance Indicators (KPIs) for the Valencia pilots was was performed according to the interoperability requirements agreed upon for all  performed according to the interoperability requirements agreed upon for all EU NBSs EU NBSs projects and is developed in Table 1. The details on relevant technical in‐ projects and is developed in Table 1. The details on relevant technical information on formation on KPIs are also given in Table 1.  KPIs are also given in Table 1. 2.3. Valencia Open Data Platform  2.3. Valencia Open Data Platform The Valencia Open Data platform [18] follows the ISO standard specification ISO/IEC  The Valencia Open Data platform [18] follows the ISO standard specification ISO/IEC 20802‐2:2016 [27] under a simple architecture (Figure 3) running under Oracle (platform‐ 20802-2:2016 [27] under a simple architecture (Figure 3) running under Oracle (platform- neutral), supporting all city datasets including the research data output from city projects,  neutral), supporting all city datasets including the research data output from city projects, while fulfilling the EU data policies and joint repositories [28].  while fulfilling the EU data policies and joint repositories [28]. Figure 3. Valencia Open Data conceptual architecture.  Figure 3. Valencia Open Data conceptual architecture. Figure 2 presents the reference infrastructure model and high‐level technical specifi‐ Figure 2 presents the reference infrastructure model and high-level technical specifica- tion cation for for all al data l data refer  referenc enceded in in this  this paper  pape,rincluding , includingits itsmain  maincomponents  components and and connection connection  points to other tools and systems. The reference architecture meets the technical and user requirements established throughout NBS pilot implementations of the H2020 Grow-Green Buildings 2022, 12, 894 6 of 13 project, addressing technology and user requirements and integrating monitored data onto the Benicalap Digital Twin. The reference architecture considers platform-neutral components, which provide a solution, simultaneously integrating all pilots, linking them to the architecture components of the Link Open Data chain. Data monitoring on pilots at a neighbourhood level was stored and managed through the Valencia Open Data platform. Variables for the Digital Twin are linked dynamically to the corresponding values of the Valencia Open Data platform, ensuring a model design that can be repeated for any neighbourhood with an equivalent structure for impact assessment. Therefore, the Digital Twin model is generic in its possible implementation. Although the full set of data is very wide, for the purpose of this research, only the hourly neighbourhood data records (for 2019 as detailed in Section 3.1) are considered. Pilot data records are used for training the CFD model, while stations away from NBS pilot influence are used as street initial conditions for the simulation of the full-scale deployment of NBSs. This data set serves as the climate baseline. 2.4. Façade Solutions and Building Model Each building is modelled according to its envelope surface (cadastral surface and height). Considering the building typology evaluated from the cadastral info (construction year) and the Episcope/TABULA tool [29] heat-exchange conditions (thermal conductivity, thermal capacity, and windows) are established for each building in the neighbourhood and included in the Digital Twin for evaluating energy transfer in each individual building prior to refurbishment. Through a careful state-of-the-art analysis [30–32] the overall reduction in energy consumption for buildings in urban environments, through better façade solutions, can achieve energy savings of 20 to 55%. Cumulative impact amounts to a possible carbon footprint reduction that could reach up to 12% of an average city [33]. The proposed refurbishing strategy concentrates on façades (adding a Phase Change Material, or PCM, layer) and windows (including radiation shields and double glass). For defining the simplified building solutions to be considered in our research, the following conceptual requirements have been used: Interventions on the existing building stock follow modular designs separately ad- dressing 3 action fields: # Windows: multiple layers including radiation reflective coatings. # External add-on skins facilitating passive skin ventilation, humidity barriers, and radiation heat reflectors. # Internal layers tuned to balance energy flows through thermal storage. Specific solutions address not only energy efficiency, but also overall sustainable designs evaluated through a comprehensive Life Cycle Assessment (LCA). The selected design approach for building solutions follows market-dominant ele- ments following multilayered sandwich solutions, supported by simple multipurpose fastening elements. Standard approaches are selected with an emphasis on quality for meeting a minimum 50-year building life cycle. The schematic design of the proposed retrofitting solution is presented in Figure 3 below with most relevant design parameters. For the evaluation of retrofitting policies, 3 different alternatives for Phase Change Materials are considered in Section 3.2 with the same support shown in Figure 4. These materials have been selected from the dominant market products [34]: organic paraffin, tetrahydrofuran clathrate hydrate, and caprylic acid with lauric acid (9:1 eutectic). Buildings 2022, 12, 894 7 of 13 Buildings 2022, 12, x FOR PEER REVIEW  7  of  13  Double glass window with radiation shield coating Watertight seal PCM heat storage Outer façade Structural Floor support PCM heat storage Façade Figure 4. Conceptual façade modular retrofit section. Figure 4. Conceptual façade modular retrofit section.  3. Modelling Outputs and Discussion 3. Modelling Outputs and Discussion  3.1. CFD Microclimate Model 3.1. CFD Microclimate Model  For selecting the CFD model best suiting the Benicalap neighbourhood, the extensive For selecting the CFD model best suiting the Benicalap neighbourhood, the extensive  review provided by Toparlar et al. [9] is followed. Since the neighbourhood is distributed as review provided by Toparlar et al. [9] is followed. Since the neighbourhood is distributed  a random grid of multiple street canyons connecting open green spaces, the governing equa- as a random grid of multiple street canyons connecting open green spaces, the governing  tions considered follow the Large Eddy Simulations (LES) approach, solved numerically equation using thes Dear consider doffed sub-grid  followscale  the Large model Eddy appr Sim oachul[a 35 tions ].  (LES) approach, solved numer‐ icallyT us hein dg o m the ai nDeardoff conside rsu edbf‐ogr rid th escale CFD model mode laipproa s enclch ose [3 d5] in. a rectangle aligned with the domiThe nan tdomain street d ic ro ecnsider tion aned d cfor irc uthe ms CFD cribi  nmodel g the l iis m enclo its of tshed e n in ei gah re boct uarn hgl ooed al (1igned 200  with 1400 the m,  dominant as present estr d eet in F dir iguerction e 2). Tand he hcoirc riumsc zontaribing l grid ithe nte rlim valit is s of 12 the m i n neighbourhood the x direction (1 an20 d01 x 4 1 m400 in  m, the as y d pr iresent ectioe nd , ain nd Fitgu herheo 2) ri.z The onta horizo l grid d ntal im egrid nsio int n iser1v 0al 0  is 12 10 0m .  A in s the for x th di e r peecrtpio en n dand icu l14 ar  grid, 50 non-uniform layers are considered. Layers closer to the surface are 3 m thick up to m in the y direction, and the horizontal grid dimension is 100 × 100. As for the perpendic‐ the 25th layer, increasing with a 1.1 expansion ratio from the 26th layer to the 35th layer, and ular grid, 50 non‐uniform layers are considered. Layers closer to the surface are 3 m thick  from here to the 50th layer a uniform thickness of 7.78 m is considered. The grid density up to the 25th layer, increasing with a 1.1 expansion ratio from the 26th layer to the 35th  selected follows the optimal performance according to [36]. layer, and from here to the 50th layer a uniform thickness of 7.78 m is considered. The grid  The physical parameters to be obtained from the climate model were previously pre- density selected follows the optimal performance according to [36].  sented in Table 1. The routine was selected from the Ansys Fluent (Ansys Inc., Canonsburg, The physical parameters to be obtained from the climate model were previously pre‐ PA, USA) software used and run using as input pre-greening temperature data together sented  in  Table  1.  The  routine  was  selected  from  the  Ansys  Fluent  (Ansys  Inc.,  Can‐ with the NBSs implemented (trees, green roofs, water reservoirs), and hourly radiation onsburg, PA, USA) software used and run using as input pre‐greening temperature data  information is taken from the VLC open data repository [37]. The geometries for each together with the NBSs implemented (trees, green roofs, water reservoirs), and hourly  Benicalap street pilot are obtained from the pilot area 3D models imported from the ArcGIS radiation information is taken from the VLC open data repository [37]. The geometries for  neighbourhood model. The CFD simulation results add to the given climate conditions, each Benicalap street pilot are obtained from the pilot area 3D models imported from the  latent heat and airflow impacts, for thermal variations. ArcGIS neighbourhood model. The CFD simulation results add to the given climate con‐ Once our climate model for each pilot area is established, the initial values for each day ditions, latent heat and airflow impacts, for thermal variations.  