Effect of Neighborhood Cluster Morphology on Energy Efficiency and Decarbonization in Regions of China with Hot Summers and Cold Winters

Abstract

Global climate change has brought issues of pollution and environmental protection to the forefront of public attention. The energy consumption and carbon emissions of buildings have become critical issues in energy conservation and emission reduction, and are important for environmental protection. This article focuses on typical residential buildings in Wuhan, a representative of regions with hot summers and cold winters, to study the impacts of different layout design parameters on energy consumption and carbon emission intensity of building complexes. VirVil-HTB2 was used for modeling and simulating building complex layouts, while SPSS was used for data analysis. This study shows that solar radiation is an important indicator for predicting building energy efficiency, directly affecting energy consumption and carbon emissions. We also examined the impact of building orientation, building spacing, staggered spacing, and the layout of open spaces between buildings on heating energy consumption, cooling energy consumption, and carbon emissions. Building spacing was positively correlated with cooling energy consumption and negatively correlated with heating energy consumption and carbon emissions. The effect of staggered spacing on energy consumption is greater in the south–north direction than in the west–east direction. Additionally, setting the building orientation to 135° results in the lowest carbon emissions. Under the idealized simulation conditions of this study, the west–east dispersed open-space layout is a preferable configuration for reducing carbon emissions from residential neighborhood buildings. This study explores the impact of layout design parameters on energy consumption and carbon emissions of building complexes in hot summer and cold winter regions, providing references for energy optimization and environmental sustainability research.

1. Introduction

As global climate change intensifies and the demand for sustainable development grows, energy conservation and carbon reduction have become critical issues of widespread international concern. For a major developing country like China, promoting energy efficiency and achieving decarbonization are not only urgent imperatives in addressing climate change but also key steps toward a green transition during its economic and social development. Data from the International Energy Agency (IEA) indicates that in 2024, the building sector accounted for approximately 30% of global energy demand, with residential buildings contributing about 70% of the sector’s total energy consumption [1]. Consequently, advancing energy conservation and emissions reduction in residential buildings in the building sector is important for improving overall energy efficiency.

The operational energy consumption of buildings is primarily driven by cooling and heating demands. In hot summer and cold winter regions, these two factors collectively account for over 50% of total operational energy consumption [2]. Urban areas in these regions cover a vast floor area, representing approximately one-third of the nation’s total urban floor space. In these regions, the combined energy consumption for air conditioning in summer and decentralized heating in winter already constitutes a significant proportion of China’s total building operational energy consumption and continues to grow. China’s hot summer and cold winter regions experience high temperatures and humidity in summer, followed by cold and damp conditions in winter. They exhibit substantial seasonal temperature variations and pronounced diurnal temperature fluctuations. This climate pattern imposes high cooling loads on buildings during summer and significant heating demands in winter, resulting in pronounced seasonal fluctuations in energy consumption. Consequently, overall building energy consumption remains high, leading to substantial carbon emissions.

Research on building energy efficiency and carbon emissions at the residential district level involves a complex system with multiple levels and variables. Factors at the planning level, such as building layout morphology, open space configuration, and greening systems, intertwine with factors at the building level, including envelope performance, equipment efficiency, and occupant behavior patterns, collectively influencing the energy consumption levels of residential communities [3,4,5]. Within this complex system, building layout emerges as a critical design dimension affecting energy performance. It serves not only as a core determinant of urban energy consumption but also exerts a significant influence on both the internal and external urban climate [6,7,8,9,10]. Microclimate conditions markedly impact building energy performance, bioclimatic design, and engineering applications [11,12,13,14,15]. The dynamic interactions between buildings and their shading effects substantially alter the thermal performance of residential structures, thereby influencing energy consumption and carbon emission patterns [16,17,18,19,20]. Deng Qingtan et al. demonstrated that design parameters such as building spacing and orientation profoundly affect heating energy consumption, particularly in cold regions, where rational layouts and building orientations can significantly reduce heating energy demands [21]. Yang Yingbao et al.’s study in Nanjing examined the effects of building height, density, layout, and greening on the thermal environment of residential buildings, concluding that appropriate urban planning can effectively improve the thermal environment of residential clusters and enhance energy efficiency [22]. Uddin and Khanna identified key design parameters affecting energy use across climate zones and built a high-precision model [23]. Nazari and Jafari found that a 1:4 aspect ratio with west–east orientation achieves the lowest energy consumption in a temperate humid climate [24].

Among the many planning factors, building spacing, staggered spacing, orientation, and the layout of open spaces offer a high degree of controllability in planning and design, making them core variables that can be actively optimized during the design phase. At the same time, building energy consumption in regions with hot summers and cold winters is highly sensitive to solar heat gain and natural ventilation efficiency. Building spacing, orientation, staggered arrangements, and the layout of open spaces are the key spatial variables that regulate these physical processes. By influencing building shading effects, wind environment organization, and heat island intensity, they directly affect heating and cooling loads [25,26]. Furthermore, these four elements do not act independently but are interconnected through synergistic or constraining relationships. Local optimization of a single element may not necessarily achieve overall energy-saving benefits; therefore, systematic analysis must be conducted within a multi-element coupling framework. However, previous studies have largely focused on isolated analyses of single or a few factors, or have been conducted in regions with either cold or hot climates. There remains a lack of systematic, quantitative research on key design elements at the planning level for regions with hot summers and cold winters—areas that have dual demands for both cooling and heating. Based on this understanding, this study builds upon existing research to further investigate the impact of these four layout elements on energy consumption under hot summer and cold winter climatic conditions.

This paper consists of five sections. Section 1 begins by outlining the significance of urban and residential planning on building energy consumption in hot summer and cold winter regions. Section 2 outlines the study area and methodology, classifies residential clusters in Wuhan, and identifies representative clusters. Section 3 employs VirVil-HTB2 software for simulation, discussing building energy consumption and solar radiation exhibited by clusters with different layout patterns. Section 4 presents parametric response trend analysis using SPSS 27 software, a critical step in elucidating experimental findings. Section 5 discusses the impact of residential cluster planning on building energy consumption, proposes recommendations for residential planning in hot summer and cold winter regions, and summarizes the key research findings and implications. This study fills a research gap regarding the influence of building layout on energy consumption and carbon emissions in hot summer and cold winter regions, providing a scientific basis for architectural design in these areas to enhance energy efficiency and promote sustainable development.

2. Materials and Methods

2.1. Research Framework and Methods

We first employed statistical methods to describe the layout of residential communities in Wuhan based on empirical data, before constructing an idealized model for these communities. Using the VirVil-HTB2 tool (Welsh School of Architecture, Cardiff University, Cardiff, UK), we conducted simulation studies on the cooling energy consumption and solar radiation of these buildings to evaluate their cooling energy demands and exposure to solar radiation. As shown in Figure 1, we first obtained climate data for the Wuhan region from the Energy Plus website (https://energyplus.net/). This dataset is based on the typical meteorological year file for the Wuhan region from the Chinese Standard Weather Year (CSWD) database. It contains hourly meteorological parameters (dry-bulb temperature, wet-bulb temperature, solar radiation, wind speed, etc.) for all 8760 h of the year. The simulation period covers the entire year (1 January to 31 December), with a time step of 1 h. Subsequently, we imported this data into the VirVil-HTB2 software. Within this software, we specified the geographic coordinates of the model, configured climate parameters, and provided the necessary material properties through a programming file. We used these input parameters to assess solar radiation levels, cooling and heating energy consumption, and carbon emissions within residential areas.

In the building layout optimization study, we carried out a systematic parametric simulation analysis for two typical layout patterns: parallel and staggered arrangements. For parallel row layouts, we employed a controlled variable method to sequentially expand building spacing in both west–east and south–north directions while simultaneously monitoring changes in building energy intensity. For staggered parallel layouts, we implemented offset adjustments of two units in the west–east direction and four units in the south–north direction, quantitatively assessing their carbon emission intensity responses. Specifically, the west–east spacing is denoted as W-E BD, the two-unit staggered spacing in the west–east direction as W-E BSD, the south–north spacing as S-N BD, and the four-unit staggered spacing in the south–north direction as S-N BSD. Furthermore, a parametric control model (see

Table 1. Model simulation illustration 1.

