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Article

The Impact of Residential Cluster Layout on Building Energy Consumption and Carbon Emissions in Regions with Hot Summers and Cold Winters in China

by
Junle Yan
1,
Hui Zhang
1,2,*,
Xiaoxin Liu
1,
Ling Ning
1 and
Wong Nyuk Hien
2
1
School of Civil Engineering, Architecture and Environment, Hubei University of Technology, Wuhan 430068, China
2
College of Design and Engineering, National University of Singapore, Singapore 117566, Singapore
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(15), 11915; https://doi.org/10.3390/su151511915
Submission received: 16 June 2023 / Revised: 27 July 2023 / Accepted: 31 July 2023 / Published: 2 August 2023
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Abstract

:
Since the early 2010s, building energy consumption in regions in China with hot summers and cold winters has experienced an average annual growth rate of 6.5%, while building carbon emissions demonstrated an average annual growth rate of 3.7%. This underscores the pressing need to reduce building energy consumption and carbon emissions. The layout of residential clusters plays a critical role in determining the effective shading coefficient, which directly impacts solar radiation gain and subsequently affects energy consumption and carbon emissions. To explore this correlation and optimize the layout configuration of residential clusters to achieve the objective of minimizing energy consumption and carbon emissions in buildings, our study employed ECOTECT 2011 software to assess the layout attributes of different residential clusters through an analysis of the effective shading coefficient. Furthermore, using VirVil-HTB2 17_04_21 software, we simulated the solar radiation, building energy consumption, and carbon emissions for different residential cluster layouts. To examine the interplay between solar radiation, energy consumption, and carbon emissions, SPSS 27 software was used. The findings revealed that different residential cluster configurations exhibit unique effective shading coefficients, substantially influencing the solar radiation received by buildings and, consequently, their thermal performance. Our research reveals that adopting a staggered layout can lead to a reduction in average operating energy consumption by up to 2.23% and cooling energy consumption by up to 7.17%, compared to an enclosed layout. Similarly, enclosed layouts can contribute to a decrease in heating energy consumption by up to 4.06%, in contrast to courtyard layouts. Additionally, scattered layouts can effectively reduce carbon emissions by up to 0.95% when compared to courtyard layouts.

1. Introduction

With global climate change, environmental pollution and preservation have received considerable attention. To promote environmental protection, prioritizing energy conservation and emission reduction measures is crucial. Addressing the energy consumption and carbon emissions of buildings plays a vital role within this framework. In 2021, buildings accounted for approximately 30% of global energy consumption and contributed to 27% of global CO2 emissions [1]. China, as the second-largest economy, has experienced substantial growth in its construction industry due to economic development. By 2020, China had a building inventory of 69.6 billion m2, with residential buildings comprising 80% of the total stock. Urban residential buildings accounted for 58% of the residential buildings, consuming 410 million tce of energy, which is 39% of the total energy consumed in the building sector. The total carbon emissions of the building process was 5.08 billion tCO2, with the building operation phase contributing 2.16 billion tCO2 [2]. Therefore, China’s efforts to reduce buildings’ energy consumption and carbon emissions are crucial for global energy conservation and emission reduction [3]. Residential buildings are a crucial factor in achieving these goals, and the factors that influence building energy consumption must be studied to achieve the goals of energy conservation and emission reduction. Furthermore, the contributions of China’s regions with hot summers and cold winters to building energy consumption and carbon emissions are rising, with a total building energy consumption of 360 million tce and an average annual growth rate of 6.5% during 2010–2020. Building carbon emissions amounted to 610 million tCO2, with an average annual growth rate of 3.7% [2]. Hence, in China, the energy consumption and carbon emissions of urban residential buildings are crucial constituents of the overall building energy consumption and carbon emissions, particularly in regions with hot summers and cold winters. Therefore, investigating urban residential buildings is vital for comprehending and mitigating building energy consumption and carbon emissions [3]. Consequently, urban residential buildings in hot summer and cold winter regions were selected as our study focus. Wuhan, an important city that has hot summers and cold winters, was chosen as the study area.
Numerous scholars worldwide have established a strong correlation between a building’s energy performance and its location within the urban context, as well as the surrounding buildings [4]. Buildings strongly influence the urban thermal and climatic environment due to their contribution to the intricate urban surface geometry and internal environment, resulting in a range of urban effects [5]. The layout of building groups, influenced by environmental conditions and building location, plays a crucial role in shaping these urban effects and impacting the energy performance of buildings [6,7,8]. To achieve energy-efficient building design, the energy-intensive aspects of existing building designs must be reassessed, and building layouts need to be optimized. Emphasizing building layout in novel design approaches offers a promising solution that can comprehensively address multiple factors, including comfort [9].
Building complexes play a large role in shaping microclimate environments within urban and neighborhood settings. Outdoor microclimatic factors such as temperature, wind speed, relative humidity, and solar radiation have a substantial impact on the energy demand of buildings [10]. The layout of residential groups affects the shading coefficient of buildings, subsequently influencing solar radiation reception and, ultimately, the energy efficiency of buildings [11,12]. Although researchers have examined the influence of building group layouts on energy consumption, specific quantitative analysis is lacking regarding the dynamic shading conditions resulting from residential group layouts and their impact on solar radiation reception. This aspect directly affects building energy consumption and carbon emissions. Some studies have explored various urban design factors’ linear impacts on heating energy consumption, such as building site coverage area, floor area ratio, building height, road height–width ratio, total wall surface area, and green space ratio [13,14,15]. Deng Qingtan et al. provided a more detailed discussion on the effects of building spacing and orientation on building energy consumption [16]. Jianxiang Huang et al. concluded that the energy performance of buildings in dense urban areas is partially influenced by the surrounding environment, which is affected by factors such as building form [17].
Multiple scholars have conducted in-depth research on the influence of building group layouts on building energy consumption, highlighting their importance in determining energy usage. Optimizing building layouts has proven effective in reducing energy consumption [18,19,20]. As such, in this study, we aimed to examine the relationship between the configuration patterns of residential clusters and their energy consumption and carbon footprint. Furthermore, we aimed to consider the influence of climate factors on energy consumption and carbon emissions of buildings across different regions. Specifically, in regions of China characterized by hot summers and cold winters, persistent high temperatures and humidity during summers, coupled with cold and humid winters, as well as long periods of wetness during the transitional seasons of spring and fall, create a diverse range of heating, cooling, ventilation, and dehumidification requirements for residential buildings throughout the year. These demands are influenced by climate characteristics, living habits, and economic conditions, among other factors, resulting in intermittent short-cycle usage patterns for heating and cooling operations in residential buildings within the region. The usage patterns of such equipment often deviate from the relevant energy-saving standard calculations in terms of usage cycles and operating conditions [21]. Consequently, the energy-saving indices of residential buildings deviate from actual energy consumption, thus impacting the effectiveness of energy-saving measures. Consequently, the layout design of residential districts in China’s regions with hot summers and cold winters needs to be investigated, considering the unique climatic conditions of these areas, with the goal of promoting energy conservation and reducing carbon emissions.
In this study, using a quantitative analysis approach, typical residential building group layout patterns were analyzed to investigate the unique wind and thermal environments formed by the residential group clusters, as well as the influence of the building cluster layout forms on building energy consumption. We used the Wuhan area, which is hot in summer and cold in winter, as our research object, and chose the settlement scale as the research scope. The investigation of energy consumption and carbon emission characteristics in residential building complex layouts within hot summer and cold winter regions, as explored in this study, has important implications not only for these specific regions but also for other areas worldwide. The methodology employed and the examination of building complex layouts also serve as valuable references for comparable studies conducted in different regions.
The remainder of the paper is structured as follows: Section 1 will provide an overview of the research background and content of this study. Section 2 details the study methodology, tools, and relevant parameter settings. Section 3 presents an analysis of simulation results, focusing on a detailed investigation of the relationships between mutual shading, wind environment, and thermal environment formed by different layout patterns of building clusters. Section 4 explores the correlation between relevant data and the reasons why the layout forms of different residential group clusters affect building energy consumption. Section 5 provides a summary of the pertinent findings of this study.

