Smart Solutions for Sustainable Cities—The Re-Coding Experience for Harnessing the Potential of Urban Rooftops
Abstract
:Featured Application
Abstract
1. Introduction
The Premises of the Research
- Showing an example of scientific investigation in support of the re-coding activity undertaken in conjunction with the Turin Municipality.
- Presenting a methodology able to evaluate the potential and feasibility of rooftop renovation in a built-up urban context.
- Evaluating the impact of smart rooftop solutions (insulated roof, green roof, high-reflectance roof, and energy production from solar energy) assessing energy savings, thermal comfort conditions, greenhouse gas (GHG) emissions, and social, environmental, and economic benefits.
- Identifying innovative building codes as an opportunity to promote rooftops’ renovation using smart solutions and technologies.
2. Materials and Methods
- (1)
- Geographic Information System (GIS) database: The main input data elaborated with the use of a GIS software and the output of the processing.
- (2)
- GIS tools: The tools used to analyze the buildings’ characteristics and the urban environment.
- (3)
- Roof suitability: The criteria used to evaluate the roof suitability according to architectural characteristics, morphological context, building codes, and regulations.
- (4)
- Roof solutions: The most effective rooftop strategies were identified to improve the livability conditions of the city of Turin, and the impact of smart technologies was investigated.
2.1. GIS Database: Input Data Collection and Processing
- Elevation models (raster data): The digital terrain model (DTM), with a precision of 10 m, describes the natural terrain. The digital elevation model (DEM), with a precision of 5 m, represents the bare-Earth surface, without natural or built features. The digital surface model (DSM), with a precision of 0.5 m and 5 m, represents the Earth’s surface including trees and buildings. These kinds of data were used to assess shadows’ effects on buildings and the surrounding’s urban context to quantify the solar radiation, taking into account the sun and sky models, and to evaluate the building characteristics such as roof slope and orientation [10].
- Satellite images (raster data) from Landsat 8, the operational land imager (OLI) and the thermal infrared scanner (TIRS) with a precision of 30 m, were used to analyze the land cover types and to calculate the albedo of the outdoor spaces, the presence of vegetation with the use of the normalized difference vegetation index (NDVI), and the land-surface temperature (LST) [11].
- Municipal technical map of the city (polygonal vector data), updated to 2019, gave information on a building’s footprint, type of users, number of buildings, number of floors or building height, period of construction, roof area, gross and net heated volume, net heated surface, and surface-to-volume (S/V) ratio. In addition, urban parameters were calculated with buildings’ information at blocks of buildings scale [14].
- ISTAT (Italian National Institute of Statistics) census section data (polygonal vector data), updated to 2011, gave information at block-of-building scale on people occupancy, number of inhabitants, number of families and family members, percentage of foreigners, gender, age, income, employment rate, socio-economic data (income at 2009), central or autonomous heating systems, and type of fuels.
- Urban parameters (polygonal vector data) at block-of-buildings scale were elaborated using Istat census database and municipal technical maps. The main variables were building density (BD), building height (BH), building coverage ratio (BCR), relative buildings’ height (H/Havg), canyon effect (H/W ratio), solar exposition, and main orientation of the streets (MOS) [15].
- Local climate data refers to weather stations’ measurements (punctual vector data) located in the city. Available hourly data refer to temperature, relative humidity, vapor pressure, and wind velocity of the outdoor air.
- Space heating and domestic hot water consumption data (punctual and polygonal vector data) were provided by the district heating IREN Company of the city. The annual, monthly, and hourly energy consumptions were processed and georeferenced. These data, used to design and validate urban-scale energy models, refer to three consecutive heating seasons: 2012–13, 2013–14, and 2014–15 [16,17,18].
- Energy performance certificates (EPCs) (punctual vector data) of the Piedmont Region gave information on residential buildings with 867,131 certificates in about 10 years. These data were used to evaluate the type of energy efficiency action and the impact retrofit interventions for the city of Turin [19].
2.2. GIS Tools: Analysis of Building and Roof Typologies
- Slope tool was used to assess the roof slope of each building using the DSM and the municipal technical map. From the simulation results, the roofs were classified into three categories: (1) Flat roofs with a slope <11°, identified as potential intensive green roofs; (2) pitched roofs with slope ≥11° and <20°, as potential extensive green roofs; and (3) and pitched roofs with slope ≥20° and <45°, as potential solar roofs [13].
- Aspect tool was used to assess the roof orientation using the DSM and the municipal technical map. Eight classes of roof surfaces’ orientation were identified according to aspect values (that varied between 0° and 360°). Considering slope values and roof orientation, the pitched roofs were classified into five categories: Gable roofs with North-South (N-S) orientation, gable roofs with East-West (E-W) orientation, hipped/pyramid roofs, shed roof, and half-hipped roof [20].
- Feature Analyst tool was used to analyze roof materials with orthophotos as input data [21] to classify surfaces according to the color tones. In addition, from orthophotos the three bands (red, green, and blue) were analyzed with a GIS tool in order to optimize the classification, identifying dark/black, medium, and light/white roofs’ colors.