Method Category	Graphical Symbol
W-E BD S-N BD
W-E BSD
S-N BSD
BO

for details) was established to explore the synergistic regulation mechanism between building orientation and spacing. While maintaining the baseline spacing between Rows A and D, a 5 m gradient expansion was applied to Columns 1–4 to reveal the radiation absorption gradient variation along the west–east axis. Then, with Column 1–4 spacing fixed, a 5 m incremental expansion was applied to Rows A-D to analyze the coupled effects of south–north axis expansion on solar radiation distribution and cooling load.

In terms of dynamic layout regulation, we propose a new strategy: keep the A and C benchmark rows, move the B and D rows east by 5 m, and establish a west–east axial misalignment model; keep the benchmark Columns 1 and 3, and move Columns 2 and 4 north by 5 m, and establish a south–north axial misalignment model, which reveals the microclimate regulation mechanism of the building’s shadowing interaction effect in a two-dimensional way. Finally, a 15° rotational gradient algorithm is introduced to systematically decipher the quantitative relationship between the building rotation angle and the solar radiation reception efficiency and cooling energy consumption under the premise of maintaining the parallel rows of the benchmark layout. This method establishes a mapping relationship between building layout parameters, microclimate responses, and energy performance, enabling us to investigate the impact of building orientation on solar radiation and cooling energy consumption.

Building upon the optimization of physical building layouts, this study further incorporates the open spaces enclosed by buildings as an active design variable. Under the condition that the volume ratio and building coverage of all open space types remain constant, the study investigates the coupled influence mechanism of their layout characteristics on site microclimate and energy consumption. To systematically examine the effects of open space layout, we propose a two-dimensional classification framework: the spatial form dimension is divided into three categories—concentrated, dispersed, and mixed—while the orientation dimension is divided into two categories—north–south and east–west. The intersection of these two dimensions forms a complete 3 × 2 classification matrix, resulting in six typical open space types (C-SN, C-WE, D-SN, D-WE, M-SN, M-WE; see

Table 2. Six typical layout patterns of open spaces.

Method Category	Graphical Symbol
C-SN
C-WE
D-SN
D-WE
M-SN
M-WE

The yellow graphics in Table 2 indicate open spaces in different categories.

for specific parameters). This classification framework has been validated in previous research. In their typological study of residential areas in Delhi, Marwal and Silva employed the k-means clustering algorithm to identify six residential typologies, including high-density compact, medium-density dispersed, and mixed-use types [27]. Zheng et al., focusing on Shanghai, treated the mixed-use type as a distinct category [28]. Köberl et al., in their study of large-scale residential areas in Germany, identified three planning paradigms: structural low-density, density-oriented urban, and socialist urban [29], corresponding to dispersed, concentrated, and mixed layouts, respectively. In terms of orientation, this study categorized sites into north–south and east–west orientations, representing the two typical orientations that influence solar heat gain and natural ventilation efficiency in building layouts within regions characterized by hot summers and cold winters. By systematically comparing the differences in building energy consumption and carbon emission intensity among these six spatial configurations, this study reveals the regulatory mechanisms through which open space organization influences microclimate and energy consumption.

2.2. Study Area and Climate Context

In China, regions characterized by hot summers and cold winters are extremely widespread, extensively covering the middle and lower reaches of the Yangtze River and surrounding areas. This region exhibits pronounced seasonal dual-peak load characteristics, featuring high temperatures and humidity in summer, cold and damp conditions in winter, and relatively high annual average humidity. The climate of these regions imposes high loads on buildings for both summer cooling and winter heating, resulting in pronounced seasonal fluctuations in operational energy consumption. Overall energy use and carbon emissions intensity remain at elevated levels. Therefore, building energy conservation in these areas must holistically address summer insulation, ventilation, and winter thermal retention needs. Studying their energy consumption patterns and emission reduction pathways is crucial for advancing energy efficiency and low-carbon transformation in the building sector.

Located in the middle reaches of the Yangtze River, Wuhan is an example of a typical region characterized by hot, humid summers and brief, cold winters. Its climate is representative of the broader region. As a densely populated central city with high building density, Wuhan faces significant energy consumption and carbon reduction pressures. This is particularly pronounced amid ongoing urbanization and deepening green building policies, making its building energy efficiency and low-carbon development needs increasingly critical. Moreover, Wuhan’s diverse building types and varied neighborhood configurations provide ample real-world samples for studying the relationship between neighborhood form and energy consumption/carbon emissions. Consequently, Wuhan emerges as an ideal research site for examining how neighborhood groupings influence energy efficiency and decarbonization.

2.3. Selection of Sample Residential Clusters

We employed multiple research methodologies to conduct an in-depth and comprehensive analysis of the layout characteristics of residential communities in Wuhan. The research design thoroughly integrated the existing academic literature and employed widely used computer software such as Google Maps (Google LLC, Mountain View, CA, USA), OpenStreetMap, and Geographic Information Systems (GIS) to systematically map and understand the distribution of residential communities from a macro perspective. Additionally, to more precisely grasp the layout features of residential building zones, detailed investigations were conducted on building forms. We examined a total of 156 residential communities across four administrative districts in Wuhan: Jianghan, Jiang’an, Wuchang, and Hongshan. Based on the scale requirements outlined in the “Standards for the Construction of Complete Residential Communities” (DB42/T 2277-2024) [30], and taking into account the range of construction dates (from the 1990s to the 2020s), 24 residential communities were selected as a sample for in-depth investigation and analysis.

Table 3. Summary of characteristics of selected residential communities.

Layout Type	Completion Year	District	Community Name	Land Area (hm2)
Parallel Layout	1992	Jiang’an District	Yiyuan Road Community	0.97
	2004	Hongshan District	Lishang Renjia Community	1.33
	2006	Jianghan District	Xiushui Jiayuan Community	2.20
	2013	Hongshan District	Baoli Xinyu Community	7.33
	2019	Jianghan District	Binjiang Jinmao Community	5.56
Enclosed Layout	1996	Jiang’an District	Anjuyuan A Community	2.80
	2002	Jianghan District	Hanxi Yingyuan Community	3.43
	2008	Jiang’an District	Baibu Yating Community	6.30
	2010	Jiang’an District	Jinqiao Tingyuan Community	8.80
	2018	Hongshan District	Xingyue Bay Community	8.50
	2024	Jianghan District	Wuhan Urban Construction Shaoxing Li 1921 Community	3.05
Staggered Layout	2008	Jiang’an District	Hankou Huayuan Phase 1 Community	3.30
	2015	Hongshan District	Lidao 2046 Community	3.50
	2015	Hongshan District	Liantou Yuyuan Community	2.96
	2022	Hongshan District	CSCEC Donghu Star Community	3.30
Scattered Layout	1995	Jiang’an District	Caijiatian Community	5.80
	2010	Hongshan District	Lidao Mancheng Community	11.30
	2010	Hongshan District	Chengkai Qingling Urban Garden Community	17.00
	2020	Jiang’an District	Jiayuan Community	1.25
Hybrid Layout	2000	Hongshan District	Jianxin Community	0.71
	2000	Jianghan District	Jianghua Yuan Community	1.29
	2008	Jiang’an District	Huaqing Yuan Community	3.12
	2010	Jianghan District	Sanjin Huadu Community	4.70
	2020	Jianghan District	Jiaxing Yuan Community	1.20

presents the features of the 24 residential communities. The results indicate that over 30% of the sample communities adopted a layout pattern featuring east–west and north–south parallel axes. Although this proportion does not constitute an absolute majority, it represents the highest share among all layout types. This finding is consistent with existing research [4,31], and this layout form is highly representative of residential community layouts in Wuhan. Accordingly, this study establishes the parallel layout as the baseline model.