are selected from hourly average values from the pre-greening monitored data at 0:00 a.m. Once our climate model for each pilot area is established, the initial values for each  The climate model is then run for the whole day keeping daily extreme temperatures day are selected from hourly average values from the pre‐greening monitored data at 0:00  together with the other KPIs (from Table 1) at the time when the extreme temperatures a.m. The climate model is then run for the whole day keeping daily extreme temperatures  are reached. This process is repeated for each pilot through the 365 days of the year (2019) together with the other KPIs (from Table 1) at the time when the extreme temperatures  after the pilots are built obtaining a table for one years’ worth of daily simulated extreme are reached. This process is repeated for each pilot through the 365 days of the year (2019)  temperature and related KPIs (see Figure 5 for more details). after the pilots are built obtaining a table for one years’ worth of daily simulated extreme  temperature and related KPIs (see Figure 5 for more details).  Separation Buildings 2022, 12, 894 8 of 13 Buildings 2022, 12, x FOR PEER REVIEW  8  of  13  Buildings 2022, 12, x FOR PEER REVIEW  8  of  13  Dayclimate data Climate model Year simulation Day simulation Temperature/Humidity Dayclimate data Cl3Dimgateome emtryodel To = T real at 0:00 am Maximum‐Minimum per day, Ye All aKrP Iss immaux/lmatinion Day simulation Temperature/Humidity ANSYS fluent 3Dgeometry RadiationdataVLCcityportal Waterretention and Wind  per day To = T real at 0:00 am Maximum‐Minimum per day, All KPIs max/min LargeEddy Deardoff model speed at the same times ANSYS fluent RadiationdataVLCcityportal Waterretention and Wind  per day LargeEddy Deardoff model speed at the same times Figure 5. Climate model development process.  Figure 5. Climate model development process. Figure 5. Climate model development process.  Simulation Simulationr esults results show  show relevant  relevant changes  changes depending  depending on  the on the specific  speci point fic point consider  cons ed. id‐ Simulation results show relevant changes depending on the specific point consid‐ To avoid local variations in the CFD model, all values are averaged for each climate variable ered. To avoid local variations in the CFD model, all values are averaged for each climate  ered. To avoid local variations in the CFD model, all values are averaged for each climate  per variable street. per This street. allows This a simpler  allows calculation a simpler calc later ulat for ion ener  latgy er for requir  ener ements gy requirements without a r elevant without  variable per street. This allows a simpler calculation later for energy requirements without  impact a relevon antaccuracy  impact on [35 accuracy ]. The average  [35]. The simulated  averager esults simulated per str  results eet ar per e then  street compar  are then ed to com the‐ a relevant impact on accuracy [35]. The average simulated results per street are then com‐ real monitoring data after greening for the two pilot areas (see Figure 6 for error evolution pared to the real monitoring data after greening for the two pilot areas (see Figure 6 for  pared to the real monitoring data after greening for the two pilot areas (see Figure 6 for  for real and simulated values in Plaza Regino Mas). Results on the deviations show a error evolution for real and simulated values in Plaza Regino Mas). Results on the devia‐ error evolution for real and simulated values in Plaza Regino Mas). Results on the devia‐ Gaussian distribution for both pilots on the 365 days evaluated. Results of the statistical tions show a Gaussian distribution for both pilots on the 365 days evaluated. Results of  tions show a Gaussian distribution for both pilots on the 365 days evaluated. Results of  distributions per KPI and pilot are presented in Table 2. the statistical distributions per KPI and pilot are presented in Table 2.  the statistical distributions per KPI and pilot are presented in Table 2.  Figure 6. Daily error evolution for simulation on “Plaza Regino Mas”.  Figure 6. Daily error evolution for simulation on “Plaza Regino Mas”.  Figure 6. Daily error evolution for simulation on “Plaza Regino Mas”. Table 2. Error distributions for the climate variables on NBSs pilots.  Table 2. Error distributions for the climate variables on NBSs pilots. Table 2. Error distributions for the climate variables on NBSs pilots.  Pilot  KPI  Error (KPIe) Distribution  Pilot KPI Error (KPI ) Distribution Pilot  KPI  Error (KPIe) Distribution  Temperature (°C)  ΔT (av.) = 1.1, σ = 0.2  Temperature ( C) Temperature (°C)  ΔT (av.) = 1.1, σ = 0.2  Humidity (% RH)  ΔH (av.) = 2.8, σ = 0.5  DT (av.) = 1.1,  = 0.2 Green roof  Humidity (% RH) Humidity (% RH)  ΔH (av.) = 2.8, σ = 0.5  Radiant black‐bulb temperature (°C) ΔDR HT (av (av.) .) = =2.8,  0.9, σ  == 0.5 0.1  Green roof  Radiant black-bulb (1)    Radiant black‐bulb temperature (°C) DΔ RRT(av  (a.)v.) = = 0.9, 0.9,  σ  = 0.1 = 0.1  Water retention (L/day) ΔWater retention (av.) = 0.5, σ = 0.02  Green roof (1) temperature ( C) (1)  Water retention (L/day) ΔWater retention (av.) = 0.5, σ = 0.02  Wind speed (m/s) ΔWind speed (av.) = 2.3, σ = 0.4  Water retention (L/day) DWater retention (av.) = 0.5,  = 0.02 Wind speed (m/s) ΔWind speed (av.) = 2.3, σ = 0.4  Temperature (°C)  ΔT (av.) = 1.5, σ = 0.3  Wind speed (m/s) DWind speed (av.) = 2.3,  = 0.4 Temperature (°C)  ΔT (av.) = 1.5, σ = 0.3  Humidity (% RH)  ΔH (av.) = 2.6, σ = 0.4  Temperature ( C) Blue/green  Humidity (% RH)  ΔH (av.) = 2.6, σ = 0.4  Radiant black‐bulb temperature (°C) ΔDR TT  (av (av.) .) = = 1.5,  1.1, σ  = = 0.3 0.1  Blue/green  Humidity (% RH) corridor (2)    Radiant black‐bulb temperature (°C) DΔHRT (av (a.)v.) = 2.6, = 1.1,  σ  = 0.4 = 0.1  Water retention (L/day) ΔWater retention (av.) = 0.9, σ = 0.01  Radiant black-bulb corridor (2)  DR (av.) = 1.1,  = 0.1 Water retention (L/day) ΔWater retention (av.) = 0.9, σ = 0.01  Blue/green corridor (2) temperature ( C) Top wind speed (m/s) ΔWind speed (av.) = 2.9, σ = 0.6  Top wind speed (m/s) ΔWind speed (av.) = 2.9, σ = 0.6  Water retention (L/day) DWater retention (av.) = 0.9,  = 0.01 The results show a good performance of the model (less than 5% error), and the cli‐ Top wind speed (m/s) DWind speed (av.) = 2.9,  = 0.6 The results show a good performance of the model (less than 5% error), and the cli‐ mate model is therefore validated. The simulation outputs for the climate model include  mate model is therefore validated. The simulation outputs for the climate model include  not only the validation KPIs (temperature/humidity, water retention and wind speed), but  The results show a good performance of the model (less than 5% error), and the climate not only the validation KPIs (temperature/humidity, water retention and wind speed), but  also radiation, convection energy exchange conditions, and latent heat energy, which will  model is therefore validated. The simulation outputs for the climate model include not also radiation, convection energy exchange conditions, and latent heat energy, which will  be needed to evaluate the energy balance for Benicalap.  only the validation KPIs (temperature/humidity, water retention and wind speed), but also be needed to evaluate the energy balance for Benicalap.  Together  with  the  digital twin  of  Benicalap,  the  climate  model allows  the  perfor‐ radiation, convection energy exchange conditions, and latent heat energy, which will be Together  with  the  digital twin  of  Benicalap,  the  climate  model allows  the  perfor‐ mance assessment of the proposed interventions on the Benicalap neighbourhood consid‐ needed to evaluate the energy balance for Benicalap. mance assessment of the proposed interventions on the Benicalap neighbourhood consid‐ ering the streets and buildings: deployment of NBSs (pilots) 1 (on all public buildings)  ering the streets and buildings: deployment of NBSs (pilots) 1 (on all public buildings)  and 2 (on every street), together with any refurbishment strategies.  and 2 (on every street), together with any refurbishment strategies.  Buildings 2022, 12, 894 9 of 13 Together with the digital twin of Benicalap, the climate model allows the performance assessment of the proposed interventions on the Benicalap neighbourhood considering the streets and buildings: deployment of NBSs (pilots) 1 (on all public buildings) and 2 (on every street), together with any refurbishment strategies. 3.2. Neighbourhood Impact Assessment As described in Section 2.4, the neighbourhood digital model is built on ArcGIS, starting from geometry modelling for later embedding building typologies (taken from Tabula/Episcope [29]) and the corresponding materials’ relevant LCA data (density, raw materials carbon footprint, thermal conductivity, specific heat capacity, labour, and average price) structured as building units (as structured in IVE’s construction database [38]). Integrated in the same model (digital twin), the weather conditions (temperature/ humidity, water retention, wind speed, radiation, convection energy exchange conditions, and latent heat energy) are geolocated to an average value per street serving as outdoor stable weather conditions per hour in the reference year (2019 in our case). Individual building carbon footprints incorporate the energy requirements for home temperature regulation (using the most common equipment per building typology), plus fixed carbon footprint components (lighting, home activities). Both are added and aver- aged for the building envelopes in order to provide the yearly carbon footprint. Weather conditions are set to the real (first) and simulated street values (according to the green infrastructure model). By comparing results, it is possible to assess the possible impact on the carbon footprint of the proposed building retrofit policy in the neighbourhood, which is the key result of our research. The last process incorporated into the model concerns building energy requirements from the CFD climate model baseline. This task is performed through CEA [23] routines coded in Python (v.3.9.10, Python software foundation, Wilmington, DE, USA) implementation of the proposed building adaption strategies and is explained in Section 2.4. CEA evaluates all heat exchanges through building envelopes (radiation, convection, and conduction). CEA is selected due to its excellent acceptance and accuracy for the energy