Low-Carbon Greening Strategies for Expressway Service Area: Optimizing Carbon Sink Design Across Diverse Climate Zones

Abstract

The increasing demand for sustainable development and carbon neutrality highlights the need to improve the energy efficiency of infrastructure, particularly in highway service areas. This study explores the application of green roofs as a low-carbon technology to reduce energy consumption across buildings located in different climate zones in China. A combination of theoretical modeling and simulation-based analysis was used to evaluate various green roof configurations in five representative cities: Harbin, Beijing, Wuhan, Guangzhou, and Kunming. The results show that green roofs can reduce annual building energy consumption by up to 2.02%, depending on climate and plant species. For example, fern roofs in Guangzhou reduced heating demand by 16.35%, while grass roofs in Wuhan lowered the daytime roof surface temperature by 31.82 °C. Furthermore, optimizing the building orientation to 60° led to energy savings of up to 7.73% in Kunming. These findings suggest that tailored greening strategies based on regional climate can effectively improve building energy performance and support the development of sustainable service infrastructure.

1. Introduction

Global climate change has become one of the most critical environmental challenges facing contemporary society. The primary driver of this phenomenon is the large-scale emission of greenhouse gases (GHGs), particularly carbon dioxide, resulting from extensive fossil fuel consumption [1,2,3]. In response to the urgent need for emission reduction, the international community has reached agreements such as the Paris Climate Accord, which aims to limit the global average temperature increase to below 1.5 °C [4]. Achieving these targets requires not only reductions in carbon emissions but also enhancements in carbon sink capacity [5,6,7].

Urban green infrastructure is increasingly recognized as an effective approach for mitigating the environmental impact of urbanization. Green spaces can improve air quality, regulate microclimates, reduce energy demand, and enhance the carbon sink capacity of cities [8,9,10,11]. Vegetation contributes to atmospheric carbon removal through photosynthesis and biomass accumulation, and its presence modifies the thermal environment, thereby reducing energy consumption in buildings [12,13,14]. Studies have demonstrated that the carbon sink potential varies significantly among plant species, with tree height, diameter at breast height (DBH), and leaf area index playing key roles [15,16,17]. In addition to trees, grass systems also contribute to urban carbon sinks and can serve as effective components in designed landscapes [18,19].

Among the various forms of green infrastructure, green roofs have attracted substantial attention due to their multifunctional benefits. These systems consist of layered assemblies that support plant growth on building rooftops, thereby providing thermal insulation, enhancing evapotranspiration, and improving overall building energy performance [20,21,22,23]. As the roof surface accounts for a significant portion of heat exchange in buildings, green roofs offer a practical solution for reducing indoor temperature fluctuations and Heating, Ventilation, and Air Conditioning (HVAC) energy loads. For instance, vertical and rooftop greening systems have been shown to lower indoor temperatures by up to 2.1 °C, reduce peak wall surface temperatures, and achieve annual energy savings of up to 8.9% in some cases [24,25,26]. Additionally, green roofs contribute to stormwater management, biodiversity conservation, and urban esthetics [27,28,29].

Despite the demonstrated benefits of green roofs, their effectiveness is highly influenced by climatic conditions. The thermal performance of vegetated roofs depends on a range of factors, including plant species selection, substrate properties, and local environmental parameters such as solar radiation, humidity, and temperature [21]. In hot and arid regions, drought-resistant species and moisture-retaining substrates are essential, while in cold climates, thermal insulation and frost-resistant vegetation are critical for effective performance. Failure to adapt green roof designs to regional conditions can significantly reduce their energy-saving and carbon sink efficacy.

Previous research has often treated green roof systems as generalized solutions, with limited consideration of climatic variability. This lack of regional adaptation constrains the scalability and practical implementation of green roofs in diverse contexts, particularly in countries like China, where climatic conditions vary significantly from north to south and from inland to coastal areas [30]. Furthermore, few studies have systematically examined the interaction between green roof design parameters and building characteristics, such as orientation, in relation to energy performance and carbon mitigation outcomes.

Highway service areas represent a critical yet understudied category of public infrastructure. These facilities, which support transportation networks and provide rest, retail, and service functions, are widely distributed and characterized by consistent energy demand due to their 24 h operational schedules [31,32]. Given their substantial roof surface area and standard building configurations, service areas offer considerable potential for the application of green roofs as a means of reducing energy consumption and supporting environmental goals.