2. Methodology

2.1. Study Area and Climate Characteristics

In China, the regions with hot summers and cold winters have the coldest monthly average temperature ranging from 0 to 10 °C and the hottest monthly average temperature ranging from 25 to 30 °C. Additionally, the average daily temperature is ≤5 °C for 0 to 90 days and ≥25 °C for 49 to 110 days. This area encompasses 16 provinces, municipalities, and autonomous regions, with an area of approximately 1.8 million square kilometers, a population of around 550 million, and a GDP accounting for approximately 48% of the country’s total. Given its high population density and economic development, studying building energy consumption and carbon emissions in this area is crucial [22]. Wuhan, located in the middle reaches of the Yangtze River, is a representative city with hot summers and cold winters in China and was thus chosen as the study area.
Wuhan has a monsoonal climate characterized by the frequent exchange of air currents from north to south. The region experiences abundant rainfall, with the majority occurring from June to August, resulting in annual precipitation ranging from 1150 to 1450 mm. The city enjoys ample sunshine, with an average annual temperature of 15.8–17.5 °C. The wind patterns in Wuhan are seasonal, with predominantly southeasterly and southerly winds in summer and northeasterly and northerly winds in winter. The local climate is influenced by topography, leading to both breezy and calm conditions. The proximity of Wuhan to the Yangtze River contributes to its humid weather.

2.2. Sample Selection Method

In this study, we performed statistical analysis on a total of 25 residential building clusters located in the Jianghan, Jiang’an, and Wuchang districts of Wuhan. Our findings revealed the presence of six primary types of layout forms: parallel row layout, staggered row layout, scattered layout, enclosed layout, courtyard layout, and hybrid layout. The statistical results and classifications of these layout forms are presented in Table 1. Based on the statistical results, six typical layouts of residential building clusters were selected for simulations ad calculations of building energy consumption and carbon emissions. As shown in Table 2, these layouts were labeled as Type 1 parallel row layout (PRL1), Type 2 staggered row layout (SRL2), Type 3 scattered layout (SL3), Type 4 enclosed layout (EL4), Type 5 courtyard layout (CL5), and Type 6 hybrid layout (HL6).