exchange evaluations, including finally the related CO footprint for building construction and use [20]. The Benicalap digital twin described here obtains the overall energy performance balance and associated carbon footprint for all the buildings in the neighbourhood under normal occupancy conditions and using the average climate adaption measures evaluated in [38]. All industrial buildings are only considered in the model with the fixed energy consumptions evaluated in [35]. Our Benicalap digital twin output graphically presents the CO footprint for private energy consumption, also including the embedded CO footprint from the construction phase. Starting with the real neighbourhood information on its current situation, the results consider the expected impact of green infrastructure and traffic on the average street temperature as described before. Each building is assessed independently according to its typology, and the calculated balance is delivered to the neighbourhood’s integrated digital model. The graphical results for the neighbourhood, without greening or building refurbishments, can be seen in Figure 7, showing the spatial variations in building carbon footprint distribution. The last step includes the combination of NBS infrastructure together with heat storage capabilities (through PCM sandwich solutions) already introduced in Section 2.4. Details on the commercial materials to be evaluated as alternative solutions for the whole neigh- bourhood façade renovation are presented in Table 3. Each of these materials are introduced on the façades of all residential buildings on the Benicalap digital twin and evaluated for their carbon footprint. The PCMs help in maintaining low energy consumption while shielding from extreme outer temperatures. Buildings 2022, 12, x FOR PEER REVIEW  9  of  13  3.2. Neighbourhood Impact Assessment  As described in Section 2.4, the neighbourhood digital model is built on ArcGIS, start‐ ing from geometry modelling for later embedding building typologies (taken from Tab‐ ula/Episcope [29]) and the corresponding materials’ relevant LCA data (density, raw ma‐ terials carbon footprint, thermal conductivity, specific heat capacity, labour, and average  price) structured as building units (as structured in IVE’s construction database [38]).  Integrated in the same model (digital twin), the weather conditions (temperature/hu‐ midity, water retention, wind speed, radiation, convection energy exchange conditions,  and latent heat energy) are geolocated to an average value per street serving as outdoor  stable weather conditions per   hour in the reference year (2019 in our case).  Individual building carbon footprints incorporate the energy requirements for home  temperature regulation (using the most common equipment per building typology), plus  fixed carbon footprint components (lighting, home activities). Both are added and aver‐ aged for the building envelopes in order to provide the yearly carbon footprint. Weather  conditions are set to the real (first) and simulated street values (according to the green  infrastructure model). By comparing results, it is possible to assess the possible impact on  the carbon footprint of the proposed building retrofit policy in the neighbourhood, which  is the key result of our research.  The last process incorporated into the model concerns building energy requirements  from the CFD climate model baseline. This task is performed through CEA [23] routines  coded in Python (v.3.9.10, Python software foundation, Wilmington, DE, USA) implemen‐ tation of the proposed building adaption strategies and is explained in point 2.4. CEA  evaluates all heat exchanges through building envelopes (radiation, convection, and con‐ duction).  CEA is selected due to its excellent acceptance and accuracy for the energy exchange  evaluations, including finally the related CO2 footprint for building construction and use  [20]. The Benicalap digital twin described here obtains the overall energy performance  balance and associated carbon footprint for all the buildings in the neighbourhood under  normal occupancy conditions and using the average climate adaption measures evaluated  in [38]. All industrial buildings are only considered in the model with the fixed energy  consumptions evaluated in [35].  Our Benicalap digital twin output graphically presents the CO2 footprint for private  energy consumption, also including the embedded CO2 footprint from the construction  phase. Starting with the real neighbourhood information on its current situation, the re‐ sults consider the expected impact of green infrastructure and traffic on the average street  temperature as described before.  Each building is assessed independently according to its typology, and the calculated  balance is delivered to the neighbourhood’s integrated digital model. The graphical re‐ Buildings 2022, 12, 894 10 of 13 sults for the neighbourhood, without greening or building refurbishments, can be seen in  Figure 7, showing the spatial variations in building carbon footprint distribution.  Figure 7. Graphical presentation of neighbourhood CO footprint per building. Table 3. PCMs to be assessed on Benicalap neighbourhood. Phase Change Thermal Name Melting Density Reference Enthalpy Conductivity Type Composition Temperature ( C) (Kg/m ) (Kj/Kg) (W/m K) Organic RT54HC [39] 53–54 200 0.20 800 paraffin Caprylic + lauric Organic [40] 3.8 151 0.20 835 acid (9:1 by mol) eutectic Tetrahydrofuran [41] 4.4 255 0.15 912 Inorganic clathrate hydrate The simulations are configured and run obtaining the results on Table 4 which will be developed in Section 4. Table 4. Carbon footprints after LCA in Benicalap neighbourhood. CO Footprint CO Footprint Total 2 2 Savings Simulation Embedded Usage CO Footprint (%) (Tn CO /year) (Tn CO /year) (Tn CO /year) 2 2 2 Current state 38.1 273.4 311.5 0.0 NBSs 40.1 256.2 296.3 4.9 NBSs + paraffin 43.7 221.1 264.8 15.0 NBSs + eutectic 42.8 232.0 274.8 11.7 NBSs + inorganic 41.5 216.7 258.2 17.1 4. Results The LCA evaluation is performed according to the ISO 14040:2006. After obtaining the results of the yearly simulation, it can be easily observed that the savings are very relevant for the low level of investment required for the interventions. These results are developed at neighbourhood level, although separate analyses can be performed per building typology, aggregating each individual building’s carbon footprint. The proposed methodology is very efficient providing excellent results compared to current alternative carbon footprint evaluations in buildings [42]. Buildings 2022, 12, 894 11 of 13 NBS green connectivity and roof installations provide not only a very relevant 5% energy consumption reduction, but also additional impact on water savings, avoidance of run-off water, and biodiversity protection. PCM solutions provide combined (with NBSs) savings ranging from 10 to 20% of technical problems related to encapsulation and durability for organic materials, but the inorganic solutions, when tuned in their heat storage capacity to the requirements (thickness can be easily adapted), can ensure a costless operation and energy savings (beyond the carbon footprint reduction benefits) that are very relevant to household economies. As identified by prior research [43], Building Energy Simulation has been extensively used during the design stage of modern buildings. The accuracy of the results depends on accurate monitoring together with historic climate records, along with oversimplification of the building types together with statistical variations on real implementations. Simulation error [44] ranges from 9 to 27% in different environments. The proposed digital twin has been tested to obtain simulation errors smaller than 9%. The building renovation wave, which is very active around Green Deal policies, has already incorporated similar solutions, which will render synergetic joint performance. Results go beyond the climate-neutral policies, and these building elements can be easily recycled, bringing down the carbon footprint by also allowing an easy combination with air circulation technologies to obtain additional benefits from natural conditions. 5. Conclusions This paper has developed an easy and accurate climate model to be used for evaluating energy performance at the city level. The proposed model has been implemented together with a joint digital twin for a neighbourhood allowing future developments for the impact assessment of urban policies. The building models also allow wide flexibility in building solution modelling, which will facilitate the performance analysis of new designs and architectural proposals. Urban development must be guided with technical evidence on combined effects for an adequate sustainability strategy. PCM performance opens many relevant synergy strategies with traditional climate- guided building designs and more advanced renewable energy integration on buildings. As a final summary, the combined deployment of the proposed techniques also allows relevant applications to many industrial processes. Author Contributions: Conceptualization, J.O.-M.; research design, J.O.-M. and M.I.-L.; methodology, J.O.-M. and R.C.-M.; experimental results and analysis, M.I.-L.; conclusions, J.O.-M. All authors have read and agreed to the published version of the manuscript. Funding: This research was co-funded by the European Commission through the H2020 project “Green Cities for Climate and Water Resilience, Sustainable Economic Growth, Healthy Citizens and Environments (GROW GREEN)” Grant Agreement: 730283. Institutional Review Board Statement: Not applicable. 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Available online: https://setis.ec.europa.eu/implementing-actions/energy-efficiency-buildings_en (accessed on 14 March 2022). 34. Zsembinszki, G.; Fernández, A.G.; Cabeza, L.F. Selection of the Appropriate Phase Change Material for Two Innovative Compact Energy Storage Systems in Residential Buildings. Appl. Sci. 2020, 10, 2116. [CrossRef] 35. Deardorff, J.W. Stratocumulus-capped mixed layers derived from a three-dimensional model. Bound-Layer Meteorol. 