To address the limited climatic responsiveness and typological specificity in existing green roof research, this study investigates the performance of green roofs in reducing building energy consumption and enhancing carbon sinks across five representative climate zones in China. The selected cities—Harbin, Beijing, Wuhan, Guangzhou, and Kunming—represent severe cold, cold, hot summer and cold winter, hot summer and warm winter, and temperate climate zones, respectively [33]. Through a combination of theoretical modeling and simulation-based analysis, the study evaluates the energy performance of different plant species and substrate configurations under varying climatic conditions. In addition, the influence of building orientation on energy use is assessed to support the development of integrated, climate-responsive greening strategies for service area buildings.

This research aims to propose scientifically grounded, regionally tailored design recommendations for green roof implementation in highway service areas. The findings contribute to the advancement of sustainable transportation infrastructure and provide empirical evidence for the broader adoption of low-carbon technologies in the built environment.

2. Methodology

2.1. Simulation Framework and Software

This study adopts a simulation-based approach to evaluate the energy-saving performance of green roofs in expressway service area buildings located in five representative climate zones in China. The building energy simulations were conducted using EnergyPlus 9.2. EnergyPlus allows detailed customization of building geometry, envelope construction, internal loads, HVAC operation, and advanced components such as green roofs through surface property modification and customized material layers. Simulations were performed on an hourly time step over an annual cycle, integrating local weather conditions and vegetation-specific parameters.

The simulation process consisted of the following steps:

Model Setup: The architectural and thermal model of the case study building was developed, including zoning, construction layers, occupancy, and HVAC profiles.

Climate Input: Weather data were obtained from the EnergyPlus official database (https://energyplus.net/weather, accessed on 30 September 2024), using China Standard Weather Data (CSWD) files. These datasets include 8760 h of detailed information on air temperature, solar radiation, humidity, wind speed, precipitation, and cloud cover, etc. CSWD files were applied for Harbin, Beijing, Wuhan, Guangzhou, and Kunming, each representing a distinct climate zone.

Green Roof Configuration: Green roof assemblies were defined using layered constructions for substrate and plant characteristics, embedded into the EnergyPlus material and surface property inputs.

HVAC and Internal Loads: HVAC operations were modeled in accordance with national building energy codes, including occupancy-based temperature setpoints, lighting, and equipment loads.

Post-Processing and Analysis: The simulation outputs included annual, seasonal, and hourly data on energy consumption for heating, cooling, and total loads under various green roof and orientation scenarios.

2.2. Case Study

This study focuses on the North Zone Complex of the Liangzihu Service Area, situated along the Ezhou-Xianning Expressway in Ezhou City, Hubei Province, China. The Liangzihu Service Area is strategically located between Liangzi Lake and Baoan Lake in Liangzihu Town, Ezhou City, Hubei Province, along the Ezhou–Xianning Expressway. As a key auxiliary facility of this expressway, its central site is positioned at K30 + 400. The area is surrounded by abundant ecological and cultural resources, including the Liangzi Lake Provincial Wetland Nature Reserve, Baoan Lake National Wetland Park, Baoan Lake Mandarin Fish Conservation Area, and Zaoshan Provincial Forest Park. Its construction reflects an integrated approach to balancing transportation demands with ecological preservation, aiming to enhance the environmental and cultural identity of the region.

Covering a net area of 189.8 acres, the Liangzihu Service Area is divided into two zones, a southern zone of 96.3 acres and a northern zone of 93.5 acres, with a total building area of 14,603 m², including 3471.14 m² of wooden structures. The structure of Liangzi Lake Express Service Area is shown in Figure 1. The facility features 412 small vehicle parking spaces, 20 bus parking spaces, 19 medium vehicle spaces, 48 large vehicle spaces, and 88 charging stations. With a building density of 6.49% and a greening rate of 41.23%, the service area embodies sustainable design principles. Its development integrates the “Service Area+” commercial model, leveraging local natural and cultural resources to establish a multifunctional hub encompassing dining, lodging, transportation, recreation, shopping, and entertainment.