- Area solar radiation tool was used to quantify the annual and monthly solar radiation values from the DSM. The quota of incident global solar radiation was quantified for each pixel (with a dimension of 0.5 m) and the hours of sunlight were calculated to identify sunny roofs (with three or more hours of sunlight) [22].
- Hillshade tool was used to create a shaded relief from the DSM by considering the illumination source angle and shadows, and in combination with other tools to evaluate roof-disturbing elements.
- Zonal Statistics tool was able to calculate statistics’ values of raster data for each roof surface. The roof-disturbing elements, such as dormers and antennas, were identified with the standard deviation using the orthophotos, the annual solar radiation analysis, and the hill–shade analysis. By overlapping the results of the statistical analysis, the disturbance percentage for each roof was assessed, identifying three classes of disturbance: 15, 25, and 35% [23].
2.3. Roof Suitability: Analysis of Criteria to Assess Rooftop Renovations’ Feasibility
- Building height had to be higher than 3.5 m for green and solar roofs, while for albedo strategies (high-reflectance roof) it had to be less than 3.5 m in order to have the greatest effect on near-surface air temperatures.
- Roof area had to be greater than 100 m2 for green roofs; for high-reflectance roofs, greater than 20 m2; and it had to be greater than 50 m2 for solar roofs.
- Roof material and color tones for green and high-reflectance roofs were excluded; roofs with high reflectance and vegetated roofs, solar roofs, roofs with red tiles and/or disturbing elements, such as dormers and/or antennas, were excluded.
- Roof slope had to be less than 11° (flat roofs) for intensive green roofs and between 11° and 20° for extensive green roofs. There is no limit for high-reflectance roofs and it had to be between 20° and 45° (pitched roofs) for solar roofs.
- Roof orientation with northern exposition was excluded for solar technology, as north-facing rooftops receive less sunlight.
- Solar radiation: Roof area should receive at least 1200 kWh/m2/year of annual solar radiation for solar technologies. The solar energy potential was investigated, identifying the available rooftop areas and quantifying the total solar radiation on the rooftop.
- Shadow effects: More than 3 h of sunlight for green roofs are necessary to allow the growth of vegetation. Therefore, the shaded roofs (less than 3 h of sunlight) were excluded. In addition, the shadowing effects are important for the selection of the most appropriate plant species for green roofs.
- Production of thermal from solar thermal (ST) collectors’ installation: At least 50% of the annual domestic hot water consumption must be covered by the ST production.
- Production of electricity from photovoltaic (PV) panels: The installed electric power, P, (in kW) must be greater than or equal to the value calculated with the following equation:
- P is the installed electric power (kW),
- K is a coefficient equal to 50 (m2/kW) after 1 January 2017, and
- A is the footprint area of the building (m2).
- Materials with high reflectance of roofs, assuming for the latter a solar reflectance value of not less than 0.65 in the case of flat roofs and 0.30 in the case of pitched roof.
- Passive cooling technologies (e.g., night ventilation and green roofs).
2.4. Impacts of Smart Roof Solutions and Technologies
2.4.1. Energy Efficiency Solutions
- is the solar irradiance entering the system (W/m2);
- is the incident solar irradiance (W/m2);
- is the short-wave extinction coefficient (-), which was assumed to equal 0.29 (values proposed for similar vegetation characteristics in [40]); and
with: | with: | |
- is the energy savings during the heating season (Wh);
- is the energy savings during the cooling season (Wh);
- is the roof area (m2);
- is the thermal transmittance of the roof (W/m2/K);
- is the thermal resistance of the roof (m2K/W);
- is the internal air temperature during the heating season equal to 20 °C;
- is the internal air temperature during the cooling season equal to 26 °C;
- is the sol–air temperature, which was introduced to take into account not only the external air temperature but also the incident solar irradiation absorbed by the roof (°C);
- is the internal surface temperature of the roof (°C);
- is the external air temperature (°C);
- is the solar absorption of the roof (-);
- is the incident solar irradiance (W/m2), which with green roof was equal to (see Equation (2)); and
- is the external thermal adductance (W/m2/K).
- is the primary energy savings during the heating season (Wh);
- is the primary energy savings during the cooling season (Wh);
- is the average seasonal efficiency of the heating system (in Italy, for residential buildings, this value varies between 0.65 and 0.75 (-)) [17]; and
- is the average seasonal energy efficiency ratio, which depends on the efficiency of air conditioners (in Italy, for a typical heat pump (air/air) this value is about 3).
Green Roof Technology
High-Reflectance Roof Strategy
- is the steady-state temperature of a black surface (K) with solar reflectance of 0.05 and infrared emittance of 0.9, under the standard solar and ambient conditions with a solar flux of 1000 Wm−2, ambient air temperature of 310 K, convective coefficient of 12 Wm−2·K−1 surfaces, and apparent sky temperature of 300 K;
- is the steady-state temperature of a white surface (K) with solar reflectance of 0.80 and infrared emittance of 0.9, under standard solar and ambient conditions;
- is the temperature of the roof surface (K) under the standard solar and ambient conditions;
- is the solar absorptance of the roof surface (-) equal to ;
- is the solar reflectance of the roof surface (-); and
- is the Stefan–Boltzmann constant, 5.67 × 10−8 (Wm−2·K−4). Table 4 shows typical roofing materials with solar absorption (α), solar reflectance (ρ), and infrared emittance (ε) values used in this work to quantify SRI and Ts before and after roof renovation using the albedo strategy.