1980, 18, 495–527. [CrossRef] 36. Baik, J.J.; Park, S.B.; Kim, J.J. Urban Flow and Dispersion Simulation Using a CFD Model Coupled to a Mesoscale Model. J. Appl. Meteorol. Climatol. 2009, 48, 1667–1681. [CrossRef] 37. Available online: https://dadesobertes.gva.es/es/dataset/med-cont-atmos-md-2019 (accessed on 9 April 2022). 38. BDC IVE. Available online: https://bdc.f-ive.es/BDC21/1 (accessed on 9 April 2022). Buildings 2022, 12, 894 13 of 13 39. Rubitherm RT-PCM. Available online: https://www.rubitherm.eu/en/index.php/productcategory/organische-pcm-rt (accessed on 9 April 2022). 40. Shengli, T.; Dong, Z.; Deyan, X. Experimental study of caprylic acid/lauric acid molecular alloys used as low-temperature phase change materials in energy storage. Energy Conserv. 2005, 6, 45–47. 41. Jankowski, N.R.; McCluskey, F.P. A review of phase change materials for vehicle component thermal buffering. Appl. Energy 2014, 113, 1525–1561. [CrossRef] 42. Fenner, A.E.; Kibert, C.J.; Woo, J.; Morque, S.; Razkenari, M.; Hakim, H.; Lu, X. The carbon footprint of buildings: A review of methodologies and applications. Renew. Sustain. Energy Rev. 2018, 94, 1142–1152. [CrossRef] 43. Harish, V.S.K.V.; Kumar, A. A review on modeling and simulation of building energy systems. Renew. Sustain. Energy Rev. 2016, 56, 1272–1292. [CrossRef] 44. Eggimann, S.; Vulic, N.; Rüdisüli, M.; Mutschler, R.; Orehounig, K.; Sulzer, M. Spatiotemporal upscaling errors of building stock clustering for energy demand simulation. Energy Build. 2022, 258, 111844. [CrossRef] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Buildings Multidisciplinary Digital Publishing Institute

Combined Greening Strategies for Improved Results on Carbon-Neutral Urban Policies

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buildings Article Combined Greening Strategies for Improved Results on Carbon-Neutral Urban Policies 1 , 2 1 Javier Orozco-Messana * , Milagro Iborra-Lucas and Raimon Calabuig-Moreno Institute for Materials Technology, Universitat Politecnica de Valencia, 46022 Valencia, Spain; raicamo@arqt.upv.es Built Environment Department, Universitat Politecnica de Valencia, 46022 Valencia, Spain; miborra1@upvnet.upv.es * Correspondence: jaormes@cst.upv.es Abstract: Starting from historical environmental records of the Benicalap neighbourhood in Valencia, this paper presents an energy model contributing to the assessment of carbon-neutral city policies for several nature-based solution (NBS) pilots extended to the neighbourhood level and combined with building façade renovation proposals. Accurate monitoring of several NBS pilot strategies was studied to validate a computational-fluid-dynamic (CFD) microclimate flux (both storage heat flux and latent heat flux) model, allowing a joint understanding of humidity and heat dynamics for the pilots under study. When expanded at a neighbourhood level, the combined effect of NBSs and energy dynamics (from buildings and vegetation) on neighbourhood microclimates is used to assess the optimal combination of urban renovation policies for energy efficiency and consequently carbon footprint reduction. Keywords: NBSs; CFD; urban carbon assessment model; PCM Citation: Orozco-Messana, J.; Iborra-Lucas, M.; Calabuig-Moreno, R. Combined 1. Introduction Greening Strategies for Improved The United Nations (UN) sustainable development goal 11 on “Making cities and Results on Carbon-Neutral Urban human settlements inclusive, safe, resilient and sustainable” [1] is becoming the greatest Policies. Buildings 2022, 12, 894. challenge for the implementation of the European Green Deal [2]. Cities are the key https://doi.org/10.3390/ driving element for achieving the UN Glasgow climate pact of carbon neutrality by 2050, as buildings12070894 developed by the key outcomes of the COP26 conference [3]. Although cities occupy only Academic Editor: Adrian Pitts 4% of the EU’s land area, they hold 75% of EU citizens, while consuming over 65% of the world’s energy with an impact on the world’s carbon footprint of higher than 70% [3]. Received: 3 June 2022 The covenant of EU mayors adopted on November 2021 the “100 Climate-Neutral Accepted: 20 June 2022 and Smart Cities” initiative [4] urging EU cities to take action on fast and effective carbon Published: 24 June 2022 reduction policies. Publisher’s Note: MDPI stays neutral Urban development policies have ignored climate effects and the accumulated elim- with regard to jurisdictional claims in ination of green surfaces together with the huge concentration of urban thermal storage published maps and institutional affil- continuously fed by urban heat emissions, which have consistently boosted the urban iations. heat-island effect (HIE) [5]. Although smart cities present a comprehensive historical record of relevant variables for these decision-making processes, few tools are available for facilitating the combined assessment of sustainability policies at the neighbourhood level. Urban Green Rating Systems (UGRSs) are the most widely accepted proposal from the Copyright: © 2022 by the authors. scientific community, although with a very fragmented approach, as can be seen in Figure 1 Licensee MDPI, Basel, Switzerland. (obtained using the VOS viewer v.1.6.18 [6] software on a full bibliography analysis through This article is an open access article the Dimensions bibliographic database [7]). The most advanced green rating systems have distributed under the terms and been developed in the US with a holistic approach to the problem. One of the most relevant conditions of the Creative Commons analyses on current systems has been developed by Elena Lucci [8], who concludes on the Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ interdisciplinary nature of urban sustainability for long-term environmental impact. The 4.0/). Buildings 2022, 12, 894. https://doi.org/10.3390/buildings12070894 https://www.mdpi.com/journal/buildings Buildings 2022, 12, x FOR PEER REVIEW  2  of  13  Buildings 2022, 12, 894 2 of 13 environmental impact. The heritage component calls for transdisciplinary actions based  heritage component calls for transdisciplinary actions based on a carbon reduction strategy on a carbon reduction strategy compatible with a participative social approach for deliv‐ compatible with a participative social approach for delivering a comprehensive approach. ering a comprehensive approach.  Figure 1. Author document references clustered network (Dimensions and VOS viewer).  Figure 1. Author document references clustered network (Dimensions and VOS viewer). Although UGRSs are excellent for assessing existing implementations, they have Although UGRSs are excellent for assessing existing implementations, they have lim‐ limited scope for evaluating a future evolution of ratings on specific urban development ited  scope  for  evaluating  a  future  evolution  of  ratings  on  specific  urban  development  plans whose impact on all rated dimensions is very difficult to simulate. plans whose impact on all rated dimensions is very difficult to simulate.  Urban heritage environments pose an additional challenge that is becoming increas- Urban heritage environments pose an additional challenge that is becoming increas‐ ingly relevant for old cities wishing to advance Sustainable Development Goal SDG 11 [1]. ingly relevant for old cities wishing to advance Sustainable Development Goal SDG 11  The need to protect heritage at the urban level requires a widened perspective where a [1]. The need to protect heritage at the urban level requires a widened perspective where  neighbourhood focus contributes to an enhanced cost-effectiveness of urban policies [8]. a neighbourhood focus contributes to an enhanced cost‐effectiveness of urban policies [8].  City planning becomes more efficient when designed from a synergetic approach, avoiding City planning becomes more efficient when designed from a synergetic approach, avoid‐ the constraints emanating from building-based approaches [9]. ing the constraints emanating from building‐based approaches [9].  Following the detailed analysis of UGRSs performed by Lucci et al. [8], none of the Following the detailed analysis of UGRSs performed by Lucci et al. [8], none of the  current systems ensure a sustainable urban development respectful to historical city mor- current systems ensure a sustainable urban development respectful to historical city mor‐ phology. This paper introduces a blended approach based on a comprehensive Life Cycle phology. This paper introduces a blended approach based on a comprehensive Life Cycle  baseline (established from the neighbourhood carbon footprint), evaluating the climate baseline (established from the neighbourhood carbon footprint), evaluating the climate  neutrality layer and including the influence of NBSs and traffic and proposing strategies neutrality layer and including the influence of NBSs and traffic and proposing strategies  for building refurbishment that complies with existing city heritage preservation policies. for building refurbishment that complies with existing city heritage preservation policies.  NBSs are an excellent approach to urban climate resilience [10]. However, their effect NBSs are an excellent approach to urban climate resilience [10]. However, their effect  can be enhanced by tuning climate variables with the buildings’ response to them for a joint can be enhanced by tuning climate variables with the buildings’ response to them for a  contribution to the neighbourhood energy balance [11]. Joint impact assessment of urban joint contribution to the neighbourhood energy balance [11]. Joint impact assessment of  development strategies should consider the combined contribution on climate mitigation urban development strategies should consider the combined contribution on climate mit‐ from street-level physical climate parameters and their influence at the micro level on igation from street‐level physical climate parameters and their influence at the micro level  building performance. A joint balance on moisture content, latent heat, thermal storage on building performance. A joint balance on moisture content, latent heat, thermal storage  dynamics, and operational energy building requirements can optimise latent heat fluxes dynamics, and operational energy building requirements can optimise latent heat fluxes  from evaporation and transpiration as well as thermal energy dynamics [12]. from evaporation and transpiration as well as thermal energy dynamics [12].  