2.3. Research Tools and Methods

To thoroughly examine the influence of residential cluster layout patterns on building energy consumption and carbon emissions, we extensively employed VirVil-HTB2 17_04_21 and ECOTECT 2011 software for meticulous analysis and evaluation. The HTB2 10_18_02_01 software, developed by the Welsh School of Architecture at Cardiff University, UK, is a comprehensive energy simulation tool. VirVil-HTB2 17_04_21 is a dynamic energy simulation plug-in operating on the SketchUp 2018 platform, a widely used design aid tool, enabling building energy simulation at the urban scale [23]. The accuracy and reliability of its simulation results have been validated by experts in the field [24].
This study focused on residential buildings within and around residential clusters as a homogeneous group, disregarding the impact of different building types on energy consumption and carbon emissions. Building upon this foundation, the present study predominantly involves an examination of the impact of the layout form of residential complexes on the average operating energy consumption, heating energy consumption, and cooling energy consumption of buildings. Moreover, the power consumption associated with heating and cooling processes constitutes the primary reference index for analyzing the carbon emission intensity of buildings. To compare the effects of residential cluster layout factors, uniform metrics such as building density, floor area ratio, and number of stories were used, treating the cluster layout as the independent variable. VirVil-HTB2 17_04_21 software, in conjunction with the SketchUp 3D modeling software, was employed to calculate building energy consumption and carbon emissions based on the study area’s geolocation and climatic conditions [25]. The results of the simulations provided primary indicators, including building energy consumption, carbon emissions, effective shading coefficient, and solar radiation. The effective shading coefficient and solar radiation are particularly important metrics for evaluating the impact of cluster layout on energy consumption and carbon emissions. The study’s approach enhances our understanding of the complex relationship between the built environment and its environmental footprint, with implications for sustainable urban planning and design [26].
The simulation and calculation analysis using VirVil-HTB2 17_04_21 software was conducted to obtain the average operating energy consumption, average heating energy consumption, average cooling energy consumption, and direct solar radiation of six residential building layout forms. The software employs direct, diffuse, and direct normal solar radiation to estimate the solar radiation received by the external surface, which is quantified by the direct solar radiation index [27]. We considered the direct solar radiation index as an important reference indicator.
In this study, ECOTECT 2011 software was utilized as a tool to assess the constructed milieu, characterized by dependable data analysis capabilities and an advanced and interactive visual interface [28]. Specifically, we aimed to determine the effective shading coefficients of building clusters using the aforementioned software, thus facilitating a comprehensive appraisal of the shading relationships among structures and their impact on the solar radiation influx received by the edifices. The shading coefficient is a widely used term in building thermal design, representing the shading effectiveness of a shading device. Typically, it is a constant value indicating the proportion of solar radiation that penetrates a static shading device, and this approach can be employed for vertical facade vegetation systems [29]. As shading circumstances vary between different layout forms of building complexes, the coefficient may also be used to quantify shading performance among building complexes. The shading coefficient can be computed to depict shading performance across various times of day and months [29].
ECOTECT 2011 software calculates the effective shading coefficient by comparing the amount of solar radiation allowed on a surface to the solar radiation that would pass through a 3 mm thick single-pane window. The effective shading coefficient values were calculated for all selected surfaces and presented as a percentage [30]. Building shading can affect the reception of solar radiation by building walls and roofs, which can in turn modify the microclimate of a building [11]. The microclimate characteristics of settlement-scale building complexes are the outcome of the interactions of multiple factors and are influenced by a blend of wind and thermal environments and other factors [31,32]. Hence, a comprehensive analysis was conducted of the six layout forms considering diverse parameters such as wind environment, thermal environment, and other relevant factors, when evaluating the energy consumption and carbon emission of residential clusters. Such an approach facilitated a comprehensive understanding of the multifaceted influence of diverse factors on the energy consumption and carbon emission of buildings. The simulation data from VirVil-HTB2 were statistically analyzed using SPSS 27 software. Fitted regression analysis in SPSS is commonly used to analyze data related to building energy consumption and carbon emissions. This approach helps quantify the impact of variables in an experiment on energy consumption and carbon emissions, as well as to assess the correlation and causality between relevant variables [33].
To specifically investigate the impact of the effective shading coefficient on solar radiation received by a residential complex, subsequently affecting thermal performance, energy consumption, and carbon emissions of the building, we used SPSS 27 software to analyze the linear correlation between the effective shading coefficient, solar radiation, building energy consumption, and carbon emissions. Building on this foundation, a comparative analysis was conducted on the layout configurations of residential clusters in regions with hot summers and cold winters. The aims were to understand how different effective shading coefficients and building cluster layouts influence the amount of solar radiation absorbed by buildings and to explore the correlation between energy consumption and carbon emissions. The objective of this study was to provide insights and references for energy-efficient and low-carbon building design.

2.4. Parameter Setting

Using VirVil-HTB2 17_04_21 software in conjunction with SketchUp 3D modeling software, we established a standardized geographic location for the model, specifically Wuhan, Hubei Province, China. This allowed us to ascertain the precise latitude and longitude coordinates of the building within the model and to incorporate relevant climatic characteristics. Moreover, to ensure accurate representation, we imported climate files specific to the Wuhan area using Add HTB2 files with a plugin option in VirVil-HTB2, thereby enabling the precise configuration of the model’s climate parameters.
In this study, to facilitate data comparison and variable control, building units of uniform base area, in accordance with the building scale of residential areas, were used. The building units were designed to have a consistent number of floors and total building footprint to ensure uniform indices of building density, floor area ratio, and body shape coefficient. By modifying the arrangement of residential spaces, the energy consumption data of residential areas under different spatial layout patterns were simulated.
In accordance with the “Standard for Urban Residential Area Planning and Design” (GB 50180-2018) [34], the floor area of a single building was established as 40 m × 10 m, with a story height of 3 m and 20 stories. To facilitate model calculation analysis, the number of single buildings was designated as 12.
In this study, VirVil-HTB2 17_04_21 software was employed to simulate building energy consumption data in Wuhan. Specifically, the heating energy consumption during a five-month period spanning from November to March and the cooling energy consumption over a nine-month period from March to November, during which both heating and cooling were required in March and November, were calculated. Consequently, the energy consumption data obtained via simulation for the aforementioned timeframe pertained to the heating and cooling energy consumption in question.
The parameters of the model were determined based on relevant regulations, including the “Design Standard for Energy Efficiency of Residential Buildings in Hot Summer and Cold Winter Zone” (JGJ134-2010) [22], the “Design Standard for Residential Buildings of Low Energy Consumption for Hubei Province” (DB42/T559-2022) [35], and the “Code for Thermal Design of Civil Building” (GB 50176-2016) [36]. Table 3 presents the parameters of the model. To simulate the model, the climate parameter file for the Wuhan area was obtained from the official website of Energy Plus (https://energyplus.net/weather, accessed on 15 June 2023).

