Next Article in Journal
Performance Evaluation of Various Filter Media for Multi-Functional Purposes to Urban Constructed Wetlands
Next Article in Special Issue
Characteristics of PM2.5 Chemical Species in 23 Chinese Cities Identified Using a Vehicular Platform
Previous Article in Journal
Life Cycle Assessment of the Sugarcane Supply Chain in the Brazilian Midwest Region
Previous Article in Special Issue
Emission Characteristics of Particle Number from Conventional Gasoline and Hybrid Vehicles
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 16
Issue 1
10.3390/su16010286
sustainability-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Modeling the Effect of Green Roofs for Building Energy Savings and Air Pollution Reduction in Shanghai

by
Yuanfan Zheng
* and
Liang Chen
School of Environmental and Geographical Sciences, Shanghai Normal University, Shanghai 200234, China
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(1), 286; https://doi.org/10.3390/su16010286
Submission received: 31 October 2023 / Revised: 21 December 2023 / Accepted: 23 December 2023 / Published: 28 December 2023
(This article belongs to the Special Issue Aerosols and Air Pollution)
Browse Figures
Versions Notes

Abstract

:
Building energy consumption is an essential source of greenhouse gas (GHG) and air pollution. Green roofs can directly absorb ambient CO2 and remove air pollutants through their vegetation layers, but a limited number of studies have examined their effects on GHG and air pollutant reduction associated with building energy savings, especially in the context of climate change. This research examined the performance of green roofs on CO2 and air pollutant reduction, including SO2, PM2.5, and NOx, through building energy demand savings in Shanghai, China. Climate change mitigation effects were assessed based on the energy consumption of five types of buildings before and after the installation of green roofs under 2020 and 2050 climate conditions, respectively. EnergyPlus software 9.5.0 was applied to simulate hourly energy consumption for different building prototypes with and without green roofs. Green roofs on all building types exhibited positive energy savings on annual, monthly, and diurnal scales, and they can save more energy for most of the building types under the projected 2050 climate condition. Moreover, most of the building energy saved by green roofs came from the Heating, Ventilation, and Cooling (HVAC) systems. In addition, this study discovered that the energy-saving benefits of green roofs vary based on the type of building they were installed on. Green roofs were found to have the largest energy saving on the shopping mall, especially on extremely hot summer days. Finally, a Geographic Information System (GIS)-based approach was developed with the ability to quantify the amount of GHG and air pollutant reduction associated with building energy savings for existing buildings in the Huangpu District of Shanghai. This approach was also utilized to present the spatial distribution of buildings with different levels of suitability to install green roofs by considering their location attributes and air pollutant reduction potential together, which is the major innovation of this research. The purpose of this study is to provide valuable guidance to policy makers regarding the performance of green roofs in building energy-saving and air quality improvement in the urban environment when facing the challenge of climate change, which is essential for urban sustainability.

1. Introduction

Increased carbon emissions are a major driving force of global warming, and China has remained the world’s largest source of carbon emissions since 2008 [1,2,3]. In order to mitigate this problem, the Chinese government pledged to achieve reach a carbon peak by 2030 and carbon neutrality by 2060 [1]. Building energy consumption is a significant contributor to GHG emissions and air pollution, accounting for 32% of China’s energy-related CO2 emissions in 2020 [4,5]. Measuring the carbon impact of the buildings can be achieved through the widely applied GHG protocol model [6], which contains three scopes: direct emission during the building construction and demolish stage (scope 1), indirect emission during the building operation stage from energy consumption (scope 2), and energy consumed to transport the construction materials (scope 3). In 2019, 42.6% of building-related GHG emissions was contributed by building energy consumed during operations [7]. In 2020, building energy consumption also contributed to 30% of China’s anthropogenic PM2.5 emissions [8,9]. It is worth noting that compared to the construction and demolish stage, GHG and air pollutants emitted during the building operation stage are more difficult to control, as they will face many challenges, such as climate change, urbanization, and transformation of energy structure. Climate change can affect building energy consumption through changes in space heating and cooling energy demand, which can increase greenhouse gas emissions because energy used for building heating and cooling, such as electricity, is still mainly generated from fossil fuels like coal, oil, and natural gas [10]. Air pollutants can also be impacted by climate change through physical changes affecting meteorological conditions, chemical changes affecting their lifetimes, and biological changes affecting their natural emissions [11,12].
In order to reduce the emissions from greenhouse gas and air pollutants, governments and developers should find a more sustainable way to control building energy consumption, especially when facing the challenge of climate change [13,14,15]. One solution is to increase the green infrastructure [16]. However, ground surface area limitations exist for ground-level tree planting in high-density urban areas. A green roof, which is a roof covered with vegetation and growing medium, offers an alternative way for urban greening [15,17]. It has various benefits including the absorption of air pollutants [18,19], the sequestration of carbon dioxide [20], biodiversity [21], and the enhancement of urban sustainability [22]. Green roofs comprise multiple layers, including vegetation, substrate, growing medium (soil), filter, drainage, membrane, and insulation [23,24] (Figure 1). They can be categorized as extensive and intensive green roofs depending on the depth of growing media and the vegetation types [15]. Extensive green roofs are planted with a limited variety of grasses and have shallow substrate layers (ranging from 5 to 15 cm). On the other hand, intensive green roofs typically have small trees or shrubs for their vegetation layer, and thicker substrate layers [25]. Due to the advantages of lower maintenance requirements, lower installation costs, and lighter weight with minimal structural support requirements, extensive green roofs are more widely used than intensive green roofs.
Previous studies have explored the efficacy of green roofs in reducing greenhouse gas emissions [26,27,28] and air pollutants [29,30,31] associated with buildings. Vegetation on green roofs can absorb ambient CO2 directly from the atmosphere through photosynthesis, stored in plants and substrates as above and below-ground biomass and substrate organic matter [26]. Heusinger and Weber applied the eddy-covariance (EC) method to investigate the GHG fluxes between the atmosphere and green roofs over a full annual cycle in Berlin, Germany. It was demonstrated that the green roof was a carbon sink on an annual basis with an uptake rate of 85 g cm−2year−1 [27]. Moreover, green roofs can reduce air pollutants and improve air quality by dry deposition and uptake through leaf stomata [19,29,30]. Yang et al. used a dry deposition model to quantify the ability of green roofs to remove air pollution in Chicago, IL, USA. They found that in one year, 19.8 ha of green roofs can remove a total of 1675 kg of air pollutants, with O3 accounting for 52%, NO2 for 27%, PM10 for 14%, and SO2 for 7% of the total [19]. Kostadinovic et al. measured the ambient concentration of PM1, PM2.5, and PM10 on the green roof and the reference roof separately on a school building in New Belgrade, Serbia. They found that during January 2020, the concentrations of PM1, PM2.5, and PM10 on green roofs were 7%, 16.6%, and 17.6% lower than that of the reference roof [30]. Irga et al. conducted field experiments to calculate the removal of ambient air pollutants by an extensive green roof compared to a conventional roof in Sydney, Australia. Their results suggested that green roofs can theoretically remove 0.5 kg of PM2.5, 6.9 kg of O3, and 2.3 kg of NO2 per year, which were significantly higher than that of the conventional roof [29].
Many other studies suggested that green roofs can lower building energy consumption during operation by the combined effect of shading, evapotranspiration, and thermal insulation [16,32,33,34]. Jim applied precision energy loggers to monitor the air-conditioning electricity consumption of six vacant apartments with different insulation and green roof experimental plots under various weather scenarios. They confirmed both species on the green roofs can reduce energy consumption compared to the regular roofs [33]. In addition to the monitor-based approach, several methods have been developed to quantify the effect of green roofs by using a model that accounts for the energy balance of vegetated rooftops [15,35,36,37,38,39,40]. An eco-roof is one of the most widely used and cited models and has been adopted by Energyplus software 9.5.0. Zeng et al. conducted a simulation to find the optimal parameter settings for green roofs in different climate zones in China. They concluded that green roofs perform similarly in cooling-dominated areas, but optimized settings are recommended for heating-dominated cities to save more heating energy [39]. Zhou et al. designed an innovative green roof model by integrating a leaf area index (LAI) that varies seasonally, to compare its performance on building energy saving to models with constant LAI values through simulation in Shanghai, China. Their results suggested that compared to the models with variable LAI, the models with constant LAI underestimated the latent heat flux and overestimated the sensible heat flux during summer in Shanghai [40]. Abuseif et al. simulated the effects of 112 green roof settings on the energy reduction in residential townhouses across different climate zones in Australia. Their results suggested that the energy-saving abilities of green roofs not only varied across regions but also different in seasons. The highest drop in energy demand was achieved in the cool temperature climate, whereas the lowest drop (10.1%) was recorded in the high humidity in the summer and warm winter climate [16].
Many existing studies quantified the amount of GHG [26,27,28] and air pollutants [29,30,31] directly absorbed or removed by the vegetation layers of green roofs. Still, few studies considered the GHG and air pollutants reduced indirectly from building energy saved by green roofs, especially in the context of climate change. Moreover, the energy-saving effects of green roofs on different types of buildings, which are affected by occupancy behaviors and operation schedules, have not been thoroughly examined in previous research. In addition, the majority of relevant existing studies were conducted at the individual building scale, but studies assessing the benefits of green roofs on reducing GHG and air pollutants at the city scale or the sub-city scale are limited. A dataset presenting the location of buildings with great benefits from energy savings and air pollution reduction after the installation of green roofs would be vitally important for the city government to identify them spatially. This study evaluated the effects of energy demand savings of green roofs on five building types, and the associated amount of GHG and air pollutants reduced in Shanghai, China. The effects of green roofs on GHG and air pollutant reduction caused by building operations were assessed under the 2020 and 2050 climate conditions. Finally, based on the evaluation results, a Geographic Information System (GIS) approach was developed with the ability to quantify the amount of GHG and air pollutant reduction associated with building energy savings for existing buildings in a district of Shanghai. This approach can further be utilized to present the spatial distribution of buildings with different levels of suitability to install green roofs by considering their location attributes and air pollutant reduction potential together, which is the major innovation of this research. The specific research questions are: (1) can green roofs provide the same or even better effects in terms of building energy saving under warmer climate conditions in the future? (2) What types of buildings can benefit most from energy savings? (3) How much GHG and air pollutants can be reduced by green roofs through building energy savings in different scenarios? The purpose of this study is to provide valuable guidance to policy makers regarding the performance of green roofs in building energy-saving and air quality improvement when facing the challenge of climate change.
The rest of this paper was organized as follows: Section 2 presents the proposed approach including building prototype construction, green roof settings, and the model used to simulate building energy consumption. Section 3 presents the results, which include the effect of green roofs on savings of building operational energy at different temporal scales and the associated benefits of GHG and air pollutant reduction. Section 4 discusses the strengths of the approach and the contributions of this research. The summary of key findings was provided in Section 5.

