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 CO
2 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 PM
2.5 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 CO
2 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 O
3 accounting for 52%, NO
2 for 27%, PM
10 for 14%, and SO
2 for 7% of the total [
19]. Kostadinovic et al. measured the ambient concentration of PM
1, PM
2.5, and PM
10 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 PM
1, PM
2.5, and PM
10 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 PM
2.5, 6.9 kg of O
3, and 2.3 kg of NO
2 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.
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.