2.5. Carbon Accounting Methods

Currently, three primary approaches are available for quantifying the carbon emissions of buildings: the actual measurement method, the mass balance method, and the emission factor method [37]. Among these, the emission factor method has been widely applied in China and has matured, with numerous exemplars and corresponding calculation formulae available [38].
In this study, the more mature emission factor method was used. In the “Standard for Building Carbon Emission Calculation” (GBT 51366-2019) [39], the formula for calculating carbon emissions in the operation phase of buildings is
C M = ∑ i = 1 n E i × E F i
where CM is the annual carbon emission per unit floor area in the operation phase of the building (kgCO2/m2·a); Ei is the annual consumption per unit area of energy of the ith category of the building (unit/m2·a); and EFi is the carbon emission factor of energy type i (tCO2/mWh).
The computation of carbon emissions from energy usage in urban residential buildings primarily involves quantifying the carbon emissions associated with the direct energy consumption by urban residents, which encompasses the carbon emissions stemming from the utilization of energy for transportation, heating, cooking, lighting, and other household appliances [40]. In residential building carbon emission calculations, the total carbon emissions usually include carbon emissions from gas usage, domestic electricity consumption, and central heating [41]. As centralized heating systems are presently planned solely for deployment in the northern region of China, which the Wuhan area is not a part of because it is located within the Yangtze River basin, the carbon emissions from heating in the Wuhan region primarily originate from electricity consumption for heat production during winter [42]. When estimating carbon emissions through the utilization of the VirVil-HTB2 model, the primary calculation parameter pertains to electricity consumption. However, during the simulation calculation, the values of electricity consumption remain constant, with the exception of cooling and heating consumption. Consequently, in this study, the electricity consumption for cooling and heating will serve as the primary reference variable for analyzing carbon emission intensity. Therefore, the calculation formula was simplified to:
C M = E × E F
where CM is the annual carbon emission per unit floor area in the operation phase of the building (kgCO2/m2·a); E is the annual consumption of electric energy per unit area of the building (kWh/m2·a); and EF is the carbon emission factor of electric energy (tCO2/mWh, see Table 4).

3. Simulation Results

3.1. Analysis of the Relationship between Building Layout and Energy Consumption

3.1.1. Analysis of General Characteristics

We examined the relationship between the average effective shading coefficient (AESC), average solar radiation (ASR), and average operational energy consumption (AOEC) throughout one year. The findings in Figure 1 and Table 5 and Table 6 indicate the rankings of the six layout forms based on their shading effect, solar radiation, and operational energy consumption. The order of average effective shading coefficients, from highest to lowest, was CL5 > SRL2 > PRL1 > HL6 > SL3 > EL4. Similarly, the average solar radiation rankings, from highest to lowest, were EL4 > SL3 > HL6 > PRL1 > SRL2 > CL5. These results demonstrated a consistent pattern between the shading coefficient and solar radiation, influenced by the shading relationship between building groups. Regarding the average operational energy consumption, the rankings from highest to lowest were EL4 > CL5 > SL3 > HL6 > PRL1 > SRL2. The data suggested a correlation between average energy consumption and the amount of solar radiation received by building groups, except for the Type 5 courtyard layout due to its unique wind–heat environment in Wuhan during summer. However, further analysis was required to explore the building energy consumption indices, including the shading coefficient, solar radiation, and average cooling and heating energy consumption, during the winter and summer seasons for different residential building group layouts.

3.1.2. The Relationship between the Layout of Residential Clusters and Building Cooling Energy Consumption

Based on the data presented in Figure 2, the average cooling energy consumption (ACEC) of the six types of layout exhibited a similar pattern of high and low distribution as the average operational energy consumption. The rankings for average cooling energy consumption, from highest to lowest, were as follows: EL4 > SL3 > CL5 > HL6 > PRL1 > SRL2. Additionally, the summer effective shading coefficients (SESCs) and summer solar radiation (SSR) also exhibited distinct rankings among the six layout forms. For the summer effective shading coefficients, the rankings for shading effectiveness, from highest to lowest, were as follows: PRL1 > CL5 > SRL2 > HL6 > SL3 > EL4. Similarly, the rankings for solar radiation received by the building complex during summer, from highest to lowest, were as follows: EL4 > SL3 > HL6 > PRL1 > SRL2 > CL5.
We examined the shading coefficients of different layouts to understand their impact on the thermal performance of the buildings in different seasons. The results revealed that the Type 4 enclosure layout, with the lowest shading coefficient of 9.51% in summer, exhibited the highest cooling energy consumption (53.94 kWh/m2·a) and solar radiation index (32,392.167 kWh/m2·a). The Type 1 parallel row layout, with the highest shading coefficient of 8.99%, had a slightly higher cooling energy consumption (50.12 kWh/m2·a) compared with the Type 2 staggered row layout (50.07 kWh/m2·a).
The observed trend in high and low solar radiation corresponds well with the effective shading coefficient of the building complex, except for the Type 1 parallel row layout. Notably, this layout had the highest shading coefficient but did not exhibit the lowest solar radiation. This could be attributed to the limitations of the average shading coefficient in capturing the shading performance of individual buildings. Some buildings may receive excessive solar radiation, whereas others may receive insufficient radiation, resulting in a diverse distribution of solar radiation within the complex. This variation cannot be fully represented by the average shading coefficient or the average solar radiation.
The Type 5 courtyard layout displayed wide variability in terms of solar radiation reception and the summer shading coefficient. Despite having a shading coefficient of 9.01%, which was not the highest, it exhibited the lowest solar radiation reception (29,873.765 kWh/m2·a) among all the considered layout types. This is due to the design pattern of the courtyard layout, which includes common walls between building volumes, reducing the available wall area for solar radiation. However, the average shading coefficient of the building complex failed to capture the variations in shading among individual buildings. Moreover, the unique internal wind and thermal environment of the courtyard layout during hot summers and cold winters contribute to its higher average cooling energy consumption of 50.86 kWh/m2·a. This can be attributed to the distinct microclimate created by the courtyard layout, leading to different wind pressure zones. As the experimental prototype in this study, we adopted a sealed building configuration with a central open-air passage between two courtyard arrangements, which hinders effective ventilation and heat dissipation during summer, resulting in increased cooling energy consumption.
The Type 6 hybrid layout had a summer effective shading coefficient of 9.2%, direct solar radiation of 30,761.256 kWh/m2·a, and an average cooling energy consumption of 50.45 kWh/m2·a, displaying a consistent trend in its energy performance. The impact of the hybrid layout on building energy consumption is relatively straightforward, as indicated by the results. The design of the hybrid layout, characterized by parallel rows and dispersed arrangement, allowed for the creation of internal air passages and the development of areas with different air pressures. However, in cases where the building density was high, this layout restricted proper internal ventilation and heat dissipation, leading to higher internal temperatures than the surrounding ambient temperature. As a result, the building’s energy consumption increased.