2. Methodology

2.1. Study Area and Data Acquisition

The study area, Shanghai, is an international metropolis located in East China at 31° N and 121° E. It is one of the most populous cities in the world with a population of 24.89 million in 2021 according to the Chinese National Bureau of Statistics [41]. Shanghai belongs to the “hot summer and cold winter” (HSCW) climate zone, which is characterized by high temperatures and minimal temperature fluctuations during summer, and low temperature and high humidity during winter. Due to these conditions, buildings in this area consume high amounts of energy since they require cooling during summer and heating during winter. This highlights the crucial role of building energy efficiency and carbon emission reduction in this region [42,43]. Huangpu District, which locates in the core area of Shanghai, was chosen to conduct a case study to evaluate the GHG and air pollutant reduction at the district level. It has the second highest population density among all districts with an area of 20.46 sqkm2 and 582,100 inhabitants [44].
This study utilized two main datasets for building energy simulation: building prototypes and the Chinese Standard Weather Data (CSWD) of Shanghai (https://energyplus.net/weather-location/asia_wmo_region_2/CHN/CHN_Shanghai.Shanghai.583620_CSWD, accessed on 15 August 2023). The CSWD contains hourly weather data for Shanghai throughout a one-year period, compiled from multiple years and belonging to the typical meteorological year (TMY3) weather dataset. The TMY3 dataset was developed by National Solar Radiation Data Base (NSRDB). It provides comprehensive data on the seasonal and diurnal variations that represent the climate characteristics of a specific location [15]. The building prototypes for five building types were established using DesignBuilder version 7.0.
The building footprint GIS data in Huangpu District were obtained from the Gaode Map, including the information of location, outline, height, and floor number of each existing building. The building type information was obtained from the Essential Urban Land Use Categories (EULUC) China data (http://data.ess.tsinghua.edu.cn, accessed on 20 August 2023), which was developed by Peng Cheng Laboratory. The population data of Shanghai from the year of 2021 in raster format with a spatial resolution of 1 km, was obtained from the Geographical Information Monitoring Cloud Platform (www.dsac.cn, accessed on 15 September 2023).
EnergyPlus was used for building energy consumption simulation, which includes space cooling and heating, lighting, ventilation, and process loads. It is a popular building energy simulation software designed and developed by the U.S. Department of Energy (DOE), which is widely used by engineers, architects, scientists, and researchers [45]. It obtains building energy simulation results at the hourly scale, which takes building characteristics associated with specific building prototypes and hourly weather data as the inputs. In this study, EnergyPlus was used to perform the simulation using five building prototypes and the CSWD dataset of Shanghai.

2.2. Building Prototype

Four types of public buildings, including a hospital, a shopping mall, an office, and hotel were chosen for energy demand simulation. An apartment building was selected to represent residential building, as it accounted for 93.3% of residential buildings in Shanghai according to Shanghai Statistical Yearbook 2022 [44]. Table 1 presents the prototypical building models for energy consumption simulation, and the number of floors they have. Thermal parameters, setting the temperature for air conditions in different seasons, operational schedules, and hourly occupancy rates were set according to the Chinese design standard of the public (GB50189-2015) [42,46] and residential buildings (JGJ134-2010) [47,48] of HSCW climate zone in EnergyPlus software. Weekends and holidays were also considered by Chinese standards. Key parameters were summarized in Table 2, Table 3 and Table 4.

2.3. Green Roof Settings

In this study, the EnergyPlus software was used to evaluate the effect of green roofs on potential building energy savings in Shanghai. GHG and air pollution reduction associated to building energy savings was further analyzed. Hourly building energy consumption by incorporating the green roof module with appropriate settings was simulated. This green roof module was developed by Sailor [49] at Portland State University in Oregon, based on validated data from monitored buildings with green roofs installed. As an integral part of the simulation software, it calculates the energy balance of a vegetated rooftop within each time step [15,50]. Previous research has widely utilized this module, but studies that evaluate the reduction in greenhouse gas and air pollutants associated with building energy savings are rare.
Users can design different types of green roofs by setting various parameters, such as plant (height of plants, leaf reflectivity, LAI, leaf emissivity) and soil parameters (thickness, conductivity, density). According to the previous study [27], compared to intensive green roofs, extensive green roofs are less expensive, easier to install, require less maintenance, and are more widely used. Therefore, we chose extensive green roofs with vegetation species belonging to the genus Sedum, which has strong adaptability to severe climate conditions and was commonly used for green roofs in many cities including Shanghai [51].
LAI and soil depth are the key parameters that determine the performance of green roofs regarding energy saving [15,38]. A seasonal variable LAI was designed following the settings in Zhou et al. [40], which agrees with the observed average LAI values for Sedum in experimental studies conducted in Shanghai [38,51]: 3.5 during spring months; 5 during summer months, 3 during fall months, and 0.5 during winter months. Soil depth was set to 0.1 m in the green roof module, as green roofs in experimental studies conducted in Shanghai have soil thickness range from 0.04 to 0.12 m [38,51]. This study followed the settings in Zhou et al. [40] for the rest of the parameters: plant height = 0.2 m; specific heat of dry soil = 1200 J/(kg-K); soil depth = 0.1 m, conductivity of dry soil = 0.35 W/(m-K); density of dry soil = 1100 kg/m3, which was also designed under the climate condition in Shanghai.