In the proposed strategy for urban heritage preservation policies, NBSs must be In the proposed strategy for urban heritage preservation policies, NBSs must be com‐ combined with heritage-compliant strategies, such as new materials for façades (phase bined with heritage‐compliant strategies, such as new materials for façades (phase change  change materials) that maintain the heritage requirements while contributing notably to materials) that maintain the heritage requirements while contributing notably to climate  climate mitigation strategies. Therefore, the carbon-footprint evolution of the combination mitigation strategies. Therefore, the carbon‐footprint evolution of the combination (street  (street climate model + energy assessment of the heritage compliant urban policy) will climate model + energy assessment of the heritage compliant urban policy) will render a  render a comprehensive approach to the problem. comprehensive approach to the problem.  The main physical climate variables are wind, temperature (air and black bulb), and humidity. Together, they configure a complex scenario for modelling urban environments at Buildings 2022, 12, 894 3 of 13 the street level. They must also incorporate radiant and convective heat loss for completely evaluating street microclimates through a simplified CFD balance [13]. Cities develop a distinctive, human-driven environment where vegetation and natural materials are replaced by buildings and service elements that render unique microclimates. The first analysis of urban environments was performed by Luke Howard in his book “the Climate of London” [14]. In this volume, urban microclimates are evaluated and presented in detail, showing higher temperatures than their rural equivalents. Their ability to store and retain more heat was identified for the first time. This phenomenon was defined for the first time by Manley [15] in 1958 as the Heat Island Effect (HIE). HIE is related to local humidity and temperature evolutions controlled by the urban materials’ physical ability to store and release heat and humidity. The range of associated effects is driven by convection exchanges at the surface level, which can be controlled by vegetation for better living conditions [16]. Meteorological evolution can be used to ensure efficient building energy demands [17] with a well-engineered built environment. This paper implements the full process described for the Benicalap neighbourhood in Valencia (Spain) starting from a heritage analysis of the neighbourhood; combined NBSs/traffic interventions are simulated at a neighbourhood level (from pilot evidence), and a refurbishment strategy is simulated for obtaining the carbon-footprint evolution for the joint neighbourhood model. Under the H2020 Grow-Green (EC grant 730283) project, climate records were used as input for the street CFD model developed for a reshaped street morphology, implementing the selected NBSs pilots at neighbourhood level (after verification of the model for each NBS from pilot impact using detailed climate monitoring). Building interaction is then evaluated jointly (as boundary conditions) through the LCA balance of the final carbon footprint for the relevant façade refurbishment solutions obtained (according to heritage constraints) using a digital model of the neighbourhood. 2. Materials and Methods 2.1. Benicalap Neighbourhood Model The proposed neighbourhood scenario corresponds to the Benicalap district in the city of Valencia. The model, or digital twin, of the physical neighbourhood starts with the cadastral digital information which is developed into a digital entity using the ArcGIS Pro 2.9 software from ESRI (Redlands, CA, USA) and following the process developed by the authors of [18]. This model has been adapted to include all building-related parameters (according to their heritage status) for a simplified LCA analysis [19,20], which is required for predicting building energy performance from environmental conditions. The proposed approach provides adequate results driven by detailed street physical parameters obtained from hourly climatic information of the neighbourhood. This informa- tion provides the necessary input parameters to the “digital twin” of the neighbourhood for determining street thermal variations from wind, latent heat (from NBSs), and heat from traffic, thanks to the CFD simulation. Simplified 3D building models allow an accurate ra- diation input to buildings through supporting metadata, established in ISO 19115:2003 [21] standard and further implemented by the EU directive “Infrastructure for Spatial Informa- tion in the European Community” (INSPIRE) [22]. The last step simulates home energy requirements using the City Energy Analyst (CEA, E.T.H. Zürich, Switzerland) [23]. The digital twin facilitates an automated virtual testbed, handling all steps from monitoring to virtual modelling of real interactions using metadata and software routines, and the actual neighbourhood digital twin is built using the procedure described in [18]. 2.2. Grow-Green Pilots The relevant Grow-Green pilots for building the Benicalap Digital Twin include the following (see Figure 2 for detail): a green roof ecosystem and blue/green corridor. Pilots are described in Figure 2, as well as their location within the intervention area. Monitoring of the selected KPIs (Table 1) was performed through one monitoring station Buildings 2022, 12, x FOR PEER REVIEW  4  of  13  2.2. Grow‐Green pilots  The relevant Grow‐Green pilots for building the Benicalap Digital Twin include the  following (see Figure 2 for detail): a green roof ecosystem and blue/green corridor.  Buildings 2022, 12, 894 4 of 13 Pilots are described in Figure 2, as well as their location within the intervention area.  Monitoring of the selected KPIs (Table 1) was performed through one monitoring station  (also in Figure 2) per pilot located in the geometrical centre of the intervention area. Data  (also in Figure 2) per pilot located in the geometrical centre of the intervention area. Data monitoring started once the pilot intervention was finished and followed for one full year.  monitoring started once the pilot intervention was finished and followed for one full Raw data for the year  pilot . Raw s (and data pa forra the llelpilots  locat(and ions parallel for base locations line defin forition) baseline  were definition)  used for wer  dee‐ used for developing and validating the CFD models to be used later. Monitoring data for the pilots veloping and validating the CFD models to be used later. Monitoring data for the pilots  correspond to 2019 and the pre-greening info dates back to 2017. correspond to 2019 and the pre‐greening info dates back to 2017.  Figure 2. Benicalap neighbourhood location, monitoring station and NBS pilots. Green roof eco‐ Figure 2. Benicalap neighbourhood location, monitoring station and NBS pilots. Green roof ecosystem system (1), Blue/Green corridor (2).  (1), Blue/Green corridor (2).     Buildings 2022, 12, x FOR PEER REVIEW  5  of  13  Buildings 2022, 12, 894 5 of 13 Table 1. KPIs for Valencia NBS pilots.  Responsible Service  Table 1. KPIs for Valencia NBS pilots. KPI  Policy  (All Links Accessed 22nd June 2022)  Temperature (°C)  Responsible Service KPI Policy Climate adaption  (All Links Accessed on 22 June 2022) Humidity (% RH)  http://climate‐adapt.eea.europa.eu/  (hourly records) Radiant T emperatur black‐bulb e (temper C) ature (°C)  Climate adaption Humidity (% RH) http://climate-adapt.eea.europa.eu/ NWRM platform  (hourly records) Water retention (L/day)  http://www.nwrm.eu/  Radiant black-bulb temperature ( C) (daily records)  NWRM platform Oppla NBS platform  Water retention (L/day) http://www.nwrm.eu/ Top wind speed (m/s)  https://oppla.eu/nbs/case‐studies  (daily records) (hourly records)  Oppla NBS platform Top wind speed (m/s) https://oppla.eu/nbs/case-studies (hourly records) Data  monitoring  from  the  NBSs  pilots  and  its  management  require  additional  measures described below:  Data monitoring from the NBSs pilots and its management require additional measures • Open Data standards following the “Open cities” platform developed by the Fraun‐ described below: hofer Institut in Fokus [24]. This platform supports the Open Data lifecycle process.  • Fiware  foundation  [25]  open  standards  used  for  device  communication  and  data  Open Data standards following the “Open cities” platform developed by the Fraun- gathering. This open standard provides an enhanced OpenStack‐based cloud envi‐ hofer Institut in Fokus [24]. This platform supports the Open Data lifecycle process. Fronment iware fo  ufor nd athe tion Internet [25] ope nofs tThings andard sdev useidcefs o ron de re vical e‐cti om me m appl unicic atat ion ion ansd while data g incor atherp ino g‐. T rating his op geo en sloc tanal dis arat dion. pro vides an enhanced OpenStack-based cloud environment for the Internet of Things devices on real-time applications while incorporating geolocalisation. • Monitoring data support the metadata INSPIRE Directive [22] proposed as the cod‐ Monitoring data support the metadata INSPIRE Directive [22] proposed as the coding ing standard of geolocalised data for the open display of relevant information from  standard of geolocalised data for the open display of relevant information from the the project. This will allow the joint publication of relevant NBSs data at the EU‐NBS  project. This will allow the joint publication of relevant NBSs data at the EU-NBS Think‐Nature cluster [26].  