3.1.3. The Relationship between the Layout of Residential Clusters and Building Heating Energy Consumption

The average heating energy consumption (AHEC) of the six residential cluster layouts ranked as follows: CL5 > SRL2 > PRL1 > HL6 > SL3 > EL4 (Figure 3). Similarly, the winter effective shading coefficients (WESCs) for the six layout patterns ranked from highest to lowest in terms of shading effectiveness as follows: CL5 > SRL2 > PRL1 > HL6 > SL3 > EL4 (Figure 3). Additionally, the winter solar radiation (WSR) acquired by the six residential cluster layouts ranked from highest to lowest as EL4 > SL3 > HL6 > PRL1 > SRL2 > CL5 (Figure 3).
These findings highlight the importance of urban form in shaping the thermal energy demand of residential buildings at the neighborhood level [43,44]. The diverse heating energy consumption patterns observed among the six urban layouts indicated variations in building energy efficiency. Furthermore, the analysis revealed a consistent correlation between the winter building energy consumption, effective shading coefficient, and solar radiation indices for the six residential building group layouts. The decrease in energy consumption for building heating during winter in Wuhan can be attributed to the relatively stable wind and thermal conditions, which have a weaker impact on ventilation and heat dissipation within the building complex.

3.2. Analysis of the Relationship between the Layout of Residential Building Clusters and Building Carbon Emissions

According to Table 7, the primary factor influencing the carbon emissions of the residential building clusters is the electricity consumption for heating (ECFH) during the winter season. In the VirVil-HTB2 simulations, the other power consumption (OPC) was the same for all six layout forms at 55.73 kWh/m2·a and was therefore not considered in the analysis. Despite the increased energy consumption for cooling during summer compared with heating during winter for the six building groups (Table 4), the electricity consumption for heating in winter was significantly higher than the electricity consumption for cooling (ECFC) in summer. This is due to the variable climate and building cluster layout in Wuhan, which creates an unstable wind and thermal environment during summer, promoting ventilation and heat dissipation and reducing power consumption. Conversely, the changes in the climate during winter in the Wuhan region are less pronounced, resulting in a stable wind and thermal environment within the building cluster, characterized by lower external temperature and decreased wind speed variability. Based on the data provided in Table 7, the ranking of the six residential building group layouts in terms of carbon emissions, from highest to lowest, was as follows: CL5 > SRL2 > EL4 > PRL1 > HL6 > SL3.
The Type 5 courtyard layout had the highest heating power consumption and carbon emissions during winter, with values of 30.56 kWh/m2·a and 52.044 kgCO2/m2·a, respectively. This was due to the layout’s reduced solar radiation exposure and the presence of ventilation ducts between building courtyards, creating areas of high and low wind speed. As a result, the courtyard layout increases heat loss within the building, especially at colder winter temperatures, leading to higher heating power consumption and carbon emissions.
The Type 2 staggered row layout ranked second in terms of winter carbon emissions, with a heating power consumption rate of 30.29 kWh/m2·a and a carbon emission rate of 51.802 kgCO2/m2·a. Similar to the courtyard layout, the staggered layout receives less solar radiation and features more air ducts and areas of high and low wind pressure between the buildings. This unfavorable layout leads to less efficient heat retention during winter, resulting in increased heat loss, heating power consumption, and carbon emissions.
The Type 4 enclosure layout had the lowest winter heating power consumption and associated carbon emissions among the six layout forms, with values of 29.32 kWh/m2·a and 51.797 kgCO2/m2·a, respectively. However, the carbon emissions of the Type 4 layout ranked after the Type 5 courtyard layout and the Type 2 staggered row layout. This is because the Type 4 enclosure layout had the highest cooling power consumption in summer, reaching 13.48 kWh/m2·a. Although this layout reduces winter heating power consumption, it does not provide the same benefits for summer cooling power consumption. The Type 4 enclosure layout performs well in terms of summer cooling power consumption due to its higher solar radiation exposure during the summer season and its closed design, which creates a more stable wind–heat environment throughout the year, resulting in reduced heat loss. Consequently, the building requires less electricity for heating in winter but more for cooling in summer.
The heating power consumption of the Type 6 hybrid layout in winter was 30.07 kWh/m2·a, and its cooling power consumption in summer was 12.61 kWh/m2·a, resulting in a carbon emission rate of 51.734 kgCO2/m2·a, second only to the Type 3 scattered layout. The balanced solar radiation received by the building of the Type 6 layout in winter and summer, coupled with its hybrid layout characteristics, allows for a more balanced wind and heat environment to form in both seasons, leading to its relatively lower electricity consumption and carbon emissions.
In contrast, the Type 3 scattered layout exhibited a winter heating power consumption rate of 29.50 kWh/m2·a, second only to the Type 4 enclosure layout, and a summer cooling power consumption rate of 12.83 kWh/m2·a. Its summer cooling power consumption was slightly lower than that of the Type 4 enclosure layout due to its higher solar radiation in summer; the scattered layout’s total electricity consumption (TEC) was the lowest among the six types of layouts, leading to a lower carbon emission rate of 51.550 kgCO2/m2·a, making it a more desirable layout form.