The architectural model of the North Complex is shown in Figure 2. The total building area is 1554 m², with 963 m² designated as air-conditioned spaces. The zoning of the various functional areas of the North Complex is shown schematically in Figure 3. The building consists of two levels, with the ground floor accommodating a lobby, supermarket, restrooms, and storage facilities. The second floor houses a dining area. This functional zoning supports diverse service demands, enhancing operational efficiency and user experience.

The building has a window-to-wall ratio of 30%, aligning with energy-saving considerations in architectural design. The construction of the various levels of the envelope of the North Complex is shown in

Table 1. The construction of the layers of envelopes.

Structure	Layer 1	Layer 2	Layer 3	Layer 4	Layer 5
Sloping roof	Tile	Air gap	Strand board	Glass wool	Plasterboard
Flat roof	Asphalt	Extruded polystyrene	Air gap	Plasterboard	/
Ground floor	Foam	Cast concrete	Floor screed	Timber flooring	/
External wall	Cement panel	Glass wool	Concrete block	Gypsum plastering	/
Internal wall	Plasterboard	Concrete block	Plasterboard	/	/
External floor	External rendering	Stone wool	Timber flooring	/	/
Window	Glass 6 mm	Air 12 mm	Glass 6 mm	/	/
Ceiling	Cast concrete	/	/	/	/
Door	Painted oak	/	/	/	/

.

Table 2. Material parameters of the construction layer.

Material	Thickness (m)	Conductivity (W/m·K)	Density (kg/m3)	Specific Heat (J/kg·K)	Thermal Absorptance	Solar Absorptance	Visible Absorptance
Tile	0.01	0.84	1900	800	0.9	0.5	0.6
Strand board	0.015	0.13	650	1700	0.9	0.6	0.6
Glass Wool	0.0845	0.04	12	840	0.9	0.6	0.6
Plasterboard	0.013	0.25	2800	896	0.9	0.5	0.5
Asphalt	0.01	0.7	2100	1000	0.9	0.85	0.9
Extruded Polystyrene	0.07	0.034	35	1400	0.9	0.6	0.6
Foam	0.1327	0.04	10	1400	0.9	0.6	0.6
Cast Concrete	0.1	1.13	2000	1000	0.9	0.6	0.6
Floor Screed	0.07	0.41	1200	840	0.9	0.73	0.73
Timber Flooring	0.03	0.14	650	1200	0.9	0.78	0.78
Cement panel	0.009	0.12	400	1470	0.9	0.4	0.6
Concrete block	0.25	0.24	670	1000	0.9	0.6	0.6
Gypsum plastering	0.013	0.4	1000	1000	0.9	0.5	0.5
External Rendering	0.025	0.5	1300	1000	0.9	0.7	0.7
Stone Wool	0.1482	0.04	30	840	0.9	0.6	0.6
Cast Concrete	0.1	1.4	2100	840	0.9	0.6	0.6
Painted oak	0.035	0.19	700	2390	0.9	0.5	0.5

shows the detailed parameters of each material. The performance parameters of each enclosure structure are shown in

Table 3. The performance of opaque envelopes.

Structure	Area (m2)	Reflectance	U-Factor with Film (W/m2·K)	U-Factor No Film (W/m2·K)	Tilt (°)
Sloping roof	1162	0.50	0.400	0.424	22.49
Flat roof	184	0.15	0.409	0.434	0.00
Ground floor	1157	0.40	0.253	0.264	180.00
External wall	890	0.60	0.505	0.547	90.00
Internal wall	595	/	1.689	1.348	/
External floor	548	0.30	0.251	0.264	180.00
Ceiling	1060	/	3.848	14.000	/
Door	119	/	2.995	5.429	/

. The relatively moderate WWR helps balance natural lighting and thermal performance, reducing reliance on artificial lighting and HVAC systems. The parameters of the transparent enclosure are shown in

Table 4. The performance of transparent envelopes.

Window Glass	Numerical Value
Conductance (W/m2·K)	2.695
Solar heat gain coefficient	0.715
Solar transmittance at normal incidence	0.628
Visible transmittance at normal incidence	0.781

. This ratio, combined with the building’s functional layout, offers an opportunity to analyze energy performance under typical operational conditions in a high-traffic service area.

The probability of human presence is shown in

Table 5. Probability of people being present at different times.