The energy performance of building complexes is largely constrained by their layout patterns, which can be comprehensively characterized by a series of parameters, including building height, density, spacing, orientation, and open space. We focused on four core morphological elements—building spacing, staggered building layout, building orientation, and open space—and explored how they influence building energy consumption in regions with hot summers and cold winters. To ensure the precision and relevance of findings, we employed strict variable control in the experimental design. By eliminating potential confounding factors such as landscape design, road planning, and building equipment, the scope was precisely confined to target elements, effectively preventing interference from extraneous variables. Concurrently, parameters like building density, floor area ratio, and building type were maintained at uniform values, enabling the use of an idealized model to simulate building energy consumption [32]. Building upon this foundation, we further employed a standardized, unified parameter system encompassing building density, floor area ratio, and building type. This ensured the reliability and reproducibility of the research data.

Due to significant climatic variations across different regions, the spatial structure of communities, as a key geographical feature, has a significant and non-negligible impact on building energy intensity. Therefore, subsequent research analyses will thoroughly examine the interaction between regional climate conditions and neighborhood spatial structures. This will deepen our understanding of factors affecting building energy consumption, providing more precise and scientifically grounded theoretical guidance and practical recommendations for optimizing building design and reducing energy consumption.

2.4. Simulation and Parameter Settings

VirVil-HTB2 is a dynamic energy simulation plugin operating on the widely used architectural design tool SketchUp 2018 platform, enabling building energy consumption simulation at the urban scale [33,34,35]. The accuracy and reliability of its simulation results have been validated by experts in the field [10,36,37,38]. The software’s layer file contains all the settings required to run the model, including material thermal properties, building envelope construction, thermal zone connections, and internal control parameters. During simulation, the software reads this input file to perform a dynamic thermal simulation on an hourly basis throughout the year.

In the energy consumption and carbon emissions simulations, internal control parameters were configured in accordance with relevant codes and standards, including the “Energy-Efficient Design Standards for Residential Buildings in Regions with Hot Summers and Cold Winters” (JGJ134-2010) [39] and the “Energy-Efficient Design Standards for Low-Energy-Consumption Residential Buildings—Hubei Provincial Local Standard” (DB42/T559-2013) [40]. Specifically, the HVAC setpoints were set to an indoor temperature of 26 °C in summer and 18 °C in winter. The operating schedule was set to 00:00–08:00 and 18:00–24:00 on weekdays, and 24 h a day on weekends. The air change rate was set to 0.5 times per hour in accordance with the requirements of the relevant standards [41], and was combined with a mechanical ventilation rate of 1.0 times per hour and a natural ventilation control threshold of 26 °C. The operating schedule aligns with that of the HVAC system. These parameters collectively form the basis for the thermostats and control strategies in the building operation simulation, ensuring that the simulation results accurately reflect energy consumption and carbon emissions under actual usage conditions.

The heating system was modeled using resistive heating, with a coefficient of performance (COP) set at 1.0; therefore, the heating energy consumption (HEC) is equivalent to the heating electricity consumption (HPC). The cooling system employs an electrically driven heat pump or air conditioning equipment. In accordance with the Class 1 energy efficiency standard specified in the “Energy Efficiency Limits and Grades for Room Air Conditioners” (GB21455-2019) [42], the coefficient of performance (COP) was set at 4.0, meaning the ratio of cooling energy consumption (CEC) to cooling electricity consumption (CPC) is 4:1. This COP setting remained consistent across all parameterized simulations.

VirVil-HTB2 calculates solar radiation incident on external surfaces by computing direct, diffuse, and global solar radiation data. Integrated with SketchUp 2018 software (Trimble Inc., Sunnyvale, CA, USA), this plugin precisely simulates and calculates solar radiation reception for each building facade. This study only considers the layout of building complexes as a variable, disregarding factors such as vegetation, roads, and building equipment. Consequently, mutual shading between buildings becomes the sole factor influencing solar radiation reception, with higher reception values indicating less shading from adjacent structures. This metric is therefore employed to quantify shading conditions between buildings.

In recent years, linear regression analysis has become a valuable tool in studies related to building energy consumption analysis [43,44]. However, given our limited sample size, we adopt a parametric trend analysis approach rather than relying on conventional regression inference. Therefore, we conducted parametric trend analysis to quantitatively analyze the simulated data using SPSS 27 (IBM Corp., Armonk, NY, USA). Initially, we ascertained whether a correlation exists between solar radiation values, indicative of inter-building shading, and building energy consumption. We then conducted parametric trend analysis to quantify the modeled relationship based on this correlation.

We used a single building of the same size and with the same basal area as the residential building for data comparison and variable control. In order to ensure the consistency of the building density, volume ratio, and bulk coefficient, the parameters required for the model were developed based on a number of relevant specifications, such as the “Energy Conservation Design Standard for Civil Buildings in Hot Summer and Cold Winter Regions” (JGJ134-2010) [39]; the “Standards for the Design of Low-Energy Residential Buildings (DB42/T559-2022)” [45]; “Code for Thermal Design of Civil Buildings (GB 50176-2016)” [46]; and “Standards for Planning and Design of Urban Residential Areas (GB 50180-2018)” [47]. Specifically, the building has a floor area of 40 m by 10 m, while the height of the building is 30 m across ten floors. The variation in cooling energy consumption and solar radiation among different residential building clusters was simulated by varying the building spacing, staggered layout, and orientation. The roof, wall, and window specifications are given in

Table 4. Parameter table.

Parameter Category	Limit Value
Building size	40 m × 10 m × 30 m
Shape coefficient	≤0.35
Roof heat transfer coefficient (W/m2/K)	≤0.50
Heat transfer coefficient of the wall (W/m2/K)	≤1.20
Window thermal coefficient (W/m2/K)	≤3.20
Area ratio of window to wall (South)	≤0.35
Area ratio of window to wall (East, West, North)	≤0.30

, and detailed material parameters are given in

Table 5. Material parameter setting table.

Serial Number	Materials	Heat Transfer Coefficient (W/m/°C)	Density (kg/m3)	Specific Heat Capacity (J/kg/°C)
1	Thick ceramic tile	0.760	1700	1050
2	Dry hardening cement mortar	0.930	1800	1050
3	Cement mortar	0.930	1800	1050
4	Cement hydrophobic expanded perlite	0.260	800	1170
5	XPS plate	0.030	40	1380
6	Reinforced concrete	1.740	2500	920
7	Cement lime mortar	0.870	1700	1050
8	Dry powder polymer cement waterproof mortar	0.930	1800	1050
9	M5.0 ready-mixed plastering mortar	0.870	1700	1050
10	Aerated concrete block wall	0.180	700	1050
11	Foam concrete inorganic insulation board	0.055	400	1400
12	Composite plank	0.170	600	2510
13	Polyethylene foam cushion	0.047	100	1380
14	Ground thermal insulation of all-light concrete building	0.140	500	1050
15	Lime mortar	0.810	1600	1050
16	Outer glass	0.760	2500	840
17	Air interlayer	0.023	1.29	1004

. Subsequently, these parameter configurations were passed to the model through the base layer file.

2.5. Carbon Accounting Methods

Carbon emissions during the building operation phase are calculated using the emission factor method, which is widely applied and technically mature in the field of building carbon emissions research [48,49]. In accordance with the “Standard for Calculation of Building Carbon Emissions” (GB/T 51366-2019) [50], the calculation formula is as follows:

$$C_M={\sum }_{i=1}^n{E}_i\times E{F}_i$$

(1)

where C_M is the annual carbon emission per unit floor area in the operation phase of the building (kgCO₂/m²·a); E_i is the annual consumption per unit area of energy of the ith category of the building (unit/m²·a); and EF_i is the carbon emission factor of energy type i (tCO₂/mWh).