Think-Nature cluster [26]. • The selection of relevant Key Performance Indicators (KPIs) for the Valencia pilots  The selection of relevant Key Performance Indicators (KPIs) for the Valencia pilots was was performed according to the interoperability requirements agreed upon for all  performed according to the interoperability requirements agreed upon for all EU NBSs EU NBSs projects and is developed in Table 1. The details on relevant technical in‐ projects and is developed in Table 1. The details on relevant technical information on formation on KPIs are also given in Table 1.  KPIs are also given in Table 1. 2.3. Valencia Open Data Platform  2.3. Valencia Open Data Platform The Valencia Open Data platform [18] follows the ISO standard specification ISO/IEC  The Valencia Open Data platform [18] follows the ISO standard specification ISO/IEC 20802‐2:2016 [27] under a simple architecture (Figure 3) running under Oracle (platform‐ 20802-2:2016 [27] under a simple architecture (Figure 3) running under Oracle (platform- neutral), supporting all city datasets including the research data output from city projects,  neutral), supporting all city datasets including the research data output from city projects, while fulfilling the EU data policies and joint repositories [28].  while fulfilling the EU data policies and joint repositories [28]. Figure 3. Valencia Open Data conceptual architecture.  Figure 3. Valencia Open Data conceptual architecture. Figure 2 presents the reference infrastructure model and high‐level technical specifi‐ Figure 2 presents the reference infrastructure model and high-level technical specifica- tion cation for for all al data l data refer  referenc enceded in in this  this paper  pape,rincluding , includingits itsmain  maincomponents  components and and connection connection  points to other tools and systems. The reference architecture meets the technical and user requirements established throughout NBS pilot implementations of the H2020 Grow-Green Buildings 2022, 12, 894 6 of 13 project, addressing technology and user requirements and integrating monitored data onto the Benicalap Digital Twin. The reference architecture considers platform-neutral components, which provide a solution, simultaneously integrating all pilots, linking them to the architecture components of the Link Open Data chain. Data monitoring on pilots at a neighbourhood level was stored and managed through the Valencia Open Data platform. Variables for the Digital Twin are linked dynamically to the corresponding values of the Valencia Open Data platform, ensuring a model design that can be repeated for any neighbourhood with an equivalent structure for impact assessment. Therefore, the Digital Twin model is generic in its possible implementation. Although the full set of data is very wide, for the purpose of this research, only the hourly neighbourhood data records (for 2019 as detailed in Section 3.1) are considered. Pilot data records are used for training the CFD model, while stations away from NBS pilot influence are used as street initial conditions for the simulation of the full-scale deployment of NBSs. This data set serves as the climate baseline. 2.4. Façade Solutions and Building Model Each building is modelled according to its envelope surface (cadastral surface and height). Considering the building typology evaluated from the cadastral info (construction year) and the Episcope/TABULA tool [29] heat-exchange conditions (thermal conductivity, thermal capacity, and windows) are established for each building in the neighbourhood and included in the Digital Twin for evaluating energy transfer in each individual building prior to refurbishment. Through a careful state-of-the-art analysis [30–32] the overall reduction in energy consumption for buildings in urban environments, through better façade solutions, can achieve energy savings of 20 to 55%. Cumulative impact amounts to a possible carbon footprint reduction that could reach up to 12% of an average city [33]. The proposed refurbishing strategy concentrates on façades (adding a Phase Change Material, or PCM, layer) and windows (including radiation shields and double glass). For defining the simplified building solutions to be considered in our research, the following conceptual requirements have been used: Interventions on the existing building stock follow modular designs separately ad- dressing 3 action fields: # Windows: multiple layers including radiation reflective coatings. # External add-on skins facilitating passive skin ventilation, humidity barriers, and radiation heat reflectors. # Internal layers tuned to balance energy flows through thermal storage. Specific solutions address not only energy efficiency, but also overall sustainable designs evaluated through a comprehensive Life Cycle Assessment (LCA). The selected design approach for building solutions follows market-dominant ele- ments following multilayered sandwich solutions, supported by simple multipurpose fastening elements. Standard approaches are selected with an emphasis on quality for meeting a minimum 50-year building life cycle. The schematic design of the proposed retrofitting solution is presented in Figure 3 below with most relevant design parameters. For the evaluation of retrofitting policies, 3 different alternatives for Phase Change Materials are considered in Section 3.2 with the same support shown in Figure 4. These materials have been selected from the dominant market products [34]: organic paraffin, tetrahydrofuran clathrate hydrate, and caprylic acid with lauric acid (9:1 eutectic). Buildings 2022, 12, 894 7 of 13 Buildings 2022, 12, x FOR PEER REVIEW  7  of  13  Double glass window with radiation shield coating Watertight seal PCM heat storage Outer façade Structural Floor support PCM heat storage Façade Figure 4. Conceptual façade modular retrofit section. Figure 4. Conceptual façade modular retrofit section.  3. Modelling Outputs and Discussion 3. Modelling Outputs and Discussion  3.1. CFD Microclimate Model 3.1. CFD Microclimate Model  For selecting the CFD model best suiting the Benicalap neighbourhood, the extensive For selecting the CFD model best suiting the Benicalap neighbourhood, the extensive  review provided by Toparlar et al. [9] is followed. Since the neighbourhood is distributed as review provided by Toparlar et al. [9] is followed. Since the neighbourhood is distributed  a random grid of multiple street canyons connecting open green spaces, the governing equa- as a random grid of multiple street canyons connecting open green spaces, the governing  tions considered follow the Large Eddy Simulations (LES) approach, solved numerically equation using thes Dear consider doffed sub-grid  followscale  the Large model Eddy appr Sim oachul[a 35 tions ].  (LES) approach, solved numer‐ icallyT us hein dg o m the ai nDeardoff conside rsu edbf‐ogr rid th escale CFD model mode laipproa s enclch ose [3 d5] in. a rectangle aligned with the domiThe nan tdomain street d ic ro ecnsider tion aned d cfor irc uthe ms CFD cribi  nmodel g the l iis m enclo its of tshed e n in ei gah re boct uarn hgl ooed al (1igned 200  with 1400 the m,  dominant as present estr d eet in F dir iguerction e 2). Tand he hcoirc riumsc zontaribing l grid ithe nte rlim valit is s of 12 the m i n neighbourhood the x direction (1 an20 d01 x 4 1 m400 in  m, the as y d pr iresent ectioe nd , ain nd Fitgu herheo 2) ri.z The onta horizo l grid d ntal im egrid nsio int n iser1v 0al 0  is 12 10 0m .  A in s the for x th di e r peecrtpio en n dand icu l14 ar  grid, 50 non-uniform layers are considered. Layers closer to the surface are 3 m thick up to m in the y direction, and the horizontal grid dimension is 100 × 100. As for the perpendic‐ the 25th layer, increasing with a 1.1 expansion ratio from the 26th layer to the 35th layer, and ular grid, 50 non‐uniform layers are considered. Layers closer to the surface are 3 m thick  from here to the 50th layer a uniform thickness of 7.78 m is considered. The grid density up to the 25th layer, increasing with a 1.1 expansion ratio from the 26th layer to the 35th  selected follows the optimal performance according to [36]. layer, and from here to the 50th layer a uniform thickness of 7.78 m is considered. The grid  The physical parameters to be obtained from the climate model were previously pre- density selected follows the optimal performance according to [36].  sented in Table 1. The routine was selected from the Ansys Fluent (Ansys Inc., Canonsburg, The physical parameters to be obtained from the climate model were previously pre‐ PA, USA) software used and run using as input pre-greening temperature data together sented  in  Table  1.  The  routine  was  selected  from  the  Ansys  Fluent  (Ansys  Inc.,  Can‐ with the NBSs implemented (trees, green roofs, water reservoirs), and hourly radiation onsburg, PA, USA) software used and run using as input pre‐greening temperature data  information is taken from the VLC open data repository [37]. The geometries for each together with the NBSs implemented (trees, green roofs, water reservoirs), and hourly  Benicalap street pilot are obtained from the pilot area 3D models imported from the ArcGIS radiation information is taken from the VLC open data repository [37]. The geometries for  neighbourhood model. The CFD simulation results add to the given climate conditions, each Benicalap street pilot are obtained from the pilot area 3D models imported from the  latent heat and airflow impacts, for thermal variations. ArcGIS neighbourhood model. The CFD simulation results add to the given climate con‐ Once our climate model for each pilot area is established, the initial values for each day ditions, latent heat and airflow impacts, for thermal variations.  