2.4. The Simulation Model

According to Yang and Hong [8], the majority of carbon dioxide and air pollutants (PM2.5, SO2, and NOx) emitted by building operations in Shanghai were contributed by electricity consumption in 2020. Moreover, a questionnaire survey by Nie 2017 [52] suggested that electricity is the major energy used for space cooling and heating in buildings in Shanghai. Therefore, this study only analyzed the effect of green roofs on buildings using electricity for space cooling and heating during operation.
In order to test the mitigation effect of green roofs in the context of climate change, this study designed an experiment that simulated building energy demand for four groups of building prototypes in EnergyPlus: two groups with green roofs under the current (2020) and 2050 climate conditions, and the other two with no green roofs (regular roofs) under same climate conditions as the control group. Each group contains five building prototypes, which included the apartment building and four types of public buildings.
The weather data utilized in EnergyPlus was retrieved from the CSDW dataset of Shanghai, which contains outdoor dry bulb temperature, outdoor wet bulb temperature, direct radiation intensity, scattered radiation intensity, outdoor relative air humidity, speed of wind, etc., to support energy demand simulations at the hourly scale for an entire year [53]. Since the CSDW dataset was constructed based on meteorological data that observed at least one decade earlier [54,55], the hourly 2020 and 2050 weather data files were generated by using a widely used method in the current literature [10,56,57,58,59,60], which applied HadCM3 model with medium carbon emission scenario to the original (CSDW) hourly weather data in Shanghai.
After the simulation was finished for four groups of building prototypes, two metrics were developed to assess the energy demand difference between the same type of buildings with green roofs and without green roofs: absolute difference (AD) and relative change (RC) in annual, monthly, and diurnal scales. The RC can be calculated as follows:
R C = 100 % × ( E r − E g ) E r
where Eg is energy demand during operation for buildings with green roofs; Er is energy demand during operation for buildings without green roofs. The AD shows the difference in energy demand intensity, which can be calculated by the following formula:
A D = ( E r − E g )
The effect of green roofs on greenhouse gases and air pollution reduction associated with savings of building energy demand during operation can then be calculated using the method provided by the Intergovernmental Panel on Climate Change (IPCC) emission inventory, which applies the emission coefficients and can be expressed as follows:
E i , j = A D × E F i × F A j
where Ei,j represents the reduced emission of air pollutant type i from energy consumption of building j during operation; EFi is the emission factor of air pollutant i from the electricity consumption, which took the indexes used in existing literature [8,42,61] as reference and summarized in Table 5; FAj is the total floor area of building j, which is the multiply of footprint area and floor number of building j.

2.5. Assessment of Carbon Emission and Air Pollution Reduction on Existing Buildings Using the GIS Modeling Technique

As a location-based platform, GIS provides a chance to present the spatial distribution of carbon emission and air pollutant reduction potential on the existing buildings after installation of green roofs. The “Shanghai Greening Regulations”, which became effective since 1 October 2015, stated that newly constructed and reconstructed public buildings with flat roofs under 50 m in height should have green infrastructures on their roofs. According to the regulation mentioned above, four types of public buildings (office, hotel, shopping mall, and hospital) which lower than 50 m of height in Huangpu District were chosen. Although the slope of building roof cannot be obtained due to data shortage, the majority of public buildings were found to have flat roofs on the 2022 high-resolution orthophoto imagery of Huangpu District. Therefore, all chosen buildings were assumed to have flat roofs and suitable for green roof installation.
Given floor area and type for individual buildings can be obtained from data layers of the building footprint and land use, the amount of carbon emission and air pollution reduction for each chosen building can be calculated by Equation (3), and the location of buildings with a large amount of reduction can be present in the map. Since green roofs can absorb ambient CO2 and air pollutants directly from the atmosphere, buildings located in urban areas with a higher population density or closer to stationary sources of air pollutants (industrial areas) are also considered as suitable candidates for green roof installation [62]. This research adopted a GIS-based approach to rank the priority of green roof installation for chosen buildings by considering their locations and potential energy-saving benefits together, and few studies have done this before. In the next step, a 200 m buffer zone was created for each industrial land use parcel in Huangpu District, which can be overlaid with other data layers such as the building footprint and the 1 km population density grid, as Figure 2 presented. Buildings can be classified into different groups regarding their priority to install green roofs by considering their location attributes (population density and distance to industrial area) and air pollution reduction associated with energy-saving benefits.

3. Results

3.1. Effect of Green Roofs on Savings of Building Operational Energy at the Annual Scale

Table 6 shows the AD and RC in the average annual building operational energy demand between buildings with green and regular roofs under both 2020 and 2050 climate conditions. A larger AD or RC value suggests more energy saving after the installation of green roofs. According to Table 6, all building types with green roofs showed an obvious decrease in energy consumption compared with regular roofs under both climate conditions. Large variations in energy demand savings across different types of buildings were observed. For example, the ADs ranged from 5.97 MJ/m2 (office) to 118.3 MJ/m2 (shopping mall), and the RCs ranged from 1.73% (office) to 21.6% (shopping mall) under 2020 climate conditions. The shopping mall presented higher AD and RC than the other types of buildings under both 2020 and 2050 climate conditions, indicating that they benefited the most from energy savings and can be considered as the most suitable candidate for the installation of green roofs. A possible reason is that the shopping mall had longer operation hours and a higher occupancy density; and in order to maintain the comfort level of indoor environment, its Heating, Ventilation, and Cooling (HVAC) system works all the time during operation hours. Moreover, all building types exhibited larger AD and RC in 2050 compared to that of 2020 except the hotel, which indicates the implementation of green roofs can save more energy under the warmer climate conditions in the future for most of the building types. The reason the hotel did not show more total energy savings under the 2050 climate condition is green roof saved much less space heating energy for it, which cannot be offset by the increase in cooling energy savings.
Figure 3 demonstrates the proportion of individual components that contribute to the annual energy savings in Shanghai under 2020 and 2050 climate conditions from green roofs. Savings from space cooling and heating constitute the majority of the total energy savings, because green roof can remove heat from the air through the process of evapotranspiration and provide insulation for buildings to reduce the energy needed for cooling and heating. For the shopping mall and apartment buildings, most energy savings came from cooling energy savings. For the office, hotel, and hospital buildings, heating energy savings contributed the most. However, the percentage of contribution to total energy savings from cooling energy was expected to increase in 2050 for all types of buildings, while savings from heating energy were expected to decrease. The reason is global warming should increase the cooling energy intensity and decrease the heating energy intensity for all types of buildings. It is worth noting that a significant portion of the total energy savings resulted from reduced fan energy consumption. This suggests that the ventilation system used less energy to circulate the indoor environment when the temperature was closer to the desired level after the installation of green roofs. Therefore, it can be concluded that in Shanghai, the majority of building energy saved by green roofs came from the HVAC systems. Savings from other (non-HVAC) types of energy (refrigeration, humidification, and pumps) were trials.