In the simulation setup, building operational energy consumption was primarily represented by electricity consumption. Since Wuhan is located in the Yangtze River basin and is not part of a centralized heating planning area, winter heating also relies on electrical equipment [51]. At the same time, other forms of electricity consumption (such as lighting and electrical equipment), excluding cooling and heating, remain consistent across all simulation scenarios, ensuring that it does not affect cross-comparisons between layouts. Based on these two points, we treated the electricity consumption generated by cooling and heating as the core variable for carbon emission intensity analysis. The calculation formula can be simplified as follows:

$$C_M=E\times EF$$

(2)

where C_M 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).

The carbon emission factor for electricity was taken from the annual average value for the Central China power grid specified in the “Standard for Calculation of Carbon Emissions from Buildings” (GB/T 51366-2019) [50], which is 0.5257 kgCO₂/kWh. Given that this study aims to compare the relative differences in carbon emissions among different residential layout configurations, all simulation scenarios employed the same constant factor, without incorporating dynamic variations at the hourly or seasonal levels. This approach ensures consistency in the comparative results regarding the impact of layout variables on carbon emissions, in accordance with the basic requirements for carbon emission accounting during the building operation phase as specified in the standard.

3. Results

3.1. Analysis of the Impact Characteristics of Building Spacing on Solar Radiation, Operational Energy Consumption, and Carbon Emissions

We simulated the impact of increasing building spacing on the carbon emissions (CE) of the residential building complex using the VirVil-HTB2 tool. As shown in Figure 2a, the carbon emission value of the building complex decreases gradually with the increase in building spacing (W-E BD) in the west–east direction, and reaches a minimum value of 35.1 kgCO₂/m²·a at a building spacing of 1.8 H (detailed in

Table 6. Simulation of carbon emission data for a residential building complex with increasing west–east building distance.

W-E BD	HEC kWh/m2·a	SRHM MWh/m2·a	CE kgCO2/m2·a	HPC kWh/m2·a	CPC kWh/m2·a
1.0H	31.48	16.359	36.34	31.48	11.93
1.2H	31.48	16.363	36.36	31.48	11.93
1.4H	31.45	16.501	36.07	31.45	11.96
1.6H	31.43	16.527	35.94	31.43	11.97
1.8H	31.25	16.833	35.10	31.25	12.02
2.0H	31.27	16.729	35.28	31.27	12.00
2.2H	31.30	16.706	35.41	31.30	11.99

). Beyond this point, the carbon emission value of the complex tends to increase with further increase in building spacing. Similarly, in the south–north direction (Figure 2b), the overall carbon emission value of the building cluster shows a decreasing trend with the gradual increase in building distance (south–north span). Respectively, at a spacing of 1.6H and 1.8H, the carbon emission values of the building cluster are 33.66 kgCO₂/m²·a and 33.67 kgCO₂/m²·a. It is worth noting that the heating energy consumption (HPC), which is closely related to the building’s carbon emissions, shows a consistent trend with carbon emissions in both the west–east-oriented and south–north-oriented building clusters. Cooling energy consumption (CPC) shows the opposite trend. The changes in heating energy consumption were more significant than the changes in cooling energy consumption. These findings suggest that heating energy consumption has a greater impact on carbon emissions in residential buildings.

In the city of Wuhan, China, there is no centralized city heating facility, and so winter heating primarily relies on electric power plants. During this season, solar radiation plays a key role in influencing the heating energy consumption (HEC) of residential building complexes. In order to investigate the impact of solar radiation on the carbon emissions of residential buildings with different spacing, we conducted simulations to evaluate the solar radiation received by the buildings during the heating months and their corresponding heating energy consumption. As shown in Figure 3a, the heating energy consumption of the west–east oriented building cluster reaches a minimum value of 31.25 kWh/m²·a when the building spacing is 1.8H and then gradually increases. This suggests that in winter, the solar radiation acquired by the buildings significantly influen the heating energy consumption of the building complex.

In addition, when the building spacing reaches 1.8H in the west–east direction, the degree of mutual shading between buildings is relatively low. In the south–north direction, the heating energy consumption of the complex gradually decreases as the building spacing increases, reaching a minimum value of 30.74 kWh/m²·a at a building spacing of 2.2 H. In contrast, the solar radiation shows the opposite trend, reaching a maximum value of 17.393 MWh/m²·a at 2.2 H, as shown in Figure 3b and

Table 7. Simulation of carbon emission data for a residential building complex with increased south–north building distance.

S-N BD	HEC kWh/m2·a	SRHM MWh/m2·a	CE kgCO2/m2·a	HPC kWh/m2·a	CPC kWh/m2·a
1.0H	31.48	16.359	36.34	31.48	11.93
1.2H	31.31	16.728	35.20	31.31	12.03
1.4H	31.13	16.858	34.04	31.13	12.12
1.6H	31.05	16.926	33.66	31.05	12.14
1.8H	31.03	17.082	33.67	31.03	12.14
2.0H	30.81	17.289	32.44	30.81	12.22
2.2H	30.74	17.393	32.02	30.74	12.26

. This can be attributed to the fact that in the south–north direction, the gradual increase in the building spacing leads to a decrease in mutual shading between buildings, which positively affects the amount of solar radiation.

Based on the above analysis, it is clear that the amount of solar radiation captured by the building complex is negatively correlated with the carbon emissions during the heating period, thus significantly influencing the extent of carbon emissions from the buildings. The exact linear relationship of this effect will be explored and discussed in detail in the subsequent parametric trend analysis.

To investigate the variations in solar radiation and building cooling energy consumption with increasing building spacing, we employed the VirVil-HTB2 tool to calculate and simulate the energy consumption of residential building complexes with varying spacing. Analysis of the data presented in Figure 4a and

Table 8. Table on the effect of BD on cooling energy consumption and solar radiation exposure.

W-E BD	CEC kWh/m2·a	SRCM MWh/m2·a	S-N BD	CEC kWh/m2·a	SRCM MWh/m2·a
1.0H	47.71	41.530	1.0H	47.71	41.530
1.2H	47.71	41.538	1.2H	48.10	42.270
1.4H	47.83	42.023	1.4H	48.49	42.587
1.6H	47.88	42.085	1.6H	48.57	42.688
1.8H	48.08	42.530	1.8H	48.54	42.867
2.0H	48.01	42.237	2.0H	48.90	43.244
2.2H	47.97	42.195	2.2H	49.03	43.467

reveals a consistent trend of high and low variations in cooling energy consumption and solar radiation as the west–east building spacing increases. As the building spacing extends from 1.0H to 1.8H, the energy consumption rises in tandem with the augmented solar radiation. Notably, at a building spacing of 1.8H, both solar radiation and building cooling energy consumption reach their peak levels: 42.53 kWh/m²·a and 48.08 kWh/m²·a, respectively. As the building spacing further increases from 1.8H to 2.2H, cooling energy consumption decreases due to a decline in solar radiation. This demonstrates a significant correlation between cooling energy consumption in the cooling month in the Wuhan area and solar radiation, with solar radiation primarily influencing the parallel row layout. Additionally, the shading relationship between building groups exhibits a decreasing-then-increasing trend in response to changes in the west–east building spacing.

To further validate the positive correlation between solar radiation and building cooling energy consumption with varying building spacing, we analyzed solar radiation and cooling energy consumption patterns in the south–north direction of the building complex. The results, as depicted in Figure 4b and Table 8, reveal a consistent and favorable trend in the changes in solar radiation and building cooling energy consumption with increasing south–north building spacing. It is noteworthy that the solar radiation and building energy consumption increase as the building spacing expands. Notably, when the building spacing reaches 2.2H, the solar radiation reaches its peak value of 43.467 MWh/m²·a, while the building energy consumption also reaches its maximum of 49.03 kWh/m²·a. These findings not only confirm the applicability of the above energy consumption characteristics in the south–north direction but also emphasize the fact that in parallel rows of building clusters, solar radiation affects the building energy consumption in the cooling months mainly through the mutual shading effect between the building clusters.