are selected from hourly average values from the pre-greening monitored data at 0:00 a.m. Once our climate model for each pilot area is established, the initial values for each  The climate model is then run for the whole day keeping daily extreme temperatures day are selected from hourly average values from the pre‐greening monitored data at 0:00  together with the other KPIs (from Table 1) at the time when the extreme temperatures a.m. The climate model is then run for the whole day keeping daily extreme temperatures  are reached. This process is repeated for each pilot through the 365 days of the year (2019) together with the other KPIs (from Table 1) at the time when the extreme temperatures  after the pilots are built obtaining a table for one years’ worth of daily simulated extreme are reached. This process is repeated for each pilot through the 365 days of the year (2019)  temperature and related KPIs (see Figure 5 for more details). after the pilots are built obtaining a table for one years’ worth of daily simulated extreme  temperature and related KPIs (see Figure 5 for more details).  Separation Buildings 2022, 12, 894 8 of 13 Buildings 2022, 12, x FOR PEER REVIEW  8  of  13  Buildings 2022, 12, x FOR PEER REVIEW  8  of  13  Dayclimate data Climate model Year simulation Day simulation Temperature/Humidity Dayclimate data Cl3Dimgateome emtryodel To = T real at 0:00 am Maximum‐Minimum per day, Ye All aKrP Iss immaux/lmatinion Day simulation Temperature/Humidity ANSYS fluent 3Dgeometry RadiationdataVLCcityportal Waterretention and Wind  per day To = T real at 0:00 am Maximum‐Minimum per day, All KPIs max/min LargeEddy Deardoff model speed at the same times ANSYS fluent RadiationdataVLCcityportal Waterretention and Wind  per day LargeEddy Deardoff model speed at the same times Figure 5. Climate model development process.  Figure 5. Climate model development process. Figure 5. Climate model development process.  Simulation Simulationr esults results show  show relevant  relevant changes  changes depending  depending on  the on the specific  speci point fic point consider  cons ed. id‐ Simulation results show relevant changes depending on the specific point consid‐ To avoid local variations in the CFD model, all values are averaged for each climate variable ered. To avoid local variations in the CFD model, all values are averaged for each climate  ered. To avoid local variations in the CFD model, all values are averaged for each climate  per variable street. per This street. allows This a simpler  allows calculation a simpler calc later ulat for ion ener  latgy er for requir  ener ements gy requirements without a r elevant without  variable per street. This allows a simpler calculation later for energy requirements without  impact a relevon antaccuracy  impact on [35 accuracy ]. The average  [35]. The simulated  averager esults simulated per str  results eet ar per e then  street compar  are then ed to com the‐ a relevant impact on accuracy [35]. The average simulated results per street are then com‐ real monitoring data after greening for the two pilot areas (see Figure 6 for error evolution pared to the real monitoring data after greening for the two pilot areas (see Figure 6 for  pared to the real monitoring data after greening for the two pilot areas (see Figure 6 for  for real and simulated values in Plaza Regino Mas). Results on the deviations show a error evolution for real and simulated values in Plaza Regino Mas). Results on the devia‐ error evolution for real and simulated values in Plaza Regino Mas). Results on the devia‐ Gaussian distribution for both pilots on the 365 days evaluated. Results of the statistical tions show a Gaussian distribution for both pilots on the 365 days evaluated. Results of  tions show a Gaussian distribution for both pilots on the 365 days evaluated. Results of  distributions per KPI and pilot are presented in Table 2. the statistical distributions per KPI and pilot are presented in Table 2.  the statistical distributions per KPI and pilot are presented in Table 2.  Figure 6. Daily error evolution for simulation on “Plaza Regino Mas”.  Figure 6. Daily error evolution for simulation on “Plaza Regino Mas”.  Figure 6. Daily error evolution for simulation on “Plaza Regino Mas”. Table 2. Error distributions for the climate variables on NBSs pilots.  Table 2. Error distributions for the climate variables on NBSs pilots. Table 2. Error distributions for the climate variables on NBSs pilots.  Pilot  KPI  Error (KPIe) Distribution  Pilot KPI Error (KPI ) Distribution Pilot  KPI  Error (KPIe) Distribution  Temperature (°C)  ΔT (av.) = 1.1, σ = 0.2  Temperature ( C) Temperature (°C)  ΔT (av.) = 1.1, σ = 0.2  Humidity (% RH)  ΔH (av.) = 2.8, σ = 0.5  DT (av.) = 1.1,  = 0.2 Green roof  Humidity (% RH) Humidity (% RH)  ΔH (av.) = 2.8, σ = 0.5  Radiant black‐bulb temperature (°C) ΔDR HT (av (av.) .) = =2.8,  0.9, σ  == 0.5 0.1  Green roof  Radiant black-bulb (1)    Radiant black‐bulb temperature (°C) DΔ RRT(av  (a.)v.) = = 0.9, 0.9,  σ  = 0.1 = 0.1  Water retention (L/day) ΔWater retention (av.) = 0.5, σ = 0.02  Green roof (1) temperature ( C) (1)  Water retention (L/day) ΔWater retention (av.) = 0.5, σ = 0.02  Wind speed (m/s) ΔWind speed (av.) = 2.3, σ = 0.4  Water retention (L/day) DWater retention (av.) = 0.5,  = 0.02 Wind speed (m/s) ΔWind speed (av.) = 2.3, σ = 0.4  Temperature (°C)  ΔT (av.) = 1.5, σ = 0.3  Wind speed (m/s) DWind speed (av.) = 2.3,  = 0.4 Temperature (°C)  ΔT (av.) = 1.5, σ = 0.3  Humidity (% RH)  ΔH (av.) = 2.6, σ = 0.4  Temperature ( C) Blue/green  Humidity (% RH)  ΔH (av.) = 2.6, σ = 0.4  Radiant black‐bulb temperature (°C) ΔDR TT  (av (av.) .) = = 1.5,  1.1, σ  = = 0.3 0.1  Blue/green  Humidity (% RH) corridor (2)    Radiant black‐bulb temperature (°C) DΔHRT (av (a.)v.) = 2.6, = 1.1,  σ  = 0.4 = 0.1  Water retention (L/day) ΔWater retention (av.) = 0.9, σ = 0.01  Radiant black-bulb corridor (2)  DR (av.) = 1.1,  = 0.1 Water retention (L/day) ΔWater retention (av.) = 0.9, σ = 0.01  Blue/green corridor (2) temperature ( C) Top wind speed (m/s) ΔWind speed (av.) = 2.9, σ = 0.6  Top wind speed (m/s) ΔWind speed (av.) = 2.9, σ = 0.6  Water retention (L/day) DWater retention (av.) = 0.9,  = 0.01 The results show a good performance of the model (less than 5% error), and the cli‐ Top wind speed (m/s) DWind speed (av.) = 2.9,  = 0.6 The results show a good performance of the model (less than 5% error), and the cli‐ mate model is therefore validated. The simulation outputs for the climate model include  mate model is therefore validated. The simulation outputs for the climate model include  not only the validation KPIs (temperature/humidity, water retention and wind speed), but  The results show a good performance of the model (less than 5% error), and the climate not only the validation KPIs (temperature/humidity, water retention and wind speed), but  also radiation, convection energy exchange conditions, and latent heat energy, which will  model is therefore validated. The simulation outputs for the climate model include not also radiation, convection energy exchange conditions, and latent heat energy, which will  be needed to evaluate the energy balance for Benicalap.  only the validation KPIs (temperature/humidity, water retention and wind speed), but also be needed to evaluate the energy balance for Benicalap.  Together  with  the  digital twin  of  Benicalap,  the  climate  model allows  the  perfor‐ radiation, convection energy exchange conditions, and latent heat energy, which will be Together  with  the  digital twin  of  Benicalap,  the  climate  model allows  the  perfor‐ mance assessment of the proposed interventions on the Benicalap neighbourhood consid‐ needed to evaluate the energy balance for Benicalap. mance assessment of the proposed interventions on the Benicalap neighbourhood consid‐ ering the streets and buildings: deployment of NBSs (pilots) 1 (on all public buildings)  ering the streets and buildings: deployment of NBSs (pilots) 1 (on all public buildings)  and 2 (on every street), together with any refurbishment strategies.  and 2 (on every street), together with any refurbishment strategies.  Buildings 2022, 12, 894 9 of 13 Together with the digital twin of Benicalap, the climate model allows the performance assessment of the proposed interventions on the Benicalap neighbourhood considering the streets and buildings: deployment of NBSs (pilots) 1 (on all public buildings) and 2 (on every street), together with any refurbishment strategies. 3.2. Neighbourhood Impact Assessment As described in Section 2.4, the neighbourhood digital model is built on ArcGIS, starting from geometry modelling for later embedding building typologies (taken from Tabula/Episcope [29]) and the corresponding materials’ relevant LCA data (density, raw materials carbon footprint, thermal conductivity, specific heat capacity, labour, and average price) structured as building units (as structured in IVE’s construction database [38]). Integrated in the same model (digital twin), the weather conditions (temperature/ humidity, water retention, wind speed, radiation, convection energy exchange conditions, and latent heat energy) are geolocated to an average value per street serving as outdoor stable weather conditions per hour in the reference year (2019 in our case). Individual building carbon footprints incorporate the energy requirements for home temperature regulation (using the most common equipment per building typology), plus fixed carbon footprint components (lighting, home activities). Both are added and aver- aged for the building envelopes in order to provide the yearly carbon footprint. Weather conditions are set to the real (first) and simulated street values (according to the green infrastructure model). By comparing results, it is possible to assess the possible impact on the carbon footprint of the proposed building retrofit policy in the neighbourhood, which is the key result of our research. The last process incorporated into the model concerns building energy requirements from the CFD climate model baseline. This task is performed through CEA [23] routines coded in Python (v.3.9.10, Python software foundation, Wilmington, DE, USA) implementation of the proposed building adaption strategies and is explained in Section 2.4. CEA evaluates all