Time	Weekdays	All Other Days
0–1	10%	20%
1–2	10%	20%
2–3	10%	20%
3–4	10%	20%
4–5	10%	20%
5–6	10%	20%
6–7	20%	45%
7–8	30%	70%
8–9	40%	100%
9–10	50%	100%
10–11	50%	100%
11–12	50%	100%
12–13	50%	100%
13–14	50%	100%
14–15	50%	100%
15–16	50%	100%
16–17	50%	100%
17–18	60%	100%
18–19	80%	100%
19–20	90%	100%
20–21	100%	85%
21–22	100%	60%
22–23	50%	40%
23–24	10%	20%

[34]. The HVAC operation hours were consistent with the building occupancy schedule. The distribution of internal heat sources, based on relevant energy conservation codes, and the results from the building research are shown in

Table 6. The distribution of internal heat sources on typical weekdays.

Zone	Operating Time	Fresh Air Volume (m3/(h p))	Lighting	Equipment (W/m2)	Maximum Population Density (p/m2)
Supermarket	0:00–24:00	20	7.5	5	0.4
Lobby	0:00–24:00	10	5	5	0.4
Washroom	0:00–24:00	30	3.5	5	0.1
Cafeteria	6:30–22:00	30	5	15	0.2

[35]. The room was temperature-controlled using a single air conditioner. The cooling temperature was set at 22 °C, and the heating temperature at 25 °C [36].

The green roof is a form of green landscaping according to the structural characteristics of the building roof, the load, and the ecological conditions on the roof. The roof greening construction layer from bottom to top is usually divided into the anti-leakage layer, root barrier layer, moisture layer, drainage layer, filtration layer, planting soil layer, vegetation layer, etc. The schematic construction of the green roof is shown in Figure 4. In real applications, slope-adapted green roof systems with anti-slip and lightweight substrate configurations would be required, and this has been acknowledged as a design assumption in our modeling process. While these construction layers make up the green roof, they also further increase the thermal resistance of the roof. The green roofs used in this study have green planting parameters as shown in

Table 7. Green planting parameters [37,38,39].

Name	Grass	Shrub	Climber	Fern
Height of Plant (m)	0.1	0.5	0.8	0.3
Leaf Area Index	2.5	3.0	4.0	2.0
Leaf Reflectivity	0.25	0.2	0.3	0.18
Leaf Emissivity	0.95	0.92	0.88	0.91
Minimum Stomatal Resistance (s/m)	180	100	60	150
Thickness (m)	0.15	0.25	0.2	0.18
Conductivity of Dry Soil (W/m·K)	0.25	0.3	0.4	0.27
Density of Dry Soil (kg/m3)	650	800	900	700
Specific Heat of Dry Soil (J/kg·K)	1100	1200	1050	1000
Thermal Absorptance	0.9	0.85	0.87	0.93
Solar Absorptance	0.8	0.75	0.78	0.82
Visible Absorptance	0.7	0.65	0.7	0.72
Saturation Volumetric Moisture Content of the Soil Layer	0.3	0.3	0.3	0.3
Residual Volumetric Moisture Content of the Soil Layer	0.01	0.01	0.01	0.01
Initial Volumetric Moisture Content of the Soil Layer	0.1	0.1	0.1	0.1

[37,38,39].

The topography of China varies markedly across latitudes and longitudes. The climate varies greatly from region to region. The Uniform Standard for Design of Civil Buildings divides the country into five climate zones [33]. To explore the performance of green roofs in service areas in different climate zones, Harbin, Beijing, Wuhan, Guangzhou and Kunming are selected as five typical cities. The location of the study cities in the climate zone is shown in Figure 5. The information representing the cities is shown in

Table 8. Information of the representative cities.

City	Climate Zone	Station Number	Latitude	Longitude	Elevation
Harbin	Severe Cold	509530	45.75	126.77	142.3
Beijing	Cold	545110	39.8	116.47	31.3
Wuhan	Hot Summer and Cold Winter	574940	30.62	114.13	23.1
Guangzhou	Hot Summer and Warm Winter	592870	23.17	113.33	41
Kunming	Temperate	567780	25.02	102.68	1892.4

.