3.3. Fitted Linear Regression Analysis

The analysis described in the previous paragraphs focused on the influence of direct solar radiation, effective shading coefficient, and the wind–heat environment on the energy consumption of residential building clusters. To establish fitting equations for the data, regression analysis was performed using SPSS 27 software for the six groups of residential building cluster layouts. The regression analysis was divided into two categories: the correlation and fitted regression analysis of direct solar radiation and summer cooling energy consumption and the correlation and fitted regression analysis of direct solar radiation and winter heating energy consumption. Additionally, correlation and linear regression analyses were conducted for the average energy consumption and average direct solar radiation.
A linear regression analysis was conducted to explore the relationship between summer solar radiation and summer cooling energy consumption based on the correlation analysis shown in Table 8. The results of this analysis revealed a significant correlation between the two variables, with a linear regression coefficient of P = 0.816 and a model significance index of Sig1 = 0.048 < 0.05. The coefficient of determination (R2) for the model fit was 0.715, indicating that the regression equation accurately captured 71.5% of the variation, whereas the remaining 28.5% was due to random factors. However, the results of the linear regression analysis showed that F = 7.976 and Sig2 = 0.48 > 0.05, indicating a failure to pass the significance test. This could be attributed to the complex wind–heat environment in the Wuhan area, which plays a large role in energy consumption.
The correlation analysis in Table 8 reveals a strong and significant relationship between winter solar radiation acquisition and winter heating energy consumption. The linear regression coefficient of P = −0.988 and the model significance index of Sig1 < 0.001 confirmed this relationship. A linear regression analysis was conducted to examine the relationship, resulting in a coefficient of determination (R2) of 0.977. This indicated that the regression equation accurately predicted 97.7% of the variation in heating energy consumption based on winter solar radiation acquisition, with the remaining 2.3% attributed to random factors. The significance level test yielded f = 169.611 and Sig2 < 0.001, demonstrating that the effective shading coefficient significantly affects winter solar radiation acquisition. The negative regression coefficient of −0.001 > 0 indicated a negative correlation between heating energy consumption in winter and winter solar radiation acquisition. As solar radiation acquisition increased, heating energy consumption decreased.
Therefore, the regression equation was obtained as follows:
Y = −0.001X + 43.486
where Y is the average heating operating energy, and X is the winter solar radiation.
The correlation analysis in Table 8 shows no significant correlation between the acquisition of average solar radiation and the average building energy consumption. This is supported by the linear regression coefficient of P = 0.637 and the model significance index of Sig1 = 0.174 > 0.05.

4. Discussion

4.1. Residential Cluster Layout Characteristics

The findings reveal that the staggered row layout (SRL2) is the most energy-efficient type among the six layout types analyzed in regions with hot summers and cold winters. This is followed by the parallel row layout (PRL1), whereas the enclosed layout (EL4) consumes the most energy. These findings suggest that the staggered row and parallel row layouts are advantageous for minimizing the overall energy consumption in residential buildings, whereas the enclosed layout is not recommended for energy conservation.
In terms of summer cooling energy consumption, the staggered row layout demonstrated the lowest average, followed by the parallel row layout, and the enclosed layout exhibited the highest levels of energy consumption. This is attributable to the large role played by the building complex in molding the microclimate environment within the community setting. Various factors, such as temperature, wind speed, relative humidity, and solar radiation, exert a pronounced impact on the energy requirements of a building. During the summer, the implementation of the parallel determinant layout and the staggered layout creates a wind tunnel effect that effectively mitigates the cooling energy consumption of the building [6,8,45]. Therefore, adopting the staggered row and parallel row layouts is recommended for reducing energy consumption in summer, whereas the enclosed layout is not suitable for energy conservation during this season.
The analysis showed that the enclosed layout (EL4) had the lowest average heating energy consumption in winter, followed by the scattered layout (SL3); the staggered row layout (SRL2) consumed the most energy. This phenomenon arises due to the influence of the prevailing wind direction in regions with hot summers and cold winters on the staggered and parallel determinant layouts. As a result, a wind tunnel effect easily forms, which contributes to a reduction in building cooling energy consumption during the summer season [45]. Therefore, adopting the enclosed and scattered layout forms is recommended for reducing heating energy consumption in winter, but the staggered row layout is unsuitable for energy conservation during this season.
Table 7 shows that winter heating electricity consumption is the main contributor to carbon emissions in residential complexes. Despite the increased summer cooling energy consumption, winter heating electricity consumption was significantly higher in the evaluated building complexes. Among the six layout forms, the courtyard layout (CL5) consumed the most winter heating electricity and contributed to the most carbon emissions, whereas the scattered layout (SL3) had the lowest total electricity consumption and carbon emissions. The enclosed layout (EL4) had the lowest heating energy consumption in winter but the highest cooling energy consumption in summer. The hybrid layout (HL6) provided a balanced wind and thermal environment in both seasons, resulting in lower electricity consumption and carbon emissions. In conclusion, winter heating electricity consumption is the primary factor affecting the carbon emissions of residential complexes.
These findings suggest that, in regions with hot summers and cold winters, the scattered layout (SL3) is the most effective in reducing the carbon emissions of buildings, followed by the hybrid layout (HL6); however, the courtyard layout (CL5) is unsuitable for carbon emission reduction. Therefore, in these regions, adopting the scattered or hybrid layout is recommended to achieve energy-saving and emission-reduction goals for residential buildings. Overall, the arrangement of a building complex is shaped by environmental factors and its positioning, and it plays a pivotal role in impacting the energy efficiency of a building [7].