3.2. Analysis of the Impact Characteristics of Building Staggered Spacing on Solar Radiation, Operational Energy Consumption, and Carbon Emissions

Staggered layouts are a common alternative to row layouts in the design of residential complexes. To examine the impact of varying staggered distances between buildings on building carbon emissions, we selected the 2nd and 4th rows from south to north, as well as from west to east. Incremental increases of 5 m were applied to the staggered distance until reaching 30 m, enabling observations of changes in building carbon emission values, solar radiation, and building energy consumption. Analysis of the data presented in Figure 5a and

Table 9. Simulation of carbon emission data for a residential building complex with increased west–east building staggered distance.

W-E BSD	HEC kWh/m2·a	SRHM MWh/m2·a	CE kgCO2/m2·a	HPC kWh/m2·a	CPC kWh/m2·a
0 m	31.48	16.359	36.34	31.48	11.93
5 m	31.43	16.393	35.95	31.43	11.96
10 m	31.47	16.423	35.85	31.47	11.99
15 m	31.42	16.444	35.53	31.42	12.02
20 m	31.50	16.369	35.92	31.50	11.99
25 m	31.51	16.383	35.77	31.51	12.02
30 m	31.54	16.319	35.93	31.54	12.00

reveals that, in the west–east direction, building carbon emissions exhibit a decreasing trend as the staggered column distance increases from 0 m to 15 m. The smallest recorded carbon emission value was 35.53 kgCO₂/m²·a, when the staggered column distance reached 15 m. By examining the solar radiation data in Figure 5b and

Table 10. Simulation of carbon emission data for a residential building complex with increased south–north building staggered distance.

S-N BSD	HEC kWh/m2·a	SRHM MWh/m2·a	CE kgCO2/m2·a	HPC kWh/m2·a	CPC kWh/m2·a
0 m	31.48	16.359	36.34	31.48	11.93
5 m	31.61	16.106	37.18	31.61	11.85
10 m	31.65	16.017	37.40	31.65	11.84
15 m	31.60	15.937	37.09	31.60	11.86
20 m	31.62	16.208	37.33	31.62	11.85
25 m	31.59	15.967	37.03	31.59	11.87
30 m	31.64	16.037	37.33	31.64	11.84

, a negative correlation between carbon emission values and solar radiation data becomes evident. The direct influence of energy consumption data on carbon emissions was found to be relatively small. This phenomenon may be related to the spatial characteristics of the parallel row layout, which is common in the Wuhan area. The effectiveness of the staggered row layout during winter may be influenced by its spatial configuration, whereas the parallel row layout appears to perform differently under the same conditions. A staggered column distance of 15 m was identified as the most optimal in mitigating tunnel wind effects. Furthermore, upon comparing the data presented in Figure 5b and Figure 6b, consistent distribution trends were observed in energy consumption and carbon emission values of the building complex when changes in distance occurred in the south–north direction. This consistency can be mainly attributed to the predominance of northerly winds in the Wuhan area during winter, where changes in distance in the south–north direction have less impact on alleyway wind fluctuations and consequently exert less influence on fluctuations in energy consumption levels within the building complex.

Based on the aforementioned analysis, it is evident that the carbon emission levels of residential building clusters exhibit a substantial negative correlation with solar radiation when utilizing staggered rows in both the west–east and south–north directions. However, it is important to note that the linear correlation between carbon emission values and solar radiation is significantly weaker compared to the effect of changes in building spacing. This can be attributed to the pronounced influence of the complex wind environment resulting from the adopted staggered rows layout, which exerts a greater impact on building energy consumption.

The staggered layout of a residential building complex exhibits similar distribution characteristics to the parallel row layout. However, in regions with hot summers and cold winters, the staggered layout may create ventilation pathways that could affect the building’s energy consumption levels [52,53]. To investigate the influence of solar radiation on the cooling energy consumption levels of buildings, we calculated and simulated the energy consumption levels for different staggered spacings. The impact of staggered spacing on solar radiation and cooling energy consumption in the west–east direction is illustrated in Figure 7a and

Table 11. Table on the effect of BSD on cooling energy consumption and solar radiation exposure.

W-E BSD	CEC kWh/m2·a	SRCM MWh/m2·a	S-N BSD	CEC kWh/m2·a	SRCM MWh/m2·a
0 m	47.71	41.530	0 m	47.71	41.530
5 m	47.41	40.738	5 m	47.86	41.639
10 m	47.34	40.575	10 m	47.98	41.831
15 m	47.46	40.630	15 m	48.08	41.876
20 m	47.39	41.141	20 m	47.97	41.739
25 m	47.47	40.726	25 m	48.06	41.811
30 m	47.38	40.718	30 m	48.02	41.648

. Through comparative analysis, it was observed that when the staggered spacing ranged from 0 m to 10 m, solar radiation and cooling energy consumption gradually decreased with increasing spacing. When the staggered building spacing reached 10 m, solar radiation and cooling energy consumption reached their lowest values of 40.575 MWh/m²·a and 47.34 kWh/m²·a, respectively. However, as the staggered spacing exceeded 10 m, solar radiation and energy consumption exhibited fluctuating changes. Notably, when the staggered spacing reached 20 m, solar radiation increased significantly, while cooling energy consumption displayed a decreasing trend, which deviates from the trend observed in the parallel row layout. This disparity may be associated with the spatial configuration of the staggered row layout, which contributes to the reduction in cooling energy consumption in the buildings. When the staggered row spacing of the building group reached 20 m, the spatial configuration became more open, which may contribute to an increase in solar radiation and a decrease in building cooling energy consumption.

Detailed simulations were conducted to calculate the variations in solar radiation and cooling energy consumption as the south–north spacing increases. The findings are presented in Figure 7b and Table 11. Within the staggered spacing range of 0 m to 15 m, the solar radiation and cooling energy consumption of the buildings exhibit a gradual increase, displaying a significant positive correlation. When the staggered spacing reaches 15 m, both solar radiation and cooling energy consumption reach their maximum values of 41.876 MWh/m²·a and 48.08 kWh/m²·a, respectively. However, when the staggered spacing exceeds 15 m, both solar radiation and cooling energy consumption demonstrate a wave-like decreasing trend. In summary, it can be observed that in the south–north direction, the influence of the layout pattern diminishes as the staggered spacing increases. Instead, solar radiation emerges as the primary factor influencing the intensity of cooling energy consumption.

3.3. Analysis of the Impact of Building Orientation on Solar Radiation, Operational Energy Consumption, and Carbon Emissions

The orientation of a residential building complex plays a crucial role in solar radiation exposure, which in turn influences building energy consumption and carbon emissions. To examine the characteristics of the influence of building orientation on the carbon emissions of residential building complexes, we conducted simulations and calculations for various orientation angles, increasing in 5° increments in a counterclockwise direction. By comparing and analyzing the energy consumption and carbon emission values, insights can be gained.

The data presented in Figure 8 and

Table 12. Simulation of carbon emission data for a residential building complex with increased building orientation.

BO	HEC kWh/m2·a	SRHM MWh/m2·a	CE kgCO2/m2·a	HPC kWh/m2·a	CPC kWh/m2·a
0°	31.48	16.359	36.34	31.48	11.93
15°	31.74	16.242	36.33	31.74	11.99
30°	32.20	16.026	36.33	32.20	12.23
45°	32.50	15.662	36.26	32.50	12.41
60°	32.48	15.966	36.51	32.48	12.39
75°	32.28	16.067	36.01	32.28	12.41
90°	32.21	16.179	36.24	32.21	12.36
105°	32.23	15.921	35.90	32.23	12.43
120°	32.38	15.698	36.14	32.38	12.45
135°	32.23	15.544	35.77	32.23	12.41
150°	31.99	15.828	35.96	31.99	12.23
165°	31.51	16.177	35.98	31.51	11.98

demonstrate an overall fluctuating decreasing trend in carbon emission values as the deflection angle increases. When the building orientation is 135°, the carbon emission value of the building reaches its lowest point at 35.77 kgCO₂/m²·a. This aligns with the overall distribution characteristics of solar radiation values depicted in Figure 9a, where the solar radiation value also reaches its minimum at 15.544 MWh/m²·a. Conversely, when the deflection angle reaches 60°, the highest carbon emission value of 36.51 kgCO₂/m²·a is observed, while the solar radiation value is lower at 15.966 MWh/m²·a.