2.3. Mathematical Methods

The energy balance of a green roof is primarily driven by solar radiative forcing. This radiation is offset by sensible heat flux through convection and latent heat flux via evaporation. The surface energy balance is used to describe and interpret the physical processes by which green roofs influence heat transfer between the building envelope and the surrounding environment. Specifically, we decompose the net surface energy into sensible heat, latent heat, and ground heat flux components.

The EnergyPlus simulation engine computes these quantities internally based on the building geometry, material definitions, weather inputs, and vegetation parameters. Therefore, the Fast All Season Soil Strength (FASST) model was used to supplement the EnergyPlus outputs with a physically grounded interpretation [41,42]. The FASST model is referenced to support the theoretical framework, but no separate data manipulation tool was applied outside of EnergyPlus. The model was selected for this study because it offers a well-validated and computationally efficient approach to modeling surface energy balance in vegetated systems under a wide range of environmental conditions.

The foliage energy balance is expressed as

$$F_f={\sigma }_f\left[I_S^{\downarrow }(1-{\alpha }_f)+{\epsilon }_f{I}_{ir}^{\downarrow }-{\epsilon }_f\sigma T_f^4\right]+{\displaystyle \frac{{\sigma }_f{\epsilon }_g{\epsilon }_f\sigma }{{\epsilon }_1}}\left(T_g^4-T_f^4\right)+H_f+L_f$$

(1)

$${{\epsilon }_1=\epsilon }_g+{\epsilon }_f-{\epsilon }_f{\epsilon }_g$$

(2)

The heat flux can be reasonably estimated using the following equation:

$$H_f=(1.1\ast LAI{\rho }_{af}{C}_{p,a}{C}_f{W}_{af})\ast (T_{af}-T_f)$$

(3)

The measurement unit is unit/m, expressed as

$$r_s={\displaystyle \frac{r_{s,min}}{LAI}}\cdot f_1\cdot f_2\cdot f_3$$

(4)

The resistance to moisture exchange offered by the boundary layer formed on the leaf surface is known as aerodynamic resistance. It is measured in units of (s/m) and is influenced by wind speed, surface roughness, and atmospheric stability. It is formulated as

$$r_a={\displaystyle \frac{1}{C_f{W}_{af}}}$$

(5)

The overall energy balance at the soil surface is

$$F_g=(1-{\sigma }_f)\left[I_s^{\downarrow }(1-{\alpha }_g)+{\epsilon }_g{I}_{ir}^{\downarrow }-{\epsilon }_g{T}_g^4\right]-{\displaystyle \frac{{\sigma }_f{\epsilon }_g{\epsilon }_f\sigma }{{\epsilon }_1}}\left(T_g^4-T_f^4\right)+H_g+L_g+K\ast {\displaystyle \frac{\partial T_g}{\partial z}}$$

(6)

Sensible heat flux between the soil surface and the air in its vicinity depends on the temperature difference between them and the wind speed within the canopy. It is given as

$$H_g={\rho }_{ag}{C}_{p,a}{C}_h^g{W}_{af}(T_{af}-T_g)$$

(7)

In order to solve the foliage and soil heat budget equations, the fourth-order terms $T_f^4$ and $T_g^4$ as well as the mixing ratio terms $q_{g,sat}$ and $q_{f,sat}$ are linearized as given:

$${\left[T_f^{(n+1)}\right]}^4={\left[T_f^n\right]}^4+4{\left[T_f^n\right]}^3\left[T_f^{n+1}-T_f^n\right]$$

(8)

$${\left[T_g^{(n+1)}\right]}^4={\left[T_g^n\right]}^4+4{\left[T_g^n\right]}^3\left[T_g^{n+1}-T_g^n\right]$$

(9)

The saturation mixing ratios at the ground and leaf surface temperatures are given as

$$q_{g,sat}\left(T_g^{n+1}\right)=q_{sat}\left(T_g^n\right)+{\left({\displaystyle \frac{\partial q_{sat}}{\partial T}}\right)}_{T_g^n}\ast \left(T_g^{n+1}-T_g^n\right)$$

(10)

$$q_{f,sat}\left(T_f^{n+1}\right)=q_{sat}\left(T_f^n\right)+{\left({\displaystyle \frac{\partial q_{sat}}{\partial T}}\right)}_{T_f^n}\ast \left(T_f^{n+1}-T_f^n\right)$$

(11)