3.2. Effect of Green Roofs on Savings of Building Operational Energy at the Monthly Scale

The performance of green roofs on building operational energy savings was further examined at the monthly scale. Figure 4 shows the monthly energy savings from green roofs on five types of buildings under 2020 and 2050 climate conditions compared to regular roofs. All buildings presented positive total energy savings after green roof installation during all months in 2020 and 2050. Moreover, total energy savings exhibited a strong seasonality for all building types except for the hospital building, which was caused by the monthly variations in savings on space cooling and heating energy. On the other hand, savings from other energy sources remain relatively constant in different months. Under both climate conditions, the shopping mall and apartment buildings presented more monthly energy savings during summer, and the other three types of buildings showed higher monthly energy savings during winter. The hospital building showed a smaller monthly variation in cooling energy savings after the green roof installation, which results in its total energy savings seems relatively constant throughout the year. The possible reason is that the hospital building has rigorous ventilation standards to maintain, as the operation of building fans can increase the circulation of indoor air and improve the efficiency of the air condition in summer, which could diminish the energy-saving advantages from green roofs. Figure 5 demonstrates the comparison of total energy savings on five types of buildings by green roofs under 2020 and 2050 climate conditions in Shanghai at the monthly scale. It was observed that green roofs can lead to more total energy savings in the summer months in 2050 compared to 2020 for five different types of buildings because green roofs can help to maintain the indoor temperature and save more cooling energy when the temperature is higher through thermal insulation and evapotranspiration, given changes in other types of energy during summer is not significant.

3.3. Effect of Green Roofs on Savings of Building Operational Energy at the Hourly Scale

Since global warming will increase the cooling energy intensity and decrease the heating energy intensity for buildings in the future. Savings on cooling energy during summer months should be vitally important to reduce the emission of GHG and air pollutants. This section presents an analysis of the hourly cooling energy-saving performance of green roofs during an extremely hot summer day in Shanghai under the 2050 climate condition. The shopping mall and apartment buildings were selected, as most of their energy saved by green roofs came from cooling energy. This study chose 2 August, which is a workday, for the shopping mall as the extremely hot summer day, as its daily high temperature reached 40.8 °C at 3 p.m. in the constructed 2050 hourly weather file. Since the apartment belongs to residential buildings, which usually presents higher energy consumption on weekend than on workday, a weekend with a daily high temperature reached 36.7 °C at 1 p.m. was selected (6 August). According to Table 7, both building types exhibited lower peak-to-valley ratio of cooling energy demand after installation of green roofs. Moreover, approximately 40.1% and 10.3% of daily cooling energy can be saved by green roofs for the shopping mall and apartment during, respectively. This result suggested that the green roof can effectively cut peak cooling energy demand at extremely hot summer days. As Figure 6 suggested, green roofs can save more total energy for two types of buildings during the daytime when the temperature was higher.

3.4. Case Study: Evaluation of Carbon Emission and Air Pollution Reduction after Green Roof Installation in Huangpu District

In this section, a case study was conducted to evaluate the annual CO2 and air pollutant emission reduction associated with building energy demand savings after green roof installation in Huangpu District of Shanghai using the GIS modeling technique.
A sensitivity analysis was conducted with the purpose to investigate the variability of GHG reduction associated with building energy savings after green roof installation due to the varying emission factors of GHG from electricity consumption used in different literature [4,41,60]. Three scenarios were developed, and emission factors of 0.588 kg/kwh, 0.726 kg/kwh, and 0.788 kg/kwh (Table 5) were used in Scenario 1, Scenario 2, and Scenario 3, respectively. As Table 8 presents, the annual CO2 reduction varied in the range of 47.68–64.34 Kt. As Table 9 presents, if green roofs were installed on all four types of public buildings lower than 50 m in Huangpu District, the emission of PM2.5, SO2, and NOx can reduce by 26.61 t, 82.06 t, and 130.97 t, respectively. According to Table 8 and Table 9, most benefits came from installation of green roofs on shopping malls. Figure 7 presents the spatial distribution of the annual emission reduction in CO2, PM2.5, SO2, and NOx for individual buildings within Huangpu District, Shanghai. Buildings with high benefits (annual CO2 reduction larger than 800 t and annual air pollution reduction larger than 400 kg) for green roof installation were mostly located in the northern part, where large shopping malls are presented.
The entire Huangpu District has a population density higher than 8000 people/km2 (Figure 2), which is higher than the average population density (approximately 4000 people/km2), suggesting that buildings in Huangpu District should have higher benefits to install green roofs. As Figure 2 indicates, there are several industrial areas located in Huangpu District, and buildings located near them should also considered to have a high priority in installing green roofs. This study designed a set of rules to classify buildings into three groups regarding their suitability for green roof installation by considering their location attributes (population density and distance to industrial area) and air pollution reduction associated with energy-saving benefits, as Table 10 presents. Buildings meet all three rules of population density (>30,000 people/km2), distance to industrial area (within the 200 m buffer zone), and air pollution reduction potential (800 t of CO2 and 400 kg of PM2.5, SO2, and NOx) associated with energy savings were classified as high suitability to install green roofs. The rest of the buildings that meet either the two rules of population density (>15,000 people/km2) and air pollution reduction potential (400 t of CO2 and 200 kg of PM2.5, SO2, and NOx) associated with energy savings, or the rule of distance to industrial area (within the 200 m buffer zone) were classified as moderate suitability for green roof installation. Some buildings in this group cannot obtain many benefits from energy savings, but their green roofs can still reduce air pollution directly by dry deposition, as they are located near the stational sources of air pollutants (industrial area). Figure 8 presents the spatial distribution of buildings with high, moderate, and low priority to install green roofs. This result can provide valuable information to the city government to develop the following roof greening policy.

4. Discussion

The contributions and innovations of this study are presented in this section. The previous studies [63,64] suggested that vegetation and soil layers in green roofs can provide thermal insulation and the evapotranspiration and transpiration of vegetation can further reduce heat transmission to buildings. Therefore, the energy demands of buildings with green roofs are less sensitive to the outside environment and can consume less space cooling and heating energy [63,64]. Despite the climate conditions, the potential energy savings using green roofs varies by season [49]. Moreover, green roofs can save more energy for buildings during the daytime when the temperature was higher and solar radiation is stronger [23]. The results of this study agreed with the above findings but further discovered that green roofs presented a better energy-saving performance on four building types under warmer climate conditions in the future (2050), which have not been discussed much in previous studies.
Unlike the previous studies that directly quantified the amount of GHG, and air pollutants absorbed or removed by the vegetation layers of green roofs, this study further evaluated the effect of green roofs from a different aspect, which is the indirect reduction emission of GHG and air pollutant associated with building energy savings. Moreover, the innovation of this study is that it provided an effective way to apply the evaluation on the existing buildings by combining the building energy simulation and air pollutant emission quantification with the GIS spatial modeling technique. Although the spatial distribution of GHG and air pollutant emission reduction for individual building were present based on results calculated at the annual temporal scale, the assessment can also be performed at a finer temporal scale (monthly and hourly) as needed. In addition, the other innovation of this study is that it presented a method to rate the suitability of existing buildings to install green roofs by considering their location attributes (distance to the industrial area and population density) and air pollutant reduction potential together, which was seldom performed by former studies. This method can be further developed if more datasets, such as air pollutant monitoring data, are available. The dataset of the solar potential of individual roofs will also be helpful, as the integration of green roofs with solar panels can be beneficial for each other [65,66]. The city government can identify the most suitable buildings for green roof installation with the information on the actual amount of air pollution reduction through both direct (absorption) and indirect (energy savings through green roofs and solar panels) ways. For example, shopping malls with great solar potential and located close to pollutant sources such as main roads or factories might be suitable according to the results in this study.
Compared with the widely used GHG protocol model and Carbon Disclosure Project (CDP) model, which update the carbon emission dataset from specific sectors annually, as the participants only submit the relevant data once per year [6], the approach proposed in this study can support carbon emission assessment at a finer temporal scale (seasonal, monthly, and diurnal). Compared with many existing sustainability risk reduction models [6,67], performed at the industrial, sectoral, and corporate levels, the approach proposed in this study is more suitable to be adopted at finer spatial scales such as the building, neighborhood, or district levels, which is a good complement to the current GHG reduction and sustainability risk reduction literature.