Solar Energy Technology
- For the residential sector, space heating consumption refers to measured data for the season 2013/2014 [17] and domestic hot water consumption was calculated taking into account that a person needs 50 L of water per day at a temperature of 45 °C (water temperature variation is 30 °C). For the nonresidential sector, space heating and domestic hot water consumption were quantified knowing, for different users, the specific consumption in kWh/m3 and the heated volume (m3) [16];
- For the residential sector, electrical consumption refers to the average monthly consumption of 1206 families for the years 2013 and 2014 [61]. For the nonresidential sector, electrical consumption (kWhel) was quantified knowing specific annual consumption in kWhel/m3 and the heated volume (m3) [62].
3. Results
3.1. Model Application
3.2. Smart Roof Solutions’ Assessment
3.2.1. Green Roof Technology
3.2.2. High-Reflectance Roof Strategy
3.2.3. Solar Energy Technology
3.3. Energy Savings: Heating and Cooling
4. Discussion
4.1. Impact Assessment of Urban Rooftops’ Renovation
4.1.1. Environmental, Social, and Economic Benefits
Environmental Benefits
Economic Benefits
Social Benefits
4.2. Smart Green Policies for Rooftop Renovation and Management
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
- Shen, B.; Ghatikar, G.; Lei, Z.; Li, J.; Wikler, G.; Martin, P. The role of regulatory reforms, market changes, and technology development to make demand response a viable resource in meeting energy challenges. Appl. Energy 2014, 130, 814–823. [Google Scholar] [CrossRef]
- Lehnerer, A. Grand Urban Rules; 010 Publishers: Rotterdam, The Netherlands, 2009. [Google Scholar]
- Marshall, S. Urban Coding and Planning; Routledge: London, UK; New York, NY, USA, 2011. [Google Scholar]
- Moroni, S.; Buitelaar, E.; Sorel, N.; Cozzolino, S. Simple Planning Rules for Complex Urban Problems: Toward Legal Certainty for Spatial Flexibility. J. Plan. Educ. Res. 2018, 40, 320–331. [Google Scholar] [CrossRef]
- Oswalt, P.; Overmeyer, K.; Misselwitz, P. Urban Catalyst: The Power of Temporary Use; DOM Publishers: Berlin, Germany, 2013. [Google Scholar]
- Baum, M.; Christiaanse, K. City as Loft: Adaptive Reuse as a Resource for Sustainable Urban Development; Gta Verl: Zürich, Switzerland, 2012. [Google Scholar]
- Slaughter, E.S. Implementation of construction innovations. Build. Res. Inf. 2000, 28, 2–17. [Google Scholar] [CrossRef]
- Nigra, M.; Dimitrijevic, B. Is radical innovation in architecture crucial to sustainability? Lessons from three Scottish contemporary buildings. Arch. Eng. Des. Manag. 2018, 14, 272–291. [Google Scholar] [CrossRef]
- Mutani, G.; Todeschi, V. An Urban Energy Atlas and Engineering Model for Resilient Cities. Int. J. Heat Technol. 2019, 37, 936–947. [Google Scholar] [CrossRef] [Green Version]
- Mutani, G.; Todeschi, V.; Matsuo, K. Urban Heat Island Mitigation: A GIS-based Model for Hiroshima. Instrum. Mes. Métrologie 2019, 18, 323–335. [Google Scholar] [CrossRef]
- Taha, H.; Sailor, D.; Municipal, S. High-Albedo Materials for Reducing Building Cooling Energy Use. Energy; U.S. Department of Energy Office of Scientific and Technical Information: Oak Ridge, TN, USA, 1992. [Google Scholar] [CrossRef] [Green Version]
- Mutani, G.; Todeschi, V. The Effects of Green Roofs on Outdoor Thermal Comfort, Urban Heat Island Mitigation and Energy Savings. Atmosphere 2020, 11, 123. [Google Scholar] [CrossRef] [Green Version]
- Mutani, G.; Todeschi, V.; Kampf, J.; Coors, V.; Fitzky, M. Building energy consumption modeling at urban scale: Three case studies in Europe for residential buildings. In Proceedings of the 2018 IEEE International Telecommunications Energy Conference (INTELEC), Turin, Italy, 7–11 October 2018; pp. 1–8. [Google Scholar]
- Boghetti, R.; Fantozzi, F.; Kämpf, J.; Mutani, G.; Salvadori, G.; Todeschi, V. Building energy models with Morphological urban-scale parameters: A case study in Turin. In Proceedings of the BSA: Building Simulation Applications, Bozen-Bolzano, South Tyrol, Italy, 19–21 June 2019; ISBN 978-88-6046-176-6. [Google Scholar]