After linearization, the final equations take the following forms:

$$C_1^f+C_2^f{T}_g+C_3^f{T}_f=0$$

(12)

$$C_1^g+C_2^g{T}_g+C_3^g{T}_f=0$$

(13)

The surface energy balance of an exposed roof is as follows:

$$Q_{sr,in}-Q_{sr,out}+Q_{lr,down}-Q_{lr,up}=Q_{cv}+Q_{ET}+Q_{tf}$$

(14)

The green roof generates additional energy fluxes due to vegetation cover and growth [43]. The surface energy balance of the green roof is as follows:

$$Q_{sr,in}-Q_{sr,out}+Q_{lr,down}-Q_{lr,up}=Q_{cv}+Q_{tp}+Q_{ep}+Q_{sp}+Q_{ss}+Q_{ph}+Q_{re}+Q_{tf}$$

(15)

3. Results and Analysis

The simulation operation results of the North District Complex were obtained according to the setup in the previous section. Harbin, Beijing, Wuhan, Guangzhou, and Kunming were taken as examples to analyze the application effect of green roofs.

3.1. The Impact of Green Roofs on Total Building Energy Consumption

The analysis in Figure 6 indicates that the implementation of green roofs on buildings in different regions of China has a notable impact on reducing annual energy consumption. The highest energy savings were achieved by grass green roofs in Beijing, Wuhan, Guangzhou, and Kunming, with 1.5%, 1.32%, 0.77%, and 2.02%, respectively. The energy efficiency of shrub green roofs was the highest in Harbin, with 1.64%, and that of grass green roofs was 1.49%. The type of green roof has a significant effect on energy-saving potential. The energy savings of shrub and grass roofs were generally better than those of other roofs. These findings suggest that the type of green roof is pivotal in determining energy-saving potential, and regional climate should be a critical factor in the selection process. The climate zone plays a significant role, as the thermal properties and insulation provided by green roofs can influence the building’s energy demand. Grasses, ferns, and shrubs have different growth habits, leaf areas, and transpiration rates, which can affect the roof’s ability to cool the building through evapotranspiration. The interaction between the green roof and the building’s HVAC system can also affect energy consumption. These factors collectively determine the effectiveness of green roofs in reducing a building’s energy consumption.

The effect of various green roof types on the cooling energy consumption of buildings in different climate zones in China is analyzed in detail in Figure 7. The energy consumption of green roof cooling in Beijing decreased slightly from 405.59 GJ to 402.31 GJ, with an energy saving rate of 0.81%. The energy consumption of green roof cooling in Harbin was reduced from 213.52 GJ to 212.73 GJ, with an energy saving rate of 0.37%. The energy consumption of grass green roof cooling in Wuhan was reduced from 552.79 GJ to 546.89 GJ, with an energy saving rate of 1.07%. The energy consumption of grass green roof cooling in Guangzhou was reduced from 577.98 GJ to 573.87 GJ, with an energy saving rate of 0.71%. The energy consumption for green roof cooling in Kunming was reduced from 156.03 GJ to 152.78 GJ. The energy saving rate was 2.08%. However, shrub roofs showed negative savings for the cooling energy consumption of buildings in areas other than Harbin. The impact of other plants on the cooling energy consumption of a building varies. The overall energy-saving rate is positive. These findings suggest that the implementation of green roofs can effectively reduce dependence on artificial cooling systems, thereby enhancing the sustainability of building operations. However, the cooling performance of green roofs varies significantly depending on the type of vegetation and the climatic context. Plant species differ in growth patterns, leaf area index, and transpiration capacity, all of which influence the roof’s ability to dissipate heat through evapotranspiration. Consequently, the selection of appropriate vegetation must consider regional climatic conditions to maximize cooling efficiency.