5. Conclusions

The performance of green roofs on building energy demand savings, GHG emission reduction, and air pollutant mitigation from five types of buildings under 2020 and 2050 climate conditions in Shanghai was evaluated. All building types with green roofs showed an apparent decrease in energy demand compared with regular roofs under both climate conditions. Most of the energy savings from green roofs came from the HVAC systems, especially from space cooling and heating energy. Moreover, the majority of building types exhibited a larger AD and RC in 2050 compared to that of 2020, which indicated that the installation of green roofs is an effective way to cope with climate change. In addition, a large variation in energy consumption savings across different types of buildings was found, caused by differences in building configuration, internal loads, occupancy behaviors, and operation schedule, suggesting the city to consider green roof installation a priority in the future. Green roofs were found to have the largest energy saving in the shopping mall, especially on extremely hot summer days, saving up to 40.1% of daily cooling energy for it. Therefore, implementing green roofs in shopping malls can be considered a promising strategy for achieving energy savings. These results not only indicate that a green roof is a good solution to alleviate the energy supply pressure under extreme weather conditions, but is also better capable of reducing GHG and air pollutants associated with building energy consumption. The findings of this study underscore the role of green roofs in mitigating air pollution and improving the environmental sustainability of urban areas. Finally, this study has good applicability because all datasets used were free and publicly available, so it can easily be applied by other researchers to different study areas.
However, the uncertainties on the effect of green roofs on GHG and air pollution reduction evaluation were due to data shortage and limitations of the green roof module. Although the green roof module is one of the most cited models in the current literature [49,68], it still did not allow the user to set more relevant parameters such as plant species and the coverage rate. Moreover, this study only quantified GHG and air pollution reduction in buildings that used electricity for both space cooling and heating, even though most space heating energy in Shanghai is currently supplied by electricity [50]. In addition, the number of floors may affect the energy saving ability of green roofs for the entire building, but space cooling and heating energy currently used in each zone in the building model are not provided in the output files of EnergyPlus, making it difficult to assess this effect. A future study can further improve the results if more datasets become available to the public and more advanced modules of green roofs are provided in software. For example, the high-resolution orthophoto and Airborne Light Detection and Ranging (LiDAR) data can allow the researchers to assess the amount of space that can be used for green roof and solar panel installation at the individual building scale. The energy supply data (electricity, natural gas, coal) at finer spatial scales (district, neighborhood, and building) can allow the researchers to evaluate GHG and air pollution reduction in a particular area with higher accuracy, given that the emission factors for different sources of energy and pollutants are both available.

Author Contributions

Conceptualization, Y.Z.; methodology, Y.Z.; software, Y.Z. and L.C.; project administration, Y.Z.; validation, Y.Z.; formal analysis, Y.Z. and L.C.; investigation, Y.Z.; resources, Y.Z.; data curation, Y.Z. and L.C.; writing—original draft preparation, Y.Z.; writing—review and editing, Y.Z.; visualization, Y.Z. and L.C. All authors have read and agreed to the published version of the manuscript.