heat exchanges through building envelopes (radiation, convection, and conduction). CEA is selected due to its excellent acceptance and accuracy for the energy exchange evaluations, including finally the related CO footprint for building construction and use [20]. The Benicalap digital twin described here obtains the overall energy performance balance and associated carbon footprint for all the buildings in the neighbourhood under normal occupancy conditions and using the average climate adaption measures evaluated in [38]. All industrial buildings are only considered in the model with the fixed energy consumptions evaluated in [35]. Our Benicalap digital twin output graphically presents the CO footprint for private energy consumption, also including the embedded CO footprint from the construction phase. Starting with the real neighbourhood information on its current situation, the results consider the expected impact of green infrastructure and traffic on the average street temperature as described before. Each building is assessed independently according to its typology, and the calculated balance is delivered to the neighbourhood’s integrated digital model. The graphical results for the neighbourhood, without greening or building refurbishments, can be seen in Figure 7, showing the spatial variations in building carbon footprint distribution. The last step includes the combination of NBS infrastructure together with heat storage capabilities (through PCM sandwich solutions) already introduced in Section 2.4. Details on the commercial materials to be evaluated as alternative solutions for the whole neigh- bourhood façade renovation are presented in Table 3. Each of these materials are introduced on the façades of all residential buildings on the Benicalap digital twin and evaluated for their carbon footprint. The PCMs help in maintaining low energy consumption while shielding from extreme outer temperatures. Buildings 2022, 12, x FOR PEER REVIEW  9  of  13  3.2. Neighbourhood Impact Assessment  As described in Section 2.4, the neighbourhood digital model is built on ArcGIS, start‐ ing from geometry modelling for later embedding building typologies (taken from Tab‐ ula/Episcope [29]) and the corresponding materials’ relevant LCA data (density, raw ma‐ terials carbon footprint, thermal conductivity, specific heat capacity, labour, and average  price) structured as building units (as structured in IVE’s construction database [38]).  Integrated in the same model (digital twin), the weather conditions (temperature/hu‐ midity, water retention, wind speed, radiation, convection energy exchange conditions,  and latent heat energy) are geolocated to an average value per street serving as outdoor  stable weather conditions per   hour in the reference year (2019 in our case).  Individual building carbon footprints incorporate the energy requirements for home  temperature regulation (using the most common equipment per building typology), plus  fixed carbon footprint components (lighting, home activities). Both are added and aver‐ aged for the building envelopes in order to provide the yearly carbon footprint. Weather  conditions are set to the real (first) and simulated street values (according to the green  infrastructure model). By comparing results, it is possible to assess the possible impact on  the carbon footprint of the proposed building retrofit policy in the neighbourhood, which  is the key result of our research.  The last process incorporated into the model concerns building energy requirements  from the CFD climate model baseline. This task is performed through CEA [23] routines  coded in Python (v.3.9.10, Python software foundation, Wilmington, DE, USA) implemen‐ tation of the proposed building adaption strategies and is explained in point 2.4. CEA  evaluates all heat exchanges through building envelopes (radiation, convection, and con‐ duction).  CEA is selected due to its excellent acceptance and accuracy for the energy exchange  evaluations, including finally the related CO2 footprint for building construction and use  [20]. The Benicalap digital twin described here obtains the overall energy performance  balance and associated carbon footprint for all the buildings in the neighbourhood under  normal occupancy conditions and using the average climate adaption measures evaluated  in [38]. All industrial buildings are only considered in the model with the fixed energy  consumptions evaluated in [35].  Our Benicalap digital twin output graphically presents the CO2 footprint for private  energy consumption, also including the embedded CO2 footprint from the construction  phase. Starting with the real neighbourhood information on its current situation, the re‐ sults consider the expected impact of green infrastructure and traffic on the average street  temperature as described before.  Each building is assessed independently according to its typology, and the calculated  balance is delivered to the neighbourhood’s integrated digital model. The graphical re‐ Buildings 2022, 12, 894 10 of 13 sults for the neighbourhood, without greening or building refurbishments, can be seen in  Figure 7, showing the spatial variations in building carbon footprint distribution.  Figure 7. Graphical presentation of neighbourhood CO footprint per building. Table 3. PCMs to be assessed on Benicalap neighbourhood. Phase Change Thermal Name Melting Density Reference Enthalpy Conductivity Type Composition Temperature ( C) (Kg/m ) (Kj/Kg) (W/m K) Organic RT54HC [39] 53–54 200 0.20 800 paraffin Caprylic + lauric Organic [40] 3.8 151 0.20 835 acid (9:1 by mol) eutectic Tetrahydrofuran [41] 4.4 255 0.15 912 Inorganic clathrate hydrate The simulations are configured and run obtaining the results on Table 4 which will be developed in Section 4. Table 4. Carbon footprints after LCA in Benicalap neighbourhood. CO Footprint CO Footprint Total 2 2 Savings Simulation Embedded Usage CO Footprint (%) (Tn CO /year) (Tn CO /year) (Tn CO /year) 2 2 2 Current state 38.1 273.4 311.5 0.0 NBSs 40.1 256.2 296.3 4.9 NBSs + paraffin 43.7 221.1 264.8 15.0 NBSs + eutectic 42.8 232.0 274.8 11.7 NBSs + inorganic 41.5 216.7 258.2 17.1 4. Results The LCA evaluation is performed according to the ISO 14040:2006. After obtaining the results of the yearly simulation, it can be easily observed that the savings are very relevant for the low level of investment required for the interventions. These results are developed at neighbourhood level, although separate analyses can be performed per building typology, aggregating each individual building’s carbon footprint. The proposed methodology is very efficient providing excellent results compared to current alternative carbon footprint evaluations in buildings [42]. Buildings 2022, 12, 894 11 of 13 NBS green connectivity and roof installations provide not only a very relevant 5% energy consumption reduction, but also additional impact on water savings, avoidance of run-off water, and biodiversity protection. PCM solutions provide combined (with NBSs) savings ranging from 10 to 20% of technical problems related to encapsulation and durability for organic materials, but the inorganic solutions, when tuned in their heat storage capacity to the requirements (thickness can be easily adapted), can ensure a costless operation and energy savings (beyond the carbon footprint reduction benefits) that are very relevant to household economies. As identified by prior research [43], Building Energy Simulation has been extensively used during the design stage of modern buildings. The accuracy of the results depends on accurate monitoring together with historic climate records, along with oversimplification of the building types together with statistical variations on real implementations. Simulation error [44] ranges from 9 to 27% in different environments. The proposed digital twin has been tested to obtain simulation errors smaller than 9%. The building renovation wave, which is very active around Green Deal policies, has already incorporated similar solutions, which will render synergetic joint performance. Results go beyond the climate-neutral policies, and these building elements can be easily recycled, bringing down the carbon footprint by also allowing an easy combination with air circulation technologies to obtain additional benefits from natural conditions. 5. Conclusions This paper has developed an easy and accurate climate model to be used for evaluating energy performance at the city level. The proposed model has been implemented together with a joint digital twin for a neighbourhood allowing future developments for the impact assessment of urban policies. The building models also allow wide flexibility in building solution modelling, which will facilitate the performance analysis of new designs and architectural proposals. Urban development must be guided with technical evidence on combined effects for an adequate sustainability strategy. PCM performance opens many relevant synergy strategies with traditional climate- guided building designs and more advanced renewable energy integration on buildings. As a final summary, the combined deployment of the proposed techniques also allows relevant applications to many industrial processes. Author Contributions: Conceptualization, J.O.-M.; research design, J.O.-M. and M.I.-L.; methodology, J.O.-M. and R.C.-M.; experimental results and analysis, M.I.-L.; conclusions, J.O.-M. All authors have read and agreed to the published version of the manuscript. Funding: This research was co-funded by the European Commission through the H2020 project “Green Cities for Climate and Water Resilience, Sustainable Economic Growth, Healthy Citizens and Environments (GROW GREEN)” Grant Agreement: 730283. Institutional Review Board Statement: Not applicable. 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Journal

BuildingsMultidisciplinary Digital Publishing Institute

Published: Jun 24, 2022

Keywords: NBSs; CFD; urban carbon assessment model; PCM

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