- Mutani, G.; Todeschi, V. Space heating models at urban scale for buildings in the city of Turin (Italy). Energy Procedia 2017, 122, 841–846. [Google Scholar] [CrossRef] [Green Version]
- Mutani, G.; Todeschi, V. Building energy modeling at neighborhood scale. Energy Effic. 2020, 13, 1353–1386. [Google Scholar] [CrossRef]
- Mutani, G.; Todeschi, V.; Beltramino, S. Energy Consumption Models at Urban Scale to Measure Energy Resilience. Sustainability 2020, 12, 5678. [Google Scholar] [CrossRef]
- Mutani, G.; Gabrielli, C.; Nuvoli, G. Energy Performance Certificates Analysis in Piedmont Region (IT). A New Oil Field Never Exploited Has Been Discovered. Tec. Ital. J. Eng. Sci. 2020, 64, 71–82. [Google Scholar] [CrossRef] [Green Version]
- Zheng, Y.; Weng, Q.; Zheng, Y. A Hybrid Approach for Three-Dimensional Building Reconstruction in Indianapolis from LiDAR Data. Remote. Sens. 2017, 9, 310. [Google Scholar] [CrossRef] [Green Version]
- Overwatch Textron Systems. Feature Analyst 5.2 Reference Guide; Overwatch Textron Systems: Austin, TX, USA, 2007. [Google Scholar]
- Mutani, G.; Todeschi, V. Urban Building Energy Modeling: Hourly energy balance model of residential buildings at district scale. J. Phys. Conf. Ser. 2020, 1599. [Google Scholar] [CrossRef]
- Mutani, G.; Todeschi, V. Low-Carbon Strategies for Resilient Cities: A Place-Based Evaluation of Solar Technologies and Green Roofs Potential in Urban Contexts. Tec. Ital. J. Eng. Sci. 2020, 64, 193–201. [Google Scholar] [CrossRef]
- Botham-Myint, D.; Recktenwald, G.W.; Sailor, D.J. Thermal footprint effect of rooftop urban cooling strategies. Urban Clim. 2015, 14, 268–277. [Google Scholar] [CrossRef] [Green Version]
- Santos, T.; Tenedório, J.A.; Gonçalves, J.A. Quantifying the City’s Green Area Potential Gain Using Remote Sensing Data. Sustainability 2016, 8, 1247. [Google Scholar] [CrossRef] [Green Version]
- Hong, T.; Lee, M.; Koo, C.; Jeong, K.; Kim, J. Development of a method for estimating the rooftop solar photovoltaic (PV) potential by analyzing the available rooftop area using Hillshade analysis. Appl. Energy 2017, 194, 320–332. [Google Scholar] [CrossRef]
- Aparicio-Gonzalez, E.; Domingo-Irigoyen, S.; Snachez-Ostiz, A. Rooftop extension as a solution to reach nZEB in building renovation. Application through typology classification at a neighborhood level. Sustain. Cities Soc. 2020, 57, 102109. [Google Scholar] [CrossRef]
- Urban, B.; Roth, K. Guidelines for Selecting Cool Roofs; United States Department of Energy: Washington, DC, USA, 2010; pp. 1–23. [Google Scholar]
- Canto-Perello, J.; Martinez-Garcia, M.P.; Curiel-Esparza, J.; Martin-Utrillas, M. Implementing Sustainability Criteria for Selecting a Roof Assembly Typology in Medium Span Buildings. Sustainability 2015, 7, 6854–6871. [Google Scholar] [CrossRef] [Green Version]
- Suter, I.; Maksimović, Č.; Van Reeuwijk, M. A neighbourhood—Scale estimate for the cooling potential of green roofs. Urban Clim. 2017, 20, 33–45. [Google Scholar] [CrossRef] [Green Version]
- Shafique, M.; Kim, R.; Rafiq, M. Green roof benefits, opportunities and challenges—A review. Renew. Sustain. Energy Rev. 2018, 90, 757–773. [Google Scholar] [CrossRef]
- Ziogou, I.; Michopoulos, A.; Voulgari, V.; Zachariadis, T. Implementation of green roof technology in residential buildings and neighborhoods of Cyprus. Sustain. Cities Soc. 2018, 40, 233–243. [Google Scholar] [CrossRef]
- Tang, M.; Zheng, X. Experimental study of the thermal performance of an extensive green roof on sunny summer days. Appl. Energy 2019, 242, 1010–1021. [Google Scholar] [CrossRef]
- Yang, J.; Bou-Zeid, E. Scale dependence of the benefits and efficiency of green and cool roofs. Landsc. Urban Plan. 2019, 185, 127–140. [Google Scholar] [CrossRef]
- Dong, J.; Lin, M.; Zuo, J.; Tao, L.; Liu, J.; Sun, C.; Luo, J. Quantitative study on the cooling effect of green roofs in a high-density urban Area—A case study of Xiamen, China. J. Clean. Prod. 2020, 255, 120152. [Google Scholar] [CrossRef]
- Dimitrijević, B. (Ed.) Innovations for Sustainable Building Design and Refurbishment in Scotland; Springer International Publishing: New York, NY, USA, 2013. [Google Scholar] [CrossRef]