Comparative analysis reveals that the distribution pattern of building energy consumption increases with the deflection angle, reaching a peak and then gradually decreasing. The distribution pattern approximately exhibits a symmetric trend, with 90° serving as a rough demarcation point.

Based on the aforementioned analysis, it becomes apparent that building orientation exerts a noteworthy negative correlation on building carbon emissions. By appropriately adjusting the building orientation, it is possible to effectively reduce the carbon emission value of the building complex. However, it is important to note that the influence of building orientation on building energy consumption and solar radiation follows a symmetrical distribution pattern. In contrast, the relationship between building orientation and carbon emissions is more intricate, requiring a more comprehensive linear analysis for detailed examination.

The comparative analysis of the data presented in Figure 9b and

Table 13. Table on the effect of BO on cooling energy consumption and solar radiation exposure.

BO	CEC kWh/m2·a	SRCM MWh/m2·a	BO	CEC kWh/m2·a	SRCM MWh/m2·a
0°	47.71	41.530	90°	49.45	40.399
15°	47.97	41.304	105°	49.72	40.620
30°	48.94	41.970	120°	49.78	41.327
45°	49.63	41.751	135°	49.64	41.672
60°	49.55	41.352	150°	48.91	41.986
75°	49.63	40.660	165°	47.94	41.168

highlights an overall positive correlation between solar radiation and the intensity of cooling energy consumption. When the building complex faces 0°, the cooling energy consumption reaches its lowest point at 47.71 kWh/m²·a. Subsequently, as the building’s facing angle increases, the cooling energy consumption gradually rises until it reaches its maximum at a building orientation of 45°. When the building orientation is between 45° and 90°, solar radiation tends to decrease. At a building orientation of 90°, the solar radiation reaches a lower value of 40.399 MWh/m²·a.

After conducting a comparative analysis, it becomes evident that the building complex experiences lower solar radiation reception and, correspondingly, lower building cooling energy consumption when the building orientation is at 0°, 165°, and 90°. Interestingly, in the overall distribution pattern, solar radiation and cooling energy consumption exhibit an approximately symmetric trend, with 90° serving as a rough demarcation point.

3.4. Analysis of the Impact Characteristics of Building Open Spaces on Operational Energy Consumption and Carbon Emissions

To analyze the impact of different open space layout configurations on the carbon emissions (CE) and energy consumption characteristics of residential building complexes, we compared six open space layout types using simulation data. As shown in Figure 10 and

Table 14. Table showing the effect of building open spaces on carbon emissions and energy consumption for heating and cooling.

Open Space Layout Types	CE kgCO2/m2·a	HEC kWh/m2·a	CEC kWh/m2·a
C-SN	32.43	24.68	15.73
D-SN	31.57	24.32	15.91
M-SN	33.14	24.98	15.56
C-WE	31.70	24.35	15.87
D-WE	29.83	23.49	16.21
M-WE	33.36	25.16	15.59

, the C-SN layout exhibited the highest building carbon emissions at 32.43 kgCO₂/m²·a, while the D-WE layout recorded the lowest at 29.83 kgCO₂/m²·a. Overall, west–east layouts generally produced lower carbon emissions than their corresponding south–north counterparts, with the D-WE layout performing best. Regarding energy consumption, cooling energy demand peaked at 16.21 kWh/m²·a for the D-WE layout and bottomed out at 15.56 kWh/m²·a for the M-SN layout, indicating significant variability in cooling energy consumption across west–east orientations. Heating energy consumption exhibits relatively small variation across different layouts, with the D-WE layout achieving the lowest value, while the mixed open space layout shows slightly higher consumption.

Notably, building carbon emissions exhibit a consistent trend with heating energy consumption. The M-SN layout shows higher values for both indicators, further highlighting the significant impact of heating energy consumption on carbon emissions. In contrast, no clear positive correlation was observed between cooling energy consumption and carbon emissions. Particularly in west–east layouts, buildings with higher cooling energy consumption actually exhibited lower carbon emissions. Layout D-WE demonstrated the best carbon emissions performance, primarily due to its more favorable ventilation conditions in winter, which significantly reduced heating energy consumption. At the same time, although its cooling energy consumption was the highest among all layouts, the reduction in heating energy consumption outweighed the increase in cooling energy consumption, resulting in the lowest total annual energy consumption and, consequently, the lowest carbon emissions. These results indicate that heating energy consumption may contribute more to the carbon emissions of building complexes than cooling energy consumption, a trend that is particularly pronounced in north–south orientations.

The physical mechanisms underlying these differences can be explained by winter ventilation and heat dissipation, summer shading effects, and the impact of spatial enclosure on heat accumulation. Layouts with high openness and oriented toward the prevailing winter wind direction (such as D-WE) can effectively remove heat from the building envelope surfaces, thereby reducing heating loads [54,55]. In highly enclosed layouts (such as the clustered type), buildings cast strong mutual shadows, which can significantly reduce solar heat gain in summer and consequently lower cooling energy consumption; however, excessive shading in winter blocks solar radiation from entering the interior, thereby increasing heating demand [56]. Layouts with a strong sense of enclosure tend to create localized stagnant air zones where heat is retained. This can increase cooling loads in summer but may slightly reduce heating demand in winter due to heat accumulation [57]; however, the overall effect must be evaluated in conjunction with building orientation.

Based on the aforementioned mechanisms, the performance of different open-space types is as follows: The D-WE layouts offer optimal winter ventilation and the lowest heating energy consumption; although they have the highest cooling energy consumption, they result in the lowest annual carbon emissions; the C-WE layout is the second-best; among north–south orientations, the dispersed layout is slightly better than the concentrated layout; although the hybrid layout has lower cooling energy consumption, it has the highest heating energy consumption and carbon emissions, showing no clear advantage. This trade-off suggests that, under similar climatic conditions, prioritizing the emission reduction benefits of optimizing winter ventilation may be more advantageous than simply controlling summer cooling loads.

4. Discussion

4.1. Parametric Response Trend Analysis

This study investigated these modeled relationships by examining the parametric response trends in solar radiation (with summer solar radiation denoted as SRCM and winter solar radiation as SRHM), cooling energy consumption, and heating energy consumption under varying building spacings, staggered spacings, and orientations. As presented in

Table 15. Exploratory correlation analysis of cooling energy consumption and related factors.

Type	Correlation Factors	Pearson Correlation	Sig
W-E	SRCM-BD	0.817	0.025
	CEC-SRCM	0.974	<0.001
	CEC-BD	0.871	0.011
S-N	SRCM-BD	0.967	<0.001
	CEC-SRCM	0.989	<0.001
	CEC-BD	0.957	<0.001
W-E	SRCM-BSD	−0.423	0.344
	CEC-SRCM	0.781	0.038
	CEC-BSD	−0.516	0.236
S-N	SRCM-BSD	0.376	0.406
	CEC-SRCM	0.844	0.017
	CEC-BSD	0.786	0.036
BO	SRCM-BO	−0.108	0.737
	CEC-SRCM	−0.223	0.485
	CEC-BO	0.276	0.386

, there was a significant positive correlation between the amount of solar radiation received by buildings in summer and their spacing in both west–east and south–north orientations. As the building spacing increases, the mutual shading effect among structures is weakened, leading to a significant increase in solar radiation heat gain.