4.2. The Relationship between the Effective Shading Coefficient and Solar Radiation

This study examined the influence of effective shading coefficients resulting from different layouts on the solar radiation and thermal performance of residential buildings. The effective shading coefficient significantly impacts solar radiation acquisition during summer, but in the Wuhan region, other factors such as the complex wind–heat environment also affect energy consumption. The enclosed layout (EL4) had the lowest effective shading coefficient but high cooling energy consumption and solar radiation index in summer. The parallel row layout (PRL1) had a higher effective shading coefficient and a slightly higher cooling energy consumption than the staggered row layout (SRL2). The shading coefficient serves as a measurable metric that can be used to evaluate the shading relationship among building clusters, and the external shading effects from neighboring buildings can strongly impact the thermal performance of buildings, particularly in regions with hot summers and cold winters [42]. Moreover, shading, although an important factor influencing solar radiation and energy usage, is not the sole determinant of the thermal performance of buildings [9].
The staggered row layout (SRL2) had a lower effective shading coefficient than the parallel row layout, but its solar radiation was lower. This is because the average effective shading coefficient does not fully capture the shading performance of individual buildings. Shading is important for solar radiation and energy use, but it is not the sole determinant.
The courtyard layout (CL5) exhibited wider variation in solar radiation and summer effective shading coefficients. Despite not having the highest effective shading factor, CL5 had the lowest solar gain among the considered layout types. This is due to the common wall structures between buildings, which reduce the available wall area for solar radiation. The unique internal wind–heat environment of the courtyard layout (CL5) in regions with hot summers and cold winters contributed to its higher average cooling energy consumption.
The hybrid layout (HL6) also showed consistent energy performance trends in summer. These results indicate that the hybrid layout has a relatively straightforward effect on building energy consumption.

5. Conclusions

  • This study ignored the influence of various factors, including vegetation cover, water bodies, roads, and buildings, which have diverse characteristics, on the surrounding residential complexes. Additionally, we did not consider the impact of indicators such as building spacing and orientation on building energy consumption and carbon emission intensity. We observed that, in a region with hot summers and cold winters in China, the closed residential cluster layout (EL4) exhibited relatively lower energy efficiency, whereas the staggered layout demonstrated comparatively reduced average energy consumption. Incorporating a staggered layout in building design showed the potential to achieve a maximum energy consumption reduction of 2.23%.
  • The findings demonstrate a substantial correlation between the effective shading coefficient during summer and the incident solar radiation on buildings located in regions characterized by hot summers and cold winters. Nonetheless, it should be noted that the observed trends do not exhibit complete concurrence. Moreover, it is essential to acknowledge that cooling energy consumption is influenced by multiple factors beyond the solar radiation influx. It is noteworthy that the staggered row layout (SRL2) demonstrated a more energy-efficient configuration regarding cooling energy consumption, manifesting lower cooling energy requirements, while the enclosed layout (EL4) exhibited elevated cooling energy demands. Employing cross-layout optimization could substantially mitigate cooling energy consumption by as much as 7.17%.
  • The effective shading coefficient of the building complex in winter had a significant impact on the solar radiation received by the buildings, which in turn had a significant influence on the thermal performance of the buildings. These indicators were found to have a good linear relationship. Concurrently, solar radiation is a significant determinant of building energy consumption, particularly during winter when the accessibility of buildings to solar radiation is a pivotal determinant of their energy consumption. This finding holds vital implications for mitigating the energy consumption of buildings during the winter season. Meanwhile, it was observed that the implementation of an enclosed layout (EL4) proved more efficacious in diminishing heating energy consumption, whereas adopting a courtyard layout (CL5) led to higher heating energy requirements. By optimizing the layout and incorporating the enclosed design, a noteworthy reduction in heating energy consumption of up to 4.06% could be attained.
  • In a comprehensive study evaluating the carbon emission intensity of various residential building complex layouts in China’s hot summer and cold winter regions, a primary focus was placed on the impact of different types of electricity consumption. The results revealed that the main variables influencing the variation in carbon emissions were related to heating and cooling electricity consumption. Among these variables, heating electricity consumption exhibited an especially pronounced effect on building carbon emissions. Our investigation reveals that the scattered layout (SL3) significantly contributes to the mitigation of building-related carbon emissions, while the courtyard layout (CL5) exerts a detrimental effect on carbon emission reduction, exhibiting a decrease of 0.95% in comparison to the two aforementioned layout forms.
  • This study provides an academic reference for energy savings and carbon reduction in regions with hot summers and cold winters in China. Simultaneously, the layout configuration of residential buildings contributes to variations in solar radiation exposure for individual buildings and the corresponding levels of energy consumption. Conducting further investigations into these related issues will play a critical role in understanding the underlying principles and characteristics that shape the impact of building cluster layouts on energy consumption and carbon emissions. Moreover, the examination of residential building complexes situated in regions characterized by hot summers and cold winters has crucial implications in addressing global environmental shifts, as well as aiding other parts of the world in attaining their energy conservation and carbon reduction objectives through the judicious optimization of building complex layouts.

Author Contributions

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

Funding

This study was supported by the Humanities and Social Science Research Project of the Ministry of Education of China, grant number 22YJAZH146; the National Natural Science Foundation of China, grant number 51508169; and the Local Cooperative Project of the China Scholarship Council, grant number 202008420322.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