- Dimitrijevic, B.; Langford, D. Assessment Focus for More Sustainable Buildings. In Proceedings of the SUE-MoT International Conference on Whole Life Urban Sustainability and Its Assessment, Glasgow, UK, 27–29 June 2007. [Google Scholar]
- He, Y.; Yu, H.; Ozaki, A.; Dong, N. Thermal and energy performance of green roof and cool roof: A comparison study in Shanghai area. J. Clean. Prod. 2020, 267, 122205. [Google Scholar] [CrossRef]
- D’Orazio, M.; Di Perna, C.; Di Giuseppe, E. Green roof yearly performance: A case study in a highly insulated building under temperate climate. Energy Build. 2012, 55, 439–451. [Google Scholar] [CrossRef]
- Del Barrio, E.P. Analysis of the green roofs cooling potential in buildings. Energy Build. 1998, 27, 179–193. [Google Scholar] [CrossRef]
- Sanchez, L.; Reames, T.G. Cooling Detroit: A socio-spatial analysis of equity in green roofs as an urban heat island mitigation strategy. Urban For. Urban Green. 2019, 44, 126331. [Google Scholar] [CrossRef]
- Hoelscher, M.-T.; Nehls, T.; Jänicke, B.; Wessolek, G. Quantifying cooling effects of facade greening: Shading, transpiration and insulation. Energy Build. 2016, 114, 283–290. [Google Scholar] [CrossRef]
- Mahmoud, A.S.; Asif, M.; Hassanain, M.A.; Babsail, M.O.; Sanni-Anibire, M.O. Energy and Economic Evaluation of Green Roofs for Residential Buildings in Hot-Humid Climates. Buildings 2017, 7, 30. [Google Scholar] [CrossRef]
- Ng, E.; Chen, L.; Wang, Y.; Yuan, C. A study on the cooling effects of greening in a high-density city: An experience from Hong Kong. Build. Environ. 2012, 47, 256–271. [Google Scholar] [CrossRef]
- Cascone, S.; Catania, F.; Gagliano, A.; Sciuto, G. A comprehensive study on green roof performance for retrofitting existing buildings. Build. Environ. 2018, 136, 227–239. [Google Scholar] [CrossRef]
- Zhang, L.; Deng, Z.; Liang, L.; Zhang, Y.; Meng, Q.; Wang, J.; Santamouris, M. Thermal behavior of a vertical green facade and its impact on the indoor and outdoor thermal environment. Energy Build. 2019, 204, 109502. [Google Scholar] [CrossRef]
- Peng, L.L.; Jiang, Z.; Yang, X.; He, Y.; Xu, T.; Chen, S.S. Cooling effects of block-scale facade greening and their relationship with urban form. Build. Environ. 2020, 169, 106552. [Google Scholar] [CrossRef]
- Krarti, M. Integrated Design of Energy Efficient Cities. In Optimal Design and Retrofit of Energy Efficient Buildings, Communities, and Urban Centers; Butterworth-Heinemann: Oxford, UK, 2018. [Google Scholar] [CrossRef]
- Akbari, H.; Pomerantz, M.; Taha, H. Cool surfaces and shade trees to reduce energy use and improve air quality in urban areas. Sol. Energy 2001, 70, 295–310. [Google Scholar] [CrossRef]
- Oleson, K.W.; Bonan, G.B.; Feddema, J. Effects of white roofs on urban temperature in a global climate model. Geophys. Res. Lett. 2010, 37. [Google Scholar] [CrossRef] [Green Version]
- MacIntyre, H.; Heaviside, C. Potential benefits of cool roofs in reducing heat-related mortality during heatwaves in a European city. Environ. Int. 2019, 127, 430–441. [Google Scholar] [CrossRef]
- Santamouris, M.; Ban-Weiss, G.; Osmond, P.; Paolini, R.; Synnefa, A.; Cartalis, C.; Muscio, A.; Zinzi, M.; Morakinyo, T.E.; Ng, E.; et al. Progress in Urban Greenery Mitigation Science—Assessment Methodologies Advanced Technologies and Impact On Cities. J. Civ. Eng. Manag. 2018, 24, 638–671. [Google Scholar] [CrossRef] [Green Version]
- Akbari, H.; Levinson, R. Evolution of Cool-Roof Standards in the US. Adv. Build. Energy Res. 2008, 2, 1–32. [Google Scholar] [CrossRef]
- Berdahl, P.; Akbari, H.; Rose, L. Aging of reflective roofs: Soot deposition. Appl. Opt. 2002, 41, 2355–2360. [Google Scholar] [CrossRef] [PubMed]
- Akbari, H.; Berhe, A.; Levinson, R.; Graveline, S.; Foley, K.; Delgado, A. Aging and Weathering of Cool Roofing Membranes; Lawrence Berkeley National Lab: Berkeley, CA, USA, 2005. [Google Scholar]
- Akbari, H.; Berdahl, P.; Levinson, R.M.; Wiel, S.; Miller, W.A.; Desjarlais, A. Cool Color Roofing Materials; Energy Technologies Area: Berkley, CA, US, 2006. [Google Scholar]