Funding

This study was Sponsored by Shanghai Pujiang Program (No. 21PJ1411600) and the National Natural Science Foundation of China (No. 42101314).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author on reasonable request.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Chen, C.; Bi, L. Study on spatio-temporal changes and driving factors of carbon emissions at the building operation stage—A case study of China. Build. Environ. 2022, 219, 109147. [Google Scholar] [CrossRef]
  2. Qi, S.J.; Zhang, Y.B. Research on the influencing factors and reduction strategies of carbon emission of construction industry in China. Soft Sci. 2013, 27, 39–43. [Google Scholar] [CrossRef]
  3. Wu, P.; Song, Y.; Zhu, J.; Chang, R. Analyzing the influence factors of the carbon emissions from China’s building and construction industry from 2000 to 2015. J. Clean. Prod. 2019, 221, 552–566. [Google Scholar] [CrossRef]
  4. Building Energy Research Center of Tsinghua University (BERC). 2022 Annual Report on China Building Energy Efficiency; China Building Industry Press: Beijing, China, 2022; pp. 5–6. [Google Scholar]
  5. UNEP, International Energy Agency (IEA). 2020 Global Status Report for Buildings and Construction towards a Zero-Emissions, Efficient and Resilient Buildings and Construction Sector. 2020. Available online: https://globalabc.org/resources/publications/2020-global-status-report-buildings-and-construction (accessed on 1 October 2023).
  6. Bognar, F.; Bocskei, E. Potential Model to Support the Achievement of Corporate Carbon Neutrality. Public Financ. Q. 2022, 67, 379–395. [Google Scholar] [CrossRef]
  7. Professional Committee of Building Energy and Emissions (CABEE): 2021 Chinese Buildings Energy and Carbon Emissions Research Report. 2021. Available online: https://www.waitang.com/report/331222.html (accessed on 11 September 2023). (In Chinese).
  8. Yang, L.; Hong, S. Impact of the clean energy structure of building operation on the co-benefits of CO2 and air pollutant emission reductions in Chinese provinces. J. Clean. Prod. 2023, 413, 137400. [Google Scholar] [CrossRef]
  9. Xi, Z.; Xiao, Y.; Meng, W.J.; Xu, H.R.; Wei, D.; Shen, G.F.; Chen, H.F.; Ma, J.M.; Shu, T. Stacked Use and Transition Trends of Rural Household Energy in Mainland China. Environ. Sci. Technol. 2019, 53, 521–529. [Google Scholar] [CrossRef]
  10. Zheng, Y.; Weng, Q. Modeling the effect of climate change on building energy demand in Los Angeles County by using a GIS-based high spatial- and temporal-resolution approach. Energy 2019, 176, 641–655. [Google Scholar] [CrossRef]
  11. Intergovernmental Panel on Climate Change (IPCC). Climate Change 2021: The Physical Science Basis. In Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2023; pp. 23–91. [Google Scholar] [CrossRef]
  12. Kirtman, B.; Power, S.B.; Adedoyin, A.J.; Boer, G.J.; Bojariu, R.; Camilloni, I.; Doblas-Reyes, F.; Fiore, A.M.; Kimoto, M.; Meehl, G.; et al. 2013: Near-Term Climate Change: Projections and Predictability. In Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change; Cambridge University Press: Cambridge, UK; New York, NY, USA; pp. 953–1028. [CrossRef]
  13. Dimond, K.; Webb, A. Sustainable roof selection: Environmental and contextual factors to be considered in choosing a vegetated roof or rooftop solar photovoltaic system. Sustain. Cities Soc. 2017, 35, 241–249. [Google Scholar] [CrossRef]
  14. Tang, X.; Qu, M. Phase change and thermal performance analysis for green roofs in cold climates. Energy Build. 2016, 121, 165–175. [Google Scholar] [CrossRef]
  15. Zheng, Y.; Weng, Q. Modeling the Effect of Green Roof Systems and Photovoltaic Panels for Building Energy Savings to Mitigate Climate Change. Remote Sens. 2020, 12, 2402. [Google Scholar] [CrossRef]
  16. Abuseif, M.; Jamei, E.; Chau, H. Simulation-based study on the role of green roof settings on energy demand reduction in seven Australian climate zones. Energy Build. 2023, 286, 112938. [Google Scholar] [CrossRef]
  17. Lukač, N.; Žalik, B. GPU-based roofs’ solar potential estimation using LiDAR data. Comput. Geosci. 2013, 52, 34–41. [Google Scholar] [CrossRef]
  18. Curre, B.; Bass, B. Estimates of air pollution mitigation with green plants and green roofs using the UFORE model. Urban. Ecosyst. 2008, 11, 409–422. [Google Scholar] [CrossRef]
  19. Yang, J.; Yu, Q.; Gong, P. Quantifying air pollution removal by green roofs in Chicago. Atmos. Environ. 2008, 42, 7266–7273. [Google Scholar] [CrossRef]
  20. Karteris, M.; Theodoridou, I.; Mallinis, G.; Tsiros, E.; Karteris, A. Towards a green sustainable strategy for Mediterranean cities: Assessing the benefits of large-scale green roofs implementation in Thessaloniki, Northern Greece, using environmental modeling, GIS and very high spatial resolution remote sensing data. Renew. Sustain. Energy Rev. 2016, 58, 510–525. [Google Scholar] [CrossRef]
  21. Mayrand, F.; Clergeau, P. Green roofs and green walls for biodiversity conservation: A contribution to urban connectivity? Sustainability. 2018, 10, 985. [Google Scholar] [CrossRef]
  22. 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]
  23. Morakinyo, T.E.; Dahanayake, K.W.D.K.C.; Ng, E.; Chow, C.L. Temperature and cooling demand reduction by green-roof types in di_erent climates and urban densities: A co-simulation parametric study. Energy Build. 2017, 145, 226–237. [Google Scholar] [CrossRef]
  24. Ecogardens. Available online: https://Info.ecogardens.com/blog/what-are-the-layers-of-a-green-roof (accessed on 29 October 2023).
  25. Heusinger, J.; Sailor, D.J.; Weber, S. Modeling the reduction of urban excess heat by green roofs with respect to different irrigation scenarios. Build. Environ. 2018, 131, 174–183. [Google Scholar] [CrossRef]
  26. Agra, H.; Klein, T.; Vasl, A.; Kadas, G.; Blaustein, L. Measuring the effect of plant-community composition on carbon fixation on green roofs. Urban. For. Urban. Green. 2017, 24, 1–4. [Google Scholar] [CrossRef]
  27. Heusinger, J.; Weber, S. Extensive green roof CO2 exchange and its seasonal variation quantified by eddy covariance measurements. Sci. Total Environ. 2017, 607, 623–632. [Google Scholar] [CrossRef]
  28. Seyedabadi, M.R.; Karrabi, M.; Nabati, J. Investigating green roofs’ CO2 sequestration with cold- and drought-tolerant plants (a short- and long-term carbon footprint view). Environ. Sci. Pollut. Control Ser. 2021, 10, 14121–141230. [Google Scholar] [CrossRef]
  29. Irga, P.; Braun, J.; Douglas, A.; Pettit, T.; Fujiware, S.; Burchett, M.; Torpy, F. The distribution of green walls and green roofs throughout Australia: Do policy instruments influence the frequency of projects? Urban. For. Urban. Green. 2017, 24, 164–174. [Google Scholar] [CrossRef]
  30. Kostadinovic’, D.; Jovanovic’, M.; Bakic, V.; Stepanc, N. Mitigation of urban particulate pollution using lightweight green roof system. Energy Build. 2023, 293, 113203. [Google Scholar] [CrossRef]
  31. Motlagh, S.H.B.; Pons-Valladares, O.; Hosseini, S.M.A. City-scale model to assess rooftops performance on air pollution mitigation; validation for Tehran. Build. Environ. 2023, 244, 110746. [Google Scholar] [CrossRef]
  32. Kim, T.D.H.; Yu, B.; Srichandra, J.; Low-Choy, S.; Michael, R.N. Water flux and biomass production of native plants at different substrate compactions for landfill phytocaps in Southeast Queensland, Australia. Ecol. Eng. 2022, 183, 106745. [Google Scholar] [CrossRef]
  33. Jim, C.Y. Air-conditioning energy consumption due to green roofs with different building thermal insulation. Appl. Energy 2014, 128, 49–59. [Google Scholar] [CrossRef]
  34. Taleghani, M. Outdoor thermal comfort by different heat mitigation strategies a review. Renew. Sustain. Energy Rev. 2018, 81, 2011–2018. [Google Scholar] [CrossRef]
  35. Chen, J.; Yin, H.; Kong, F. Measuring the performance of green roof cooling effects from a multi-dimensional perspective. Acta Ecol. Sin. 2020, 40, 1445–1454. [Google Scholar] [CrossRef]
  36. He, Y.; Lin, E.S.; Tan, C.L.; Yu, Z.; Tan, P.Y.; Wong, N.H. Model development of Roof Thermal Transfer Value (RTTV) for green roof in tropical area: A case study in Singapore. Build. Environ. 2021, 203, 108101. [Google Scholar] [CrossRef]
  37. Tan, T.; Kong, F.; Yin, H.; Cook, L.M.; Middel, A.; Yang, S. Carbon dioxide reduction from green roofs: A comprehensive review of processes, factors, and quantitative methods. Renew. Sustain. Energy Rev. 2023, 182, 113412. [Google Scholar] [CrossRef]