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Sustainability 15 11915 g001
Figure 1. Comparative chart of annual average indicators.
Figure 1. Comparative chart of annual average indicators.
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Sustainability 15 11915 g002
Figure 2. Comparison of summer average indicators.
Figure 2. Comparison of summer average indicators.
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Sustainability 15 11915 g003
Figure 3. Comparison of winter average indicators.
Figure 3. Comparison of winter average indicators.
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Table 1. Illustrative layout form of Wuhan settlement building groups.
Table 1. Illustrative layout form of Wuhan settlement building groups.
Layout TypeLayout Type Illustration
Parallel Row LayoutSustainability 15 11915 i001Sustainability 15 11915 i002Sustainability 15 11915 i003Sustainability 15 11915 i004Sustainability 15 11915 i005
Staggered Row LayoutSustainability 15 11915 i006Sustainability 15 11915 i007Sustainability 15 11915 i008
Scattered LayoutSustainability 15 11915 i009Sustainability 15 11915 i010Sustainability 15 11915 i011Sustainability 15 11915 i012
Enclosed LayoutSustainability 15 11915 i013Sustainability 15 11915 i014Sustainability 15 11915 i015Sustainability 15 11915 i016Sustainability 15 11915 i017
Courtyard LayoutSustainability 15 11915 i018Sustainability 15 11915 i019Sustainability 15 11915 i020Sustainability 15 11915 i021
Hybrid LayoutSustainability 15 11915 i022Sustainability 15 11915 i023Sustainability 15 11915 i024Sustainability 15 11915 i025
Table 2. Typical residential cluster building group layout form model illustration table.
Table 2. Typical residential cluster building group layout form model illustration table.
Layout FormTypical Model DiagramLayout FormTypical Model Diagram
PRL1Sustainability 15 11915 i026Sustainability 15 11915 i027SRL2Sustainability 15 11915 i028Sustainability 15 11915 i029
SL3Sustainability 15 11915 i030Sustainability 15 11915 i031EL4Sustainability 15 11915 i032Sustainability 15 11915 i033
CL5Sustainability 15 11915 i034Sustainability 15 11915 i035HL6Sustainability 15 11915 i036Sustainability 15 11915 i037
Table 3. Model construction parameters.
Table 3. Model construction parameters.
Building TypeStructure TypeHeat Transfer
Coefficient (W/m2·C)		MaterialThickness (mm)
Residential buildingRoofing0.4Thick floor tiles10
Dry hardening cement mortar25
Cement mortar20
Cement hydrophobic expanded perlite20
XPS Plate75
Reinforced concrete120
Cement lime mortar5
Cement lime mortar5
Exterior wall0.74Dry powder polymer cement waterproof mortar5
M5.0 ready-mixed plastering mortar15
Aerated concrete block wall200
Foam concrete inorganic insulation board50
Ground1.98Composite wood2
Polyethylene foam pad6
Polyethylene foam cushion50
Reinforced concrete100
Cement mortar5
Lime mortar5
Window3.0Outer glass6
Air interlayer12
Inner glass6
Table 4. Regional grid carbon emission factors.
Table 4. Regional grid carbon emission factors.
Grid NameCarbon Emission Factors (tCO2/mWh)
North China regional grid0.8843
Northeast regional grid0.7769
East China regional grid0.7035
Central China regional grid0.5257
Northwest regional grid0.6671
Southern regional grid0.5271
Table 5. Simulated energy consumption indicators for residential cluster layout.
Table 5. Simulated energy consumption indicators for residential cluster layout.
Layout FormAOEC (kWh/m2·a)ACEC (kWh/m2·a)AHEC (kWh/m2·a)
PRL1126.8950.1230.26
SRL2126.8650.0730.29
SL3127.3351.3329.5
EL4129.7653.9429.32
CL5127.9250.8630.56
HL6127.0250.4530.07
Table 6. Simulated shading coefficient and solar radiation for residential cluster layouts.
Table 6. Simulated shading coefficient and solar radiation for residential cluster layouts.
Layout FormAESCSESCWESCASR (kWh/m2·a)SSR (kWh/m2·a)WSR (kWh/m2·a)
PRL18.90%8.99%8.52%36,867.67030,449.24311,850.920
SRL28.94%9.10%8.51%36,566.35630,232.37111,637.321
SL39.10%9.27%8.64%38,688.12231,918.40712,424.619
EL49.37%9.51%8.98%39,294.01232,392.16712,609.111
CL58.88%9.01%8.50%36,161.85429,873.76511,552.148
HL69.04%9.20%8.62%37,233.82430,761.25611,906.991
Table 7. Electricity and carbon emissions of residential cluster layout.
Table 7. Electricity and carbon emissions of residential cluster layout.
Layout FormECFH (kWh/m2·a)ECFC (kWh/m2·a)OPC (kWh/m2·a)TEC (kWh/m2·a)CE (kgCO2/m2·a)
PRL130.2612.5355.7398.5251.792
SRL230.2912.5255.7398.5451.802
SL329.5012.8355.7398.0651.550
EL429.3213.4855.7398.5351.797
CL530.5612.7155.7399.0052.044
HL630.0712.6155.7398.4151.734
Table 8. Simulated effective shading coefficient and solar radiation of residential cluster layouts.
Table 8. Simulated effective shading coefficient and solar radiation of residential cluster layouts.
Fitting Regression Analysis NameRR2FSig2PSig1
Solar radiation and cooling energy consumption in summer0.8460.7157.9760.480.816 *0.048
Solar radiation and heating energy consumption in winter0.9880.977169.611<0.001−0.988 *<0.001
Average solar radiation and average building energy consumption----0.6370.174
Sig1: Correlation analysis significance index. Sig2: Linear regression analysis significance index. * Correlation is significant at the 0.05 level (two-tailed).
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Yan, J.; Zhang, H.; Liu, X.; Ning, L.; Hien, W.N. The Impact of Residential Cluster Layout on Building Energy Consumption and Carbon Emissions in Regions with Hot Summers and Cold Winters in China. Sustainability 2023, 15, 11915. https://doi.org/10.3390/su151511915

AMA Style

Yan J, Zhang H, Liu X, Ning L, Hien WN. The Impact of Residential Cluster Layout on Building Energy Consumption and Carbon Emissions in Regions with Hot Summers and Cold Winters in China. Sustainability. 2023; 15(15):11915. https://doi.org/10.3390/su151511915

Chicago/Turabian Style

Yan, Junle, Hui Zhang, Xiaoxin Liu, Ling Ning, and Wong Nyuk Hien. 2023. "The Impact of Residential Cluster Layout on Building Energy Consumption and Carbon Emissions in Regions with Hot Summers and Cold Winters in China" Sustainability 15, no. 15: 11915. https://doi.org/10.3390/su151511915

APA Style

Yan, J., Zhang, H., Liu, X., Ning, L., & Hien, W. N. (2023). The Impact of Residential Cluster Layout on Building Energy Consumption and Carbon Emissions in Regions with Hot Summers and Cold Winters in China. Sustainability, 15(15), 11915. https://doi.org/10.3390/su151511915

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