The effect of different types of green roofs on the heating energy consumption of buildings in different climate zones in China is analyzed in Figure 8. The energy consumption of shrub roof heating in Harbin decreased from 598.4 GJ to 580.39 GJ, with an energy saving rate of 3.01%. The energy consumption of shrub roof heating in Beijing was reduced from 284.26 GJ to 271.25 GJ, with an energy saving rate of 4.58%. The energy consumption of shrub roof heating in Wuhan was reduced from 141.97 GJ to 133.07 GJ, with an energy-saving rate of 6.27%. The energy consumption of fern roof heating in Guangzhou was reduced from 20.73 GJ to 17.34 GJ, with an energy saving rate of 16.35%. The energy consumption of shrub roof heating in Kunming was reduced from 58.51 GJ to 50.13 GJ, with an energy-saving rate of 14.32%. The various plants showed good heating and energy efficiency in all regions. The shrubs showed relatively better results. The high heating energy savings for all green roof types suggest that green roofs can significantly reduce reliance on artificial heating systems. The green roof provides an additional layer of insulation that reduces heat transfer from the building in winter. This was especially effective in colder climates, and the type of vegetation on a green roof affects its energy-saving potential.

3.2. The Effect of Orientation on Total Building Energy Consumption

The actual orientation of the Liangzi Lake Service Area North Complex is taken as the reference. Rotate the building orientation clockwise as the forward direction and anticlockwise as the reverse direction. The relationship between building energy consumption and building orientation is obtained. The effect of building orientation on the annual operational energy consumption of the roof at different rotation angles is analyzed in Figure 9. The energy saving rate fluctuates with the rotation angle, indicating that the orientation of the building can have a significant impact on its energy efficiency.

The building exhibits energy savings in all areas of forward rotation and achieves optimal energy savings at a 60° rotation. The buildings were in Harbin, Beijing, Wuhan, Guangzhou, and Kunming and had optimal energy savings of 5.31%, 7.55%, 5.66%, 4.65%, and 7.73%. Each building was in a north–south orientation, which allowed it to receive more light and reduce direct sunlight. The −30° rotation showed negative energy savings in all regions and reached energy savings at a −90° rotation. Whether or not energy was saved at a rotation of −60° varied from region to region. The solar altitude angle and radiant illumination vary in different climatic zones. This leads to differences in energy savings for the same angle of rotation in each region.

The effect of building orientation on roof cooling energy consumption is shown in Figure 10. The cooling energy consumption of the building was reduced in all zones in the case of positive rotation. However, the angle of rotation at which the energy saving rate peaks is not the same. The buildings’ cooling energy consumption in the case of counter-rotation shows a decreasing trend as the angle is rotated, and the cooling energy saving rate increases step by step. The best refrigeration energy-saving angles in Harbin, Beijing, Wuhan, Guangzhou, and Kunming were −90°, 60°, 90°, 60°, and 60°, and the maximum energy-saving rate was 5.31%, 7.55%, 5.66%, 4.65%, and 7.73%. This was due to the different levels of shielding from solar radiation at different angles in different regions.

The effect of building orientation on roof heating energy consumption is shown in Figure 11. The heating energy consumption of the building was reduced in all regions under positive rotation. In cold places such as Harbin and Beijing, the best heating energy saving rate was achieved when rotating 60°, with 5.57% and 13.31%, respectively. However, in warm places such as Wuhan, Guangzhou and Kunming, the best heating energy saving rate was achieved when rotating 90°, with 19.19%, 16.88% and 27.79%, respectively, and the heating energy consumption increased in the case of reverse rotation, except for the case of rotating −90° in Kunming. This was since buildings receive different solar radiation in different areas with different orientations.

3.3. The Effect of Green Roofs on Temperature

The roof surface temperature on a typical day in summer is shown in Figure 12. The roof covered with grass was used as an example to analyze the temperature changes on the inner and outer surfaces of the roof. The representative day of summer (1 July) and the representative day of winter (1 January) were selected. The internal surface temperature (T_gin) and external surface temperature (T_gout) of a green roof with grass and the internal surface temperature (T_rin) and external surface temperature (T_rout) of a normal roof were analyzed for the same outdoor temperature (T_out). The green roof with grass could make T_gin slightly lower than T_rin in summer due to the effect of HVAC under high temperatures outside at noon. In other cases, the T_gin was almost the same as the T_rin. The T_gout was higher than the T_rout at night due to the heat storage effect of the soil and the effect of grass on air convection. The shading effect and transpiration of plants can effectively reduce the external surface temperature during daytime. The maximum reduction of T_gout relative to T_rout was 31.82 °C during the day in Wuhan.

The roof surface temperature on a typical day in winter is shown in Figure 13. T_gin was slightly higher than T_rin at night in winter, due to HVAC, and slightly lower than T_rin during the day, while in T_gin was slightly lower than T_rin during the day. T_gout was higher than T_rout at night. T_gout and T_rout during the daytime in the relatively cold areas of Beijing and Harbin were almost the same. In the warmer areas of Wuhan, Guangzhou, and Kunming, daytime T_gout was lower than T_rout. This was due to the fact that the outer surface receives more solar radiation. T_gout could be higher than T_rout by 7.22 °C at night in the Kunming area. This was caused by soil heat storage as well as the combined effect of plants.

4. Discussion

This study explored the energy-saving potential of green roofs and building orientation adjustments in expressway service area buildings across five distinct climate zones in China.

The performance of green roofs varied with vegetation type and climate. Grass and shrub roofs showed better results in temperate and cold regions such as Kunming and Harbin. This aligns with prior studies indicating that vegetation adapted to regional climates enhances insulation and energy performance [22,38].

In contrast, fern roofs proved more effective in warm and humid climates like Guangzhou. Their ability to reduce heating energy use is consistent with findings by Sailor, who emphasized the role of high leaf reflectivity and evapotranspiration [39]. This highlights the importance of selecting vegetation not only for esthetics or availability but also for physiological traits like leaf area index and stomatal resistance [21].

Building orientation had a clear impact on energy use, with a 60° rotation from the baseline orientation offering consistent savings across all cities. This corresponds with findings by Bhamare et al., who noted that passive strategies like orientation and solar exposure management can significantly influence energy efficiency [23].

There is also potential in combining orientation adjustments with vegetation design. For example, aligning plant shading with solar angles may improve cooling in hot months. Similar integrated approaches have been discussed in earlier research on green infrastructure multifunctionality [27].

From a practical standpoint, the vegetation types modeled in this study are generally accessible in landscaping across China. However, in regions with low rainfall or long dry seasons, irrigation may be necessary, potentially offsetting some energy benefits. This trade-off between water use and cooling efficiency has been noted in studies such as that of Hsieh et al. [11].

Taken together, the findings support the idea that green roof design should be climate-sensitive, with consideration given to vegetation traits, substrate properties, and building layout. These elements work best as a system, rather than in isolation, and offer practical strategies for reducing energy use in public infrastructure.

Further studies could expand on this by incorporating real-time weather data, plant growth stages, and irrigation systems into simulations. Exploring mixed green systems, such as combining green roofs with vertical greenery, may also improve energy performance while addressing space and maintenance constraints.

5. Conclusions

This study explored the application of green roofs in expressway service area buildings across five representative climate zones in China, focusing on their energy-saving performance and the impact of building orientation. The conclusions drawn from the simulation results are as follows:

(1)

Green roofs can effectively reduce the annual energy consumption of buildings. Grass roofs achieved the highest energy savings in Beijing (1.5%), Wuhan (1.32%), Guangzhou (0.77%), and Kunming (2.02%), while shrub roofs performed best in Harbin with a saving rate of 1.64%. These results indicate that the choice of vegetation type should be tailored to local climatic conditions, with grasses and shrubs showing the best performance in temperate and cold regions, and ferns proving advantageous in warmer climates.

(2)

In terms of heating energy consumption, fern roofs in Guangzhou reduced energy use by 16.35%, while shrub roofs in Kunming achieved savings of 14.32%. For cooling energy, grass roofs in Kunming resulted in a 2.08% reduction. These results demonstrate the climate sensitivity of green roof performance, highlighting the importance of vegetation physiology, moisture retention, and thermal inertia.

(3)

Orientation optimization significantly affects building energy consumption. A rotation of 60° produced the best overall energy savings across all five cities, with maximum reductions of 5.31% (Harbin), 7.55% (Beijing), 5.66% (Wuhan), 4.65% (Guangzhou), and 7.73% (Kunming). While green roofs and building orientation were studied independently, their combined effects suggest potential synergies, particularly when aligning vegetation shading patterns with solar exposure.

(4)

On typical summer days, green roofs effectively reduced the external surface temperature of the roof. In Wuhan, the grass roof decreased the outer surface temperature by up to 31.82 °C during the daytime. In Kunming during winter nights, green roofs increased the roof surface temperature by 7.22 °C due to soil heat retention and reduced convective loss.