  38. 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]
  39. Zeng, C.; Bai, X.; Sun, L.; Zhang, Y.; Yuan, Y. Optimal parameters of green roofs in representative cities of four climate zones in China: A simulation study. Energy Build. 2017, 150, 118–131. [Google Scholar] [CrossRef]
  40. Zhou, L.W.; Wang, Q.; Li, Y.; Liu, M.; Wang, R.Z. Green roof simulation with a seasonally variable leaf area index. Energy Build. 2018, 174, 156–167. [Google Scholar] [CrossRef]
  41. Chinese National Bureau of Statistics. Available online: www.stats.gov.cn/sj/ndsj/2022/indexch.htm (accessed on 10 October 2023).
  42. Liang, Y.; Pan, Y.; Yuan, X.; Yang, X.; Yang, Y.; Fu, L.; Li, J.; Sun, T.; Huang, Z.; Kosonen, R. Assessment of operational carbon emission reduction of energy conservation measures for commercial buildings: Model development. Energy Build. 2022, 268, 112189. [Google Scholar] [CrossRef]
  43. Xu, L.; Liu, J.; Pei, J.; Han, X. Building energy saving potential in Hot Summer and Cold Winter (HSCW) Zone, China-Influence of building energy efficiency standards and implications. Energy Policy 2013, 57, 253–262. [Google Scholar] [CrossRef]
  44. Shanghai Statistical Yearbook 2022. Available online: https://tjj.sh.gov.cn/index.html (accessed on 8 October 2023).
  45. Crawley, D.B.; Lawrie, L.K.; Winkelmann, F.C.; Buhl, W.F.; Huang, Y.J.; Pedersen, C.O.; Strand, R.K.; Liesen, R.J.; Fisher, D.E.; Witte, M.J.; et al. EnergyPlus: Creating a new-generation building energy simulation program. Energy Build. 2001, 33, 319–331. [Google Scholar] [CrossRef]
  46. GB50189-2015; Design Standard for Energy Efficiency of Public Buildings. Ministry of Housing and Urban-Rural Development of the People’s Republic of China: Beijing, China, 2015.
  47. Zhang, G.; Li, X.; Shi, W.; Wang, B.; Li, Z.; Cao, Y. Simulations of the energy performance of variable refrigerant flow system in representative operation modes for residential buildings in the hot summer and cold winter region in China. Energy Build. 2018, 174, 414–427. [Google Scholar] [CrossRef]
  48. JGJ134-2010; Design Standard for Energy Efficiency of Residential Buildings in Hot Summer and Cold Winter Zone. Ministry of Housing and Urban-Rural Development of the People’s Republic of China: Beijing, China, 2010.
  49. Sailor, D.J. A green roof model for building energy simulation programs. Energy Build. 2008, 40, 1466–1478. [Google Scholar] [CrossRef]
  50. Sailor, D.J.; Elley, T.B.; Gibson, M. Exploring the building energy impacts of green roof design decisions—A modeling study of buildings in four distinct climates. J. Build. Phys. 2012, 35, 372–391. [Google Scholar] [CrossRef]
  51. He, Y.; Yu, H.; Ozaki, A.; Dong, N.; Zheng, S. Long-term thermal performance evaluation of green roof system based on two new indexes: A case study in Shanghai area. Build. Environ. 2017, 120, 13–28. [Google Scholar] [CrossRef]
  52. Nie, Y.; Bu, Z.; Yang, J. Discussion on heating strategies in winter of residential buildings in Shanghai aiming at ultra-low energy target. Procedia Eng. 2017, 205, 3130–3137. [Google Scholar] [CrossRef]
  53. Huang, Y.; Kang, J.; Liu, L.; Zhong, X.; Lin, J.; Xie, S.; Meng, C.; Zeng, Y.; Shah, N.; Brandon, N.; et al. A hierarchical coupled optimization approach for dynamic simulation of building thermal environment and integrated planning of energy systems with supply and demand synergy. Energy Convers. Manag. 2022, 258, 115497. [Google Scholar] [CrossRef]
  54. Zhang, Q. Development of the typical meteorological database for Chinese locations. Energy Build. 2006, 38, 1320–1326. [Google Scholar] [CrossRef]
  55. National Climatic Data Center (NCDC); U.S. Air Force Combat Climatology Center. International Surface Weather Observations 1982–1997; NCDC: New Delhi, India; National Oceanic and Atmospheric Administration: Washington, DC, USA; US Dept. of Commerce: Asheville, NC, USA; The Air Force Combat Climatology Center (AFCCC): Asheville, NC, USA; US Dept. of Air Force: Washington, DC, USA, 1998; Volume 1–5. [Google Scholar]
  56. Andrić, I.; Gomes, N.; Pina, A.; Ferrão, P.; Fournier, J.; Lacarrière, B.; Le Corre, O. Modeling the long-term effect of climate change on building heat demand: Case study on a district level. Energy Build. 2016, 126, 77–93. [Google Scholar] [CrossRef]
  57. Tamer, T.; Dino, I.G.; Baker, D.K.; Akgül, C.M. Coupling PCM wallboard utilization with night Ventilation: Energy efficiency and overheating risk in office buildings under climate change impact. Energy Build. 2023, 298, 113482. [Google Scholar] [CrossRef]
  58. Duan, Z. Impact of climate change on the life cycle greenhouse gas emissions of cross-laminated timber and reinforced concrete buildings in China. J. Clean. Prod. 2023, 395, 136446. [Google Scholar] [CrossRef]
  59. Ashrafian, T. Enhancing school buildings energy efficiency under climate change: A comprehensive analysis of energy, cost, and comfort factors. J. Build. Eng. 2023, 80, 107969. [Google Scholar] [CrossRef]
  60. Chen, Y.; Ren, Z.; Peng, Z.; Yang, J.; Chen, Z.; Deng, Z. Impacts of climate change and building energy efficiency improvement on city-scale building energy consumption. J. Build. Eng. 2023, 78, 107646. [Google Scholar] [CrossRef]
  61. Wu, Y.; Sharifi, A.; Yang, P. Mapping building carbon emissions within local climate zones in Shanghai. Energy Procedia 2018, 152, 815–822. [Google Scholar] [CrossRef]
  62. Aleksejeva, J.; Voulgaris, G.; Gasparatos, A. Assessing the potential of strategic green roof implementation for green infrastructure: Insights from Sumida ward, Tokyo. Urban For. Urban Green. 2022, 74, 127632. [Google Scholar] [CrossRef]
  63. Peng, L.L.H.; Yang, X.; He, Y.; Hu, Z.; Xu, T.; Jiang, Z.; Yao, L. Thermal and energy performance of two distinct green roofs: Temporal pattern and underlying factors in a subtropical climate. Energy Build. 2019, 185, 247–258. [Google Scholar] [CrossRef]
  64. Zheng, X.; Kong, F.; Yin, H.; Middel, A.; Yang, S.; Liu, H.; Huang, J. Green roof cooling and carbon mitigation benefits in a subtropical city. Urban For. Urban Green. 2023, 86, 128018. [Google Scholar] [CrossRef]
  65. 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]
  66. Osma, G.; Ordonez, G.; Hernandez, E.; Quintero, L.; Torres, M. The impact of height installation on the performance of PV panels integrated into a green roof in tropical conditions. In Energy Production and Management in the 21st Century II: The Quest for Sustainable Energy; WIT Press: Southampton, UK, 2016; Volume 205, pp. 147–156. [Google Scholar]
  67. Schulte, J.; Knuts, S. Sustainability impact and effects analysis—A risk management tool for sustainable product development. Sustain. Prod. Consum. 2022, 30, 737–751. [Google Scholar] [CrossRef]
  68. He, Y.; Yu, H.; Dong, N.; Ye, H. Thermal and energy performance assessment of extensive green roof in summer: A case study of a lightweight building in Shanghai. Energy Build. 2016, 127, 762–773. [Google Scholar] [CrossRef]
Sustainability 16 00286 g001
Figure 1. The layers of a typical extensive green roof.
Figure 1. The layers of a typical extensive green roof.
Sustainability 16 00286 g001
Sustainability 16 00286 g002
Figure 2. Data layers of the building footprint, the 1 km population density grid, industrial land use area and its 200 m buffer zones.
Figure 2. Data layers of the building footprint, the 1 km population density grid, industrial land use area and its 200 m buffer zones.
Sustainability 16 00286 g002
Sustainability 16 00286 g003
Figure 3. Proportion (%) of individual components that contribute to the annual energy savings from green roofs compared with regular roofs in (a) 2020 and (b) 2050.
Figure 3. Proportion (%) of individual components that contribute to the annual energy savings from green roofs compared with regular roofs in (a) 2020 and (b) 2050.
Sustainability 16 00286 g003
Sustainability 16 00286 g004
Figure 4. Monthly energy savings (MJ/m2) from green roofs on five building types at a monthly scale under 2020 and 2050 climate conditions compared to regular roofs.
Figure 4. Monthly energy savings (MJ/m2) from green roofs on five building types at a monthly scale under 2020 and 2050 climate conditions compared to regular roofs.