- Kodysh, J.B.; Omitaomu, O.A.; Bhaduri, B.L.; Neish, B.S. Methodology for estimating solar potential on multiple building rooftops for photovoltaic systems. Sustain. Cities Soc. 2013, 8, 31–41. [Google Scholar] [CrossRef]
- Schindler, B.Y.; Blaustein, L.; Lotan, R.; Shalom, H.; Kadas, G.J.; Seifan, M. Green roof and photovoltaic panel integration: Effects on plant and arthropod diversity and electricity production. J. Environ. Manag. 2018, 225, 288–299. [Google Scholar] [CrossRef] [PubMed]
- Song, X.; Huang, Y.; Zhao, C.; Liu, Y.; Lu, Y.; Chang, Y.; Yang, J. An Approach for Estimating Solar Photovoltaic Potential Based on Rooftop Retrieval from Remote Sensing Images. Energies 2018, 11, 3172. [Google Scholar] [CrossRef] [Green Version]
- Mutani, G.; Todeschi, V.; Novo, R.; Mattiazzo, G.; Tartaglia, A. Le Isole Minori tra Sole, Mare e Vento; ENEA: Rome, Italy, 2019. (In Italian) [Google Scholar]
- Mutani, G.; Pastorelli, M.; De Bosio, F. A model for the evaluation of thermal and electric energy consumptions in residential buildings: The case study in Torino (Italy). In Proceedings of the International Conference on Renewable Energy Research and Applications (ICRERA), Glasgow, UK, 27–30 September 2015; pp. 1399–1404. [Google Scholar] [CrossRef]
- Mutani, G.; Beltramino, S.; Forte, A. A Clean Energy Atlas for Energy Communities in Piedmont Region (Italy). Int. J. Des. Nat. Ecodyn. 2020, 15, 343–353. [Google Scholar] [CrossRef]
- La Roche, P.; Berardi, U. Comfort and energy savings with active green roofs. Energy Build. 2014, 82, 492–504. [Google Scholar] [CrossRef]
- Rakotondramiarana, H.T.; Ranaivoarisoa, T.F.; Morau, D. Dynamic Simulation of the Green Roofs Impact on Building Energy Performance, Case Study of Antananarivo, Madagascar. Buildings 2015, 5, 497–520. [Google Scholar] [CrossRef] [Green Version]
- Costanzo, V.; Evola, G.; Marletta, L. Energy savings in buildings or UHI mitigation? Comparison between green roofs and cool roofs. Energy Build. 2016, 114, 247–255. [Google Scholar] [CrossRef]
- Silva, C.M.; Gomes, M.G.; Silva, M. Green roofs energy performance in Mediterranean climate. Energy Build. 2016, 116, 318–325. [Google Scholar] [CrossRef]
- Bevilacqua, P.; Bruno, R.; Arcuri, N. Green roofs in a Mediterranean climate: Energy performances based on in-situ experimental data. Renew. Energy 2020, 152, 1414–1430. [Google Scholar] [CrossRef]
- Kumar, V.; Prasad, L. Performance Analysis of Three-sides Concave Dimple Shape Roughened Solar Air Heater. J. Sustain. Dev. Energy Water Environ. Syst. 2018, 6, 631–648. [Google Scholar] [CrossRef]
- Gann, D.M.; Salter, A.J. Innovation in project-based, service-enhanced firms: The construction of complex products and systems. Res. Policy 2000, 29, 955–972. [Google Scholar] [CrossRef]
- Bossink, B. Eco-Innovation and Sustainability Management; Informa UK Limited: London, UK, 2013. [Google Scholar]
- Berardi, U.; GhaffarianHoseini, A.; GhaffarianHoseini, A. State-of-the-art analysis of the environmental benefits of green roofs. Appl. Energy 2014, 115, 411–428. [Google Scholar] [CrossRef]
- Vijayaraghavan, K. Green roofs: A critical review on the role of components, benefits, limitations and trends. Renew. Sustain. Energy Rev. 2016, 57, 740–752. [Google Scholar] [CrossRef]
- Castleton, H.; Stovin, V.; Beck, S.; Davison, J. Green roofs; building energy savings and the potential for retrofit. Energy Build. 2010, 42, 1582–1591. [Google Scholar] [CrossRef]
- Boixo, S.; Diaz-Vicente, M.; Colmenar-Santos, A.; Castro, M.A. Potential energy savings from cool roofs in Spain and Andalusia. Energy 2012, 38, 425–438. [Google Scholar] [CrossRef]
- Rosado, P.J.; Faulkner, D.; Sullivan, D.P.; Levinson, R. Measured temperature reductions and energy savings from a cool tile roof on a central California home. Energy Build. 2014, 80, 57–71. [Google Scholar] [CrossRef] [Green Version]
- New, J.R.; Miller, W.A.; Huang, Y.J.; Levinson, R. Comparison of software models for energy savings from cool roofs. Energy Build. 2016, 114, 130–135. [Google Scholar] [CrossRef] [Green Version]
- Seifhashemi, M.; Capra, B.R.; Milller, W.; Bell, J. The potential for cool roofs to improve the energy efficiency of single storey warehouse-type retail buildings in Australia: A simulation case study. Energy Build. 2018, 158, 1393–1403. [Google Scholar] [CrossRef]
- Akbari, H.; Menon, S.; Rosenfeld, A. Global cooling: Increasing world-wide urban albedos to offset CO2. Clim. Chang. 2008, 94, 275–286. [Google Scholar] [CrossRef]
- Sproul, J.; Wan, M.P.; Mandel, B.H.; Rosenfeld, A.H. Economic comparison of white, green, and black flat roofs in the United States. Energy Build. 2014, 71, 20–27. [Google Scholar] [CrossRef]
- Chiri, G.M.; Achenza, M.; Canì, A.; Neves, L.; Tendas, L.; Ferrari, S. The Microclimate Design Process in Current African Development: The UEM Campus in Maputo, Mozambique. Energies 2020, 13, 2316. [Google Scholar] [CrossRef]
- Teotónio, I.; Silva, C.M.; Cruz, C.O. Eco-solutions for urban environments regeneration: The economic value of green roofs. J. Clean. Prod. 2018, 199, 121–135. [Google Scholar] [CrossRef]
- Bianchini, F.; Hewage, K. Probabilistic social cost-benefit analysis for green roofs: A lifecycle approach. Build. Environ. 2012, 58, 152–162. [Google Scholar] [CrossRef]
- Corcelli, F.; Fiorentino, G.; Petit-Boix, A.; Rieradevall, J.; Gabarrell, X. Transforming rooftops into productive urban spaces in the Mediterranean. An LCA comparison of agri-urban production and photovoltaic energy generation. Resour. Conserv. Recycl. 2019, 144, 321–336. [Google Scholar] [CrossRef] [Green Version]
- Mahdiyar, A.; Tabatabaee, S.; Abdullah, A.; Marto, A. Identifying and assessing the critical criteria affecting decision-making for green roof type selection. Sustain. Cities Soc. 2018, 39, 772–783. [Google Scholar] [CrossRef]
- Wang, M.; Mao, X.; Gao, Y.; He, F. Potential of carbon emission reduction and financial feasibility of urban rooftop photovoltaic power generation in Beijing. J. Clean. Prod. 2018, 203, 1119–1131. [Google Scholar] [CrossRef]
- Oberndorfer, E.; Lundholm, J.; Bass, B.; Coffman, R.R.; Doshi, H.; Dunnett, N.; Gaffin, S.; Köhler, M.; Liu, K.K.Y.; Rowe, B. Green Roofs as Urban Ecosystems: Ecological Structures, Functions, and Services. Bioscience 2007, 57, 823–833. [Google Scholar] [CrossRef]
- Williams, K.J.; Lee, K.E.; Sargent, L.; Johnson, K.A.; Rayner, J.; Farrell, C.; Miller, R.E.; Williams, N.S. Appraising the psychological benefits of green roofs for city residents and workers. Urban For. Urban Green. 2019, 44. [Google Scholar] [CrossRef]
- Barioglio, C.; Campobenedetto, D.; Marianna, N.; Barale, M.F.; Frassoldati, F.; Robiglio, M. Re-Coding—Ripensare le Regole Della Città; Department of Architecture (DAD), Politecnico di Torino: Torino, Italy, 2019; ISBN 978-88-85745-28-5. (In Italian) [Google Scholar]
- Leach, M.; Rockström, J.; Raskin, P.; Scoones, I.; Stirling, A.; Smith, A.; Thompson, J.; Millstone, E.; Ely, A.; Arond, E.; et al. Transforming Innovation for Sustainability. Ecol. Soc. 2012, 17, 11–16. [Google Scholar] [CrossRef] [Green Version]
- Mumford, L. Stick and Stones, a Study on American Architecture and Civilization; Dover Publications: New York, NY, USA, 1955. [Google Scholar]
Case Study | Type of Change | |
---|---|---|
New York: Roof as layer | Modular | OneNYC 2050 and Climate Mobilization Act pushed for converting the majority of city roofs into green layers |
Ch2 di Melbourne: Roof as system | System | The roof of this project is conceived to host technical function to improve the overall energy performance of the building |
La Friche, Marseille: Roof as City | Architectural | This project change the use of the roof by conferring it the idea of extending the surface of the public city above a private building |
‘Quel temps fera-t-il demain’, Paris: Roof as fith face | Incremental | This project show a small change in the use of the roof, which is treated as the fifth facade of the building by using its surface as a base for street art |
MAAT Museum of Art, Architecture and Technology, Lisbon: Roof as infrastructure | Radical | This project offers the example of a roof that, by extending itself to the city becomes an infrastructure |
Criteria | Green Roof | High-Reflectance Roof | Solar Technology |
---|---|---|---|
Building height | >3.5 m (heated building) | ≤3.5 m (low building) | >3.5 m (heated building) |
Roof area | >100 m2 | >20 m2 | >50 m2 |
Roof material/color tones | No high-reflectance, vegetated and red-tiled roofs | No high-reflectance, vegetated and red-tiled roofs | No red-tiles roofs No disturbing element |
Roof slope | <11° intensive (flat) ≥11° and <20° extensive (pitched) | <8.5° low sloped ≥8.5° steep sloped | ≥20° and <45° pitched |
Roof orientation | No limit | No limit | No North exposition |
Solar radiation | Related to shadow criterion | No limit | ≥1200 kWh/m2/year |
Shadow effects | Sunny roofs with more than 3 h of sunlight | No limit | Related to solar radiation criterion |
Documents | Credits | Application | SRI Threshold Value |
---|---|---|---|
LEED 2009 Itaca | 1 point | Roofs | At least 75% of the roof surface must consist of material having: SRI ≥ 78 for low sloped roofs (<8.5°) and SRI ≥ 29 for steep sloped roofs |
GBC HOME | 2 points | Roofs | At least 50% of the roof surface must consist of material having: SRI ≥ 82 for low sloped roofs and SRI ≥ 29 for steep sloped roofs (>8.5°) |
GBC HISTORIC BUILDING | 2 points | High-reflectance roofs | |
Ministerial Decree 11/01/2017 | - | Roofs | SRI ≥ 29 for roofs with slope greater than 8.5° and SRI ≥ 76 for roofs with slope less than or equal to 8.5° |
Roof Material | α (-) | ρ (-) | ε (-) | Ts (°C) | SRI (-) |
---|---|---|---|---|---|
Smooth bitumen | 0.94 | 0.06 | 0.86 | 83 | −0.1 |
Generic black shingle | 0.95 | 0.05 | 0.91 | 82 | 0.1 |
Vegetated field | 0.90 | 0.10 | 0.76 | 83 | −0.2 |
Grey EPDM | 0.77 | 0.23 | 0.87 | 68 | 0.21 |
Red clay tile | 0.67 | 0.33 | 0.90 | 69 | 0.36 |
Red concrete tile | 0.82 | 0.18 | 0.91 | 76 | 0.17 |
Shasta white shingle | 0.74 | 0.26 | 0.91 | 64 | 0.27 |
Light gravel | 0.66 | 0.34 | 0.90 | 57 | 0.37 |
Aluminum | 0.39 | 0.61 | 0.25 | 48 | 0.56 |
White EPDM | 0.31 | 0.69 | 0.87 | 25 | 0.84 |
White coating on shingle | 0.29 | 0.71 | 0.91 | 23 | 0.87 |
White PVC | 0.17 | 0.83 | 0.92 | 11 | 1.04 |
Roof Solutions | No Buildings | Heightavg (m) | Potential Roof Area (m2) | Slopeavg (°) |
---|---|---|---|---|
Green roof | 110 | 13.6 | 64,712 | 0 |
High reflectance roof | 417 | 3.6 | 44,956 | 9 |
Solar roof | 570 | 19.3 | 172,749 | 36 |
Area | Roof | Well Exposed with No Disturbances (15–35%) | ST for DHW Energy-Use | PV 1/50 kW/m2 | PV max |
---|---|---|---|---|---|
m2 | 172,749 | 101,048 | 4717 | 21,141 | 96,631 |
100% | 58% | 2.7% | 12.2% | 55.8% |
Roof Solutions | A (-) | U (W/m2/K) | QH (Wh/m2) | QC (Wh/m2) | Δ QH (Wh/m2) | ΔQC (Wh/m2) | GHGH (tCO2/MWh) | GHGC (tCO2/MWh) |
---|---|---|---|---|---|---|---|---|
Common | 0.60 | 1.80 | 76,838 | 32,135 | - | - | 1333 | 319 |
Common insulated | 0.60 | 0.24 | 10,245 | 4285 | 88,790 | 9284 | 178 | 43 |
Insulated white | 0.30 | 0.24 | 10,874 | 2147 | 87,951 | 9996 | 189 | 21 |
Insulated green | 0.87 | 0.24 | 11,130 | 1457 | 87,611 | 10,226 | 193 | 14 |
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Todeschi, V.; Mutani, G.; Baima, L.; Nigra, M.; Robiglio, M. Smart Solutions for Sustainable Cities—The Re-Coding Experience for Harnessing the Potential of Urban Rooftops. Appl. Sci. 2020, 10, 7112. https://doi.org/10.3390/app10207112
Todeschi V, Mutani G, Baima L, Nigra M, Robiglio M. Smart Solutions for Sustainable Cities—The Re-Coding Experience for Harnessing the Potential of Urban Rooftops. Applied Sciences. 2020; 10(20):7112. https://doi.org/10.3390/app10207112
Chicago/Turabian StyleTodeschi, Valeria, Guglielmina Mutani, Lucia Baima, Marianna Nigra, and Matteo Robiglio. 2020. "Smart Solutions for Sustainable Cities—The Re-Coding Experience for Harnessing the Potential of Urban Rooftops" Applied Sciences 10, no. 20: 7112. https://doi.org/10.3390/app10207112
APA StyleTodeschi, V., Mutani, G., Baima, L., Nigra, M., & Robiglio, M. (2020). Smart Solutions for Sustainable Cities—The Re-Coding Experience for Harnessing the Potential of Urban Rooftops. Applied Sciences, 10(20), 7112. https://doi.org/10.3390/app10207112