Furthermore, a significant positive correlation is discerned between the cooling energy intensity of the buildings and the acquired solar radiation. This suggests that heightened building spacing reduces shading effects and consequently amplifies the cooling energy intensity. For west–east staggered spacing, both solar radiation and cooling energy intensity exhibit negative correlations, though the results do not reach statistical significance (Sig > 0.05). Conversely, cooling energy consumption displays a substantial positive correlation with solar radiation, aligning with the requirement for correlation. Meanwhile, when examining south–north staggered column spacing, solar radiation and staggered column spacing exhibit no discernible correlation. Nonetheless, notable positive correlations exist between solar radiation and cooling energy consumption, as well as cooling energy consumption and south–north staggered column spacing distance. In the assessment of orientation factors, the amount of solar radiation received by the building does not exhibit significant correlations with either cooling energy consumption or building orientation.

Linear regression was carried out to help explain the results of the correlation analysis. As seen in Table 16, the relationship between summer solar radiation and building spacing in the west–east orientation can be expressed as follows:

Y₁ = 0.291X₁ + 47.419

(3)

where Y₁ represents the summer solar radiation in the west–east direction, and X₁ represents W-E BD.

Similarly, the formula describing the relationship between cooling energy consumption and building spacing in the west–east orientation is

Y₂ = 0.310X₂ + 46.811

(4)

where Y₂ represents the cooling energy consumption in the west–east direction, and X₂ represents W-E BD.

The formula describing the relationship between summer solar radiation and building spacing in the south–north orientation is

Y₃ = 1.436X₃ + 40.368

(5)

where Y₃ represents the summer solar radiation in the south–north direction, and X₃ represents S-N BD.

The formula describing the relationship between cooling energy consumption and building spacing in the south–north orientation is

Y₄ = 1.002X₄ + 46.874

(6)

where Y₄ represents the cooling energy consumption in the south–north direction, and X₄ represents S-N BD.

The formula describing the relationship between cooling energy consumption and building staggered spacing in the south–north orientation is

Y₅ = 0.009X₅ + 47.813

(7)

where Y₅ represents the cooling energy consumption in the south–north direction, and X₅ represents S-N BSD.

Table 16. Exploratory parametric trend analysis of cooling energy consumption and related factors.

Type	Correlation Factors	R2	Sig1	Constant Term	Sig2	Coefficient	Sig3
W-E	SRCM-BD	0.710	0.011	47.419	<0.001	0.291	0.011
	CEC-BD	0.759	0.011	46.811	<0.001	0.310	0.011
S-N	SRCM-BD	0.921	<0.001	40.368	<0.001	1.436	<0.001
	CEC-BD	0.898	<0.001	46.874	<0.001	1.002	<0.001
	CEC-BSD	0.541	0.036	47.813	<0.001	0.009	0.036

Table 16. Exploratory parametric trend analysis of cooling energy consumption and related factors.

Type	Correlation Factors	R2	Sig1	Constant Term	Sig2	Coefficient	Sig3
W-E	SRCM-BD	0.710	0.011	47.419	<0.001	0.291	0.011
	CEC-BD	0.759	0.011	46.811	<0.001	0.310	0.011
S-N	SRCM-BD	0.921	<0.001	40.368	<0.001	1.436	<0.001
	CEC-BD	0.898	<0.001	46.874	<0.001	1.002	<0.001
	CEC-BSD	0.541	0.036	47.813	<0.001	0.009	0.036

A correlation analysis was conducted to explore the relationships between building morphological elements, solar radiation, carbon emissions, and heating energy consumption. As shown in

Table 17. Exploratory correlation analysis of heating energy consumption and related factors.

Type	Correlation Factors	Pearson Correlation	Sig
W-E	SRHM-BD	0.875	0.010
	CE-SRHM	−0.995	<0.001
S-N	SRHM-BD	0.980	<0.001
	CE-SRHM	−0.984	<0.001
W-E	SRHM-BSD	−0.362	0.425
	CE-SRHM	−0.611	0.145
S-N	SRHM-BSD	−0.545	0.206
	CE-SRHM	−0.618	0.139
BO	SRHM-BO	−0.448	0.144
	CE-SRHM	0.442	0.151

and Figure 11, building spacing exhibits a positive correlation with solar radiation in both orientations, consistent with the expectation that increased spacing reduces mutual shading and enhances solar heat gain. Carbon emissions show a negative correlation with solar radiation (Sig. < 0.001), suggesting that greater solar heat gain may reduce heating demand and associated carbon emissions.

We found that the solar radiation received by buildings did not exhibit a statistically significant correlation with either staggered spacing or orientation, and no significant correlation was observed between the carbon emission intensity of buildings and the solar radiation they received. Based on these findings, a supplementary analysis was conducted to examine the parametric response trends, aiming to explore the influence of different building spacing configurations in the west–east and south–north directions on solar radiation levels, as well as the mechanism through which variations in solar radiation affect carbon emission intensity.

A supplementary parametric trend analysis was conducted to examine the influence of building spacing on solar radiation levels and the subsequent effect on carbon emission intensity. As shown in Table 18 and Figure 12, in the west–east direction, the regression between solar radiation and building spacing yields an R² of 76.5%, with a p-value below 0.001. The resulting equation is as follows:

Y₆ = 0.376X₆ + 15.939

(8)

where Y₆ represents the winter solar radiation in the west–east direction, and X₆ represents the building spacing in the west–east direction.

For the relationship between solar radiation and building carbon emissions in the west–east direction, the R² is 0.990, with both the constant and coefficient terms significant at less than a 0.001 level. The corresponding equation is as follows:

Y₇ = −2.775X₇ + 81.780

(9)

where Y₇ denotes the winter building carbon emissions in the west–east direction, and X₇ denotes solar radiation in the west–east direction.

In the south–north direction, the regression between solar radiation and building spacing yields an R² of 0.960 (p < 0.001), with both constant and coefficient terms significant. The corresponding equation is as follows:

Y₈ = 0.794X₈ + 15.677

(10)

where Y₈ denotes winter solar radiation in the south–north direction, and X₈ denotes building spacing in the south–north direction. The effect of building spacing on solar radiation is more pronounced in the south–north direction than in the west–east.

For the relationship between solar radiation and building carbon emissions in the south–north direction, the R² is 0.969 (p < 0.001), with both constant and coefficient terms significant. The corresponding equation is as follows:

Y₉ = −4.205X₉ + 105.18

(11)

where Y₉ represents the winter building carbon emissions in the south–north direction, and X₉ represents solar radiation in the south–north direction. The negative coefficient in this equation indicates that a significant negative correlation also exists in the south–north direction, with a more pronounced carbon reduction effect than that observed in the west–east direction, suggesting that optimizing building spacing in the south–north orientation plays a more significant role in reducing carbon emissions.

Table 18. Exploratory parametric trend analysis of heating energy consumption and related factors.

Type	Correlation Factors	R2	Sig1	Constant Term	Sig2	Coefficient	Sig3
W-E	SRHM-BD	0.765	0.010	15.973	<0.001	0.376	0.010
	CE-SRHM	0.990	<0.001	81.780	<0.001	−2.775	<0.001
S-N	SRHM-BD	0.960	<0.001	15.677	<0.001	0.794	<0.001
	CE-SRHM	0.969	<0.001	105.180	<0.001	−4.205	<0.001

Table 18. Exploratory parametric trend analysis of heating energy consumption and related factors.

Type	Correlation Factors	R2	Sig1	Constant Term	Sig2	Coefficient	Sig3
W-E	SRHM-BD	0.765	0.010	15.973	<0.001	0.376	0.010
	CE-SRHM	0.990	<0.001	81.780	<0.001	−2.775	<0.001
S-N	SRHM-BD	0.960	<0.001	15.677	<0.001	0.794	<0.001
	CE-SRHM	0.969	<0.001	105.180	<0.001	−4.205	<0.001

Figure 12. Histogram of the model’s residual distribution.

Figure 12. Histogram of the model’s residual distribution.

In summary, the parametric response trend analysis suggests a sequential relationship among building spacing, solar radiation, and carbon emissions: increased building spacing is associated with higher winter solar radiation, which in turn correlates with reduced carbon emissions. This relationship is particularly pronounced in the south–north direction, providing a reference for energy-efficient building design under the idealized simulation conditions of this study. However, it should be noted that all regression and correlation analyses in this study are exploratory in nature, and the potential nonlinear relationships between building spacing, solar radiation, energy consumption, and carbon emissions require further investigation.

4.2. Comparison with Prior Studies and Contribution

Unlike previous studies, which often included the open space ratio as a macro-level statistical indicator in regression models [58], or focused on the independent effects of a single morphological parameter, this study incorporates four parameters—building spacing, staggered spacing, orientation, and open space layout—into a unified analytical framework to systematically examine the mechanisms by which the combination of multiple factors influences the energy consumption of building clusters. Our previous research [10] focused on typological comparisons of layout forms; this study builds upon that foundation to advance to a quantitative analysis at the parameter level, examining the contribution weights of each design parameter. Furthermore, unlike traditional studies that directly establish a “form–energy consumption” correlation [59], this study uses solar radiation gain as a key mediating indicator to construct an analytical framework for the “form–radiation–energy consumption” transmission mechanism, thereby overcoming the limitations of existing research in revealing intermediate transmission mechanisms.

Based on the aforementioned analytical framework, this study employs a combined approach of parametric simulation and parameter response trend analysis. By coupling two stages—solar radiation simulation and energy consumption simulation—we quantitatively analyzed the “form–radiation–energy consumption” transmission chain. Specifically, solar radiation simulation was used to quantify the spatiotemporal distribution of solar radiation received by building surfaces, which served as a key input for the energy consumption simulation. The parameter response trend analysis was used to isolate the independent effects of multiple morphological factors and identify the patterns of their influence on energy consumption. This method overcomes the limitations of traditional case studies, which struggle to separate multiple influencing factors and identify independent patterns of influence, thereby providing trend-based, mechanism-driven evidence for optimizing residential area morphology.

To further validate the robustness of the model, we benchmarked the simulation results against existing studies in regions with hot summers and cold winters. Regarding building spacing, this study found that the effect of east–west spacing on cooling energy consumption exhibits a non-monotonic relationship, peaking at 1.8H; increasing north–south spacing continues to drive up cooling energy consumption, while heating energy consumption decreases significantly with increasing north–south spacing. This is consistent with the spacing-energy relationship observed by Cui et al. in hot-summer, cold-winter climates [60]. Regarding staggered spacing, existing research indicates that staggered layouts can significantly reduce building energy consumption [61,62]. This study further examined the influence of staggered orientation and found that east–west staggering reduces building cooling energy consumption, whereas north–south staggering actually increases it. Regarding building orientation, Zhou et al. found that north–south orientation maximizes community energy self-sufficiency [31]. This study further found that building energy consumption exhibits periodic fluctuations with orientation, with southeast orientation (approximately 135°) resulting in the lowest carbon emissions.

A notable trade-off emerges in decentralized east–west-oriented open spaces, where cooling energy consumption and carbon emissions exhibit a clear inverse relationship. Although this layout has the highest cooling energy consumption, it significantly reduces heating energy consumption through enhanced natural ventilation; the reduction in heating energy consumption exceeds the increase in cooling energy consumption, resulting in the lowest total annual energy consumption and the smallest carbon emissions. Based on an analysis of the “form–radiation–energy consumption” mechanism, we propose the following specific design guidelines: Regarding building spacing, east–west spacing should avoid the 1.8H peak range, while north–south spacing should not be excessive, as increasing it will continuously raise cooling energy consumption; regarding staggered spacing, east–west staggered layouts should be prioritized to reduce building cooling energy consumption, and north–south staggered layouts should be avoided; Regarding building orientation, a southeast orientation (approximately 135°) should be prioritized to minimize carbon emissions; regarding open space patterns, when carbon reduction is the primary objective, the D-EW layout should be prioritized; if fluctuations in cooling energy consumption also need to be considered, north–south dispersed or centralized layouts may be considered. By regulating solar heat gain and utilization efficiency, the above strategies offer valuable insights for optimizing the layout of high-density residential areas in Wuhan and other regions with hot summers and cold winters.

4.3. Limitations and Future Prospects

This study employs standardized parameters and idealized models to examine the influence of individual urban form parameters under controlled conditions. To this end, it uses a simplified block form to represent cubic buildings, focusing on core geometric variables such as length, width, and height, while simplifying real-world driving factors such as vegetation dynamics, paving materials, facade construction, land-use patterns, and natural ventilation behavior. While this approach enhances internal validity and comparability, it inevitably excludes site-specific factors such as topography, surrounding buildings, and non-standard forms, making it difficult for the model to capture the nonlinear responses resulting from the coupling of multiple factors in real communities. Therefore, the quantitative results of this study should be regarded as indicative trends, and their applicability should be limited to regions with climatic conditions similar to those simulated; they should not be directly extrapolated to areas with significantly different climates.

In terms of analytical methods, linear models are primarily used for exploratory analysis, aiming to identify the main effects and basic trends of key variables. Consequently, the interpretation of simulation results in this paper focuses on identifying trends in parameter responses rather than rigorous statistical inference. Future research could incorporate comparative analyses across more climate zones and introduce actual energy consumption monitoring data to calibrate simulation results, thereby enhancing the generalizability and accuracy of the research conclusions.

5. Conclusions

This study examines the layout patterns of residential buildings in Wuhan as a representative case of regions with hot summers and cold winters. Using VirVil-HTB2 software for modeling and simulating building cluster layouts and employing SPSS 27 for data analysis, this research aims to investigate how different layout design parameters influence the energy consumption and carbon emission intensity of building clusters. The main conclusions are as follows:

• This study adopts survey research and computer-based energy simulation as its primary methods, using the amount of solar radiation received by buildings as a key metric to quantitatively analyze the mechanisms through which building spacing, staggered spacing, orientation, and open space layout influence the energy consumption intensity of building clusters. This approach provides a quantitative basis for the planning and design of residential building complexes, while also offering insights into the investigation of factors affecting energy consumption intensity. The research framework holds reference value for studies on building energy consumption in regions of China with hot summers and cold winters, as well as in areas with similar climatic characteristics.
• In regions of China characterized by hot summers and cold winters, the solar radiation index, as computed through the VirVil-HTB2 simulation, serves as a valuable metric for quantifying the shading impact of adjacent buildings. This enables a more accurate prediction of building cooling energy intensity, even without factoring in the orientation variable. Notably, the solar radiation index exhibits a robust linear correlation with cooling energy intensity under these conditions. In contrast, when accounting for variations in building orientation, the relationship between solar radiation and cooling energy intensity deviates from this characteristic.
• When exclusively examining the impact of alterations in building spacing on cooling energy consumption, the cooling energy consumption intensity reaches its peak when the west–east building spacing reaches 1.8H. Conversely, in the south–north direction, the intensity of cooling energy consumption steadily amplifies as building spacing increases, concomitant with consistent variations in solar radiation levels. The comprehensive implementation of a west–east staggered layout serves to mitigate building cooling energy consumption. However, when a south–north staggered layout is employed, building cooling energy consumption escalates. When solely considering the orientation factor, energy intensity exhibits a cyclical fluctuation pattern. This modulation can reduce solar radiation by up to 3.89% while lowering cooling energy consumption by up to 4.3%. When only building open space is considered, carbon emissions and heating energy consumption show consistent trends. The dispersed west–east layout yields the lowest heating energy consumption. While heating energy consumption varies only slightly across layouts, cooling energy consumption fluctuates notably in west–east orientations. This study provides a quantitative reference for the design of energy-efficient urban forms.