Sustainability 16 00286 g004
Sustainability 16 00286 g005
Figure 5. Comparison of monthly total energy savings (MJ/m2) from green roofs under 2020 and 2050 climate conditions on five types of buildings.
Figure 5. Comparison of monthly total energy savings (MJ/m2) from green roofs under 2020 and 2050 climate conditions on five types of buildings.
Sustainability 16 00286 g005
Sustainability 16 00286 g006
Figure 6. Comparison of diurnal total energy savings (KJ/m2) from green roofs on the shopping mall and the apartment during the extremely hot summer days under 2050 climate conditions.
Figure 6. Comparison of diurnal total energy savings (KJ/m2) from green roofs on the shopping mall and the apartment during the extremely hot summer days under 2050 climate conditions.
Sustainability 16 00286 g006
Sustainability 16 00286 g007
Figure 7. Spatial distribution of the annual emission reduction in CO2 (panel (a)), PM2.5 (panel (b)), SO2 (panel (c)), and NOx (panel (d)) for individual buildings within Huangpu District, Shanghai.
Figure 7. Spatial distribution of the annual emission reduction in CO2 (panel (a)), PM2.5 (panel (b)), SO2 (panel (c)), and NOx (panel (d)) for individual buildings within Huangpu District, Shanghai.
Sustainability 16 00286 g007
Sustainability 16 00286 g008
Figure 8. Spatial distribution of buildings with high, moderate, and low priority to install green roofs within Huangpu District, Shanghai.
Figure 8. Spatial distribution of buildings with high, moderate, and low priority to install green roofs within Huangpu District, Shanghai.
Sustainability 16 00286 g008
Table 1. The prototypical building models.
Table 1. The prototypical building models.
Building TypeNumber of FloorsPrototypical Building Models
Office6Sustainability 16 00286 i001
Hotel5Sustainability 16 00286 i002
Shopping Mall5Sustainability 16 00286 i003
Hospital6Sustainability 16 00286 i004
Apartment5Sustainability 16 00286 i005
Table 2. Key thermal properties of commercial buildings in HSCW climate zones based on “Design standard for energy efficiency of public building of China” (GB50189-2015).
Table 2. Key thermal properties of commercial buildings in HSCW climate zones based on “Design standard for energy efficiency of public building of China” (GB50189-2015).
ParameterValueParameter Source from Ref.
Roof U-factor (W/m2K)0.4Liang et al. [42]
External Wall U-factor (W/m2K)0.6Liang et al. [42]
Shading coefficient (SC)0.4Liang et al. [42]
Air infiltration rate (h−1)0.2Liang et al. [42]
Outdoor air flow per person (m3/(h.p))30Liang et al. [42]
Table 3. Key envelope properties of residential building (apartment) in Shanghai.
Table 3. Key envelope properties of residential building (apartment) in Shanghai.
ParameterValueParameter Source from Ref.
Roof U-factor (W/m2K)0.557Zhang et al. [47]
External Wall U-factor (W/m2K)0.705Zhang et al. [47]
Floor U-factor (W/m2K)1.107Zhang et al. [47]
External Window U-factor (W/m2K)2.7Zhang et al. [47]
Table 4. Key parameters of four types of public buildings based on “Design standard for energy efficiency of public building of China” (GB50189-2015) [46].
Table 4. Key parameters of four types of public buildings based on “Design standard for energy efficiency of public building of China” (GB50189-2015) [46].
ParameterOfficeHotelShopping MallHospital
Per capita occupancy area (m2/p)102588
Equipment power density (w/m2)15151320
Lighting power density97109
Fresh air volume (m3/(h.p))30303030
Table 5. Electricity indirect emission factors of CO2, PM2.5, SO2, and NOx in Shanghai, China.
Table 5. Electricity indirect emission factors of CO2, PM2.5, SO2, and NOx in Shanghai, China.
Pollutant SourceEmission FactorParameter Source from Ref.
CO20.584 (kg/Kwh)Liang et al. [42]
0.726 (kg/Kwh)Yang and Hong [8]
0.788 (kg/Kwh)Wu et al. [61]
PM2.50.326 (kg/Mwh)Yang and Hong [8]
SO21.005 (kg/Mwh)Yang and Hong [8]
NOx1.604 (kg/Mwh)Yang and Hong [8]
Table 6. Absolute difference (MJ/m2) and relative change (%) in the annual building energy consumption between buildings with green roofs and regular roofs in 2020 and 2050 in Shanghai.
Table 6. Absolute difference (MJ/m2) and relative change (%) in the annual building energy consumption between buildings with green roofs and regular roofs in 2020 and 2050 in Shanghai.
Building Type2020 AD2020 RC2050 AD2050 RC
Office5.971.73%6.161.77%
Hotel17.234.0%16.243.79%
Shopping mall118.321.6%120.6622.2%
Hospital49.354.5%51.094.7%
Apartment32.168.5%33.168.6%
Table 7. The peak-to-valley (PTV) ratio and daily demand of cooling energy of the shopping mall and the apartment with two roof types during the extremely hot summer day under 2050 climate condition.
Table 7. The peak-to-valley (PTV) ratio and daily demand of cooling energy of the shopping mall and the apartment with two roof types during the extremely hot summer day under 2050 climate condition.
Building TypePeak-to-Valley RatioDaily Cooling Energy (KJ/m2)
Shopping mall with regular roof2.81788
Shopping mall with green roof1.81070
Apartment with regular roof1.91389
Apartment with green roof1.51246
Table 8. Estimation of the annual CO2 emission reduction associated with building energy demand savings after green roof installation on four types of public buildings in Huangpu District of Shanghai under 2020 climate condition.
Table 8. Estimation of the annual CO2 emission reduction associated with building energy demand savings after green roof installation on four types of public buildings in Huangpu District of Shanghai under 2020 climate condition.
Building TypeTotal Floor Area (km2)Annual CO2 Reduction (Kt)
Scenario 1Scenario 2Scenario 3
Office4.304.165.185.62
Hotel1.905.326.617.17
Shopping Mall1.613138.5441.83
Hospital0.907.28.959.72
Total9.7147.6859.2864.34
Table 9. Estimation of the annual PM2.5, SO2, and NOx emission reduction associated with building energy demand savings after green roof installation on four types of public buildings in Huangpu District of Shanghai under 2020 climate condition.
Table 9. Estimation of the annual PM2.5, SO2, and NOx emission reduction associated with building energy demand savings after green roof installation on four types of public buildings in Huangpu District of Shanghai under 2020 climate condition.
Building TypeTotal Floor Area (km2)PM2.5 (t)SO2 (t)NOx (t)
Office4.32.327.1711.44
Hotel1.902.979.1514.60
Shopping Mall1.6117.3153.3585.15
Hospital0.904.0212.3919.78
Total9.7126.6182.06130.97
Table 10. Classification of buildings regarding the suitability of green roof installation by considering location attributes and air pollution reduction associated potential with energy saving benefits.
Table 10. Classification of buildings regarding the suitability of green roof installation by considering location attributes and air pollution reduction associated potential with energy saving benefits.
Priority to Install Green RoofsRules
High(1) located in area of population density > 30,000 people/km2 and;
(2) located near industrial area (within the 200 m buffer zone) and;
(3) annual CO2 reduction larger than 800 t and annual air pollution (PM2.5, SO2, and NOx) reduction larger than 400 kg
Moderate(1) located in area of population density > 15,000 people/km2 and;
(2) annual CO2 reduction larger than 400 t and annual air pollution (PM2.5, SO2, and NOx) reduction larger than 200 kg or;
(3) located near industrial area (within the 200 m buffer zone)
Lowthe rest of the buildings
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Zheng, Y.; Chen, L. Modeling the Effect of Green Roofs for Building Energy Savings and Air Pollution Reduction in Shanghai. Sustainability 2024, 16, 286. https://doi.org/10.3390/su16010286

AMA Style

Zheng Y, Chen L. Modeling the Effect of Green Roofs for Building Energy Savings and Air Pollution Reduction in Shanghai. Sustainability. 2024; 16(1):286. https://doi.org/10.3390/su16010286

Chicago/Turabian Style

Zheng, Yuanfan, and Liang Chen. 2024. "Modeling the Effect of Green Roofs for Building Energy Savings and Air Pollution Reduction in Shanghai" Sustainability 16, no. 1: 286. https://doi.org/10.3390/su16010286

APA Style

Zheng, Y., & Chen, L. (2024). Modeling the Effect of Green Roofs for Building Energy Savings and Air Pollution Reduction in Shanghai. Sustainability, 16(1), 286. https://doi.org/10.3390/su16010286

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop