Street Orientation, Aspect Ratio, and Tree Species Interactions on Heat Exposure in Temperate Monsoon Climate

Abstract

Rapid urbanization has intensified microclimatic deterioration in temperate monsoon cities, directly affecting human thermal comfort. This study investigates the regulatory effects of common street tree species under varying street aspect ratios (H/W) and orientations in Shenyang, China, a representative temperate monsoon city characterized by cold winters. Field surveys and questionnaire data were combined with ENVI-met simulations to quantify thermal comfort responses using the Universal Thermal Climate Index (UTCI). Results demonstrate that street geometry strongly constrains microclimate regulation: streets with H/W = 1.2 and a SE–NW orientation achieved the most favorable balance between shading and ventilation, yielding the lowest UTCI values. Significant interspecies variability was observed: Golden Elm and Chinese Willow provided the greatest cooling benefits, whereas Ginkgo exhibited limited adaptability, particularly in enclosed or highly open canyons. A comparison with subjective thermal comfort votes confirmed strong model reliability, though discrepancies emerged in dense commercial areas due to non-meteorological factors. Based on these findings, a spatially driven, species-adaptive, and human-centered framework is proposed to optimize street greening strategies in a temperate monsoon city characterized by cold winters. This research provides quantitative evidence for urban greening design, highlights the necessity of integrating spatial form with tree-species selection, and offers practical guidance for resilient thermal comfort management in rapidly urbanizing cold-region cities.

1. Introduction

Urban microclimate issues, primarily driven by anthropogenic activities, have emerged as a critical challenge for sustainable urban development. With global economic growth and accelerated urbanization, the proportion of people living in cities is projected to reach 66% by 2050 [1,2]. Various urban elements—including buildings, roads, traffic, industries, and green infrastructure—exert complex influences on local microclimatic conditions. For instance, inappropriate street density can reduce urban ventilation efficiency, leading to excessive daytime heat accumulation and further deterioration of the thermal environment [3,4]. Microclimate refers to small-scale climatic conditions formed within a specific area, strongly affected by surrounding factors such as urban design, building morphology, and vegetation. Among these, street trees represent one of the most effective interventions for improving urban microclimates and have attracted increasing scholarly attention [5,6]. However, the inappropriate selection of tree species may exacerbate the adverse effects of solar radiation on outdoor thermal environments [7]. Such issues directly contribute to the worsening of urban microclimates, ultimately undermining the quality of the residential environment.

Street trees refer to arbor landscapes planted at regular intervals along both sides of roads or within median greenbelts. They play important roles in regulating the microclimate of streets and enhancing outdoor thermal environments through processes such as transpiration, evapotranspiration, shading, and reflecting solar radiation, thereby improving pedestrians’ thermal comfort [8,9]. A well-planned distribution of street trees has significant positive impacts on improving the urban climatic environment, mitigating the urban heat island effect, providing shade, and reducing wind speed, thus creating a more comfortable outdoor environment for citizens [10]. Among these factors, the tree species selected for street planting is a key determinant in addressing urban environmental issues and enhancing human thermal comfort. The design of street tree species should carefully consider various environmental conditions, while fully reflecting the dual function of environmental protection and landscape beautification [10]. Some of the research conducted in Utrecht, the Netherlands, involving microclimatic measurements and analyses of nine streets with similar geometries, demonstrated that shading by street trees has a substantial effect on thermal comfort. An increase of 10% in vegetation coverage within streets can significantly reduce the mean radiant temperature (MRT) [11,12]. Similar conclusions have been drawn through simulation studies. For example, Aminipouri et al., using the ENVI-met model in six local climate zones of Vancouver, Canada, found that when street tree coverage increased by just 1% of the total area, the daytime MRT decreased by 15.5–17.3 °C [13]. Likewise, Huang et al., through field measurements in Wuhan, China, reported that in summer at midday, areas with high canopy coverage provided greater microclimatic benefits compared with areas of medium or low canopy coverage, reducing air temperature by 3.3 °C and MRT by 13.9 °C [14]. Research by E. S. Krayenhoff et al. highlighted that street trees play a critical role in driving microclimatic variations within streets, improving air temperature conditions, enhancing resilience to extreme heat events, and contributing to human thermal comfort. However, the magnitude of these benefits depends on multiple influencing factors [15]. Furthermore, Vesna Stojakovic and colleagues, based on microclimate measurements and simulations in both summer and winter, found that the cooling effect of trees during sunny and hot days was twice as strong as during cloudy and cold days, with some degree of air temperature reduction also observed in winter. During peak heat periods, trees lowered thermal sensation by up to 16%. In summer, the maximum daily air temperature difference between shaded and unshaded areas reached 2.5 °C, while in winter, trees reduced mean air temperatures by 0.5 °C. These findings suggest that trees play a vital role in regulating microclimates and enhancing human comfort across different seasons [16].

As a carrier through which people directly perceive and experience the urban environment, livable streets play a crucial role in residents’ daily experiences and social life. Functioning as linear spaces that meet people’s everyday needs, these streets not only serve transportation functions but are also equipped with cultural, recreational, and leisure services to support daily activities and social interactions. As such, they represent one of the most frequently used and closely connected types of outdoor public spaces for urban residents [17]. In the relationship between livable streets and the urban microclimate, factors such as street aspect ratio and orientation significantly influence variations in the thermal environment. In a study on the effects of street orientation on solar radiation, Mohajeri et al. selected Geneva, Switzerland—located in a temperate oceanic climate zone—as the study area. Their findings at the urban scale revealed that street orientation is one of the most critical factors affecting solar radiation reception [18]. Similarly, Shareef et al., focusing on Dubai in the United Arab Emirates (tropical desert climate), demonstrated that appropriate street orientations could lower outdoor temperatures by up to 1.8 °C, while higher building diversity within the same block orientation could further reduce outdoor temperatures by up to 1.1 °C [19]. In tropical Singapore, Acero et al. examined the effects of public outdoor urban spaces associated with human activities on thermal comfort at the city scale and identified street orientation as one of the most influential factors [20]. Furthermore, Narimani et al., using Isfahan, Iran (temperate continental climate) as their case study, investigated the influence of vegetation coverage in urban streets on outdoor thermal comfort. Their results indicate that although vegetation coverage contributes positively to thermal comfort improvement, the effect of proper street orientation was more significant than vegetation coverage in enhancing the thermal comfort of urban streets [6].

Urban morphology has a significant influence on microclimatic conditions and outdoor thermal comfort. Building on extensive previous research, this study selects street aspect ratio (H/W) and street orientation as key spatial parameters, while incorporating tree canopy coverage as a dynamic input variable within the simulation framework. Geometric characteristics of typical livable streets were extracted through field inventory to provide baseline data for model development and comparative analysis. According to the Street Design Guidelines, “typical livable streets” are defined as streets located within residential communities, where adjacent land uses are predominantly residential, and the road hierarchy mainly comprises urban secondary roads and local streets [21]. Aspect ratio (H/W), defined as the ratio of the average building height on both sides of the street to the street width, is an important indicator of spatial enclosure [22]. Streets with a high H/W value are prone to forming urban canyon effects, where ventilation is hindered and heat becomes trapped, thereby intensifying heat stress in summer. In contrast, low H/W values promote air circulation and heat dissipation, which enhance pedestrian-level thermal comfort [23,24]. Street orientation, typically expressed as the angle between the street axis and true north, is a critical factor in regulating solar radiation exposure and alignment with prevailing wind directions [25]. When street orientation is parallel to the prevailing wind, high-H/W streets may enhance internal airflow, while low-H/W streets may experience a “venturi effect,” reducing wind speed. Conversely, if streets are oriented perpendicular to prevailing winds, building-induced wind shadows can obstruct air exchange. At oblique angles, bidirectional airflow often occurs within the street, forming a more balanced ventilation pattern [26]; the related mechanisms are illustrated in Figure 1.

Urban microclimate issues are directly related to the quality of life of city residents. Among the key factors influencing microclimate improvement, street trees, street orientation, and street aspect ratio play a significant role in enhancing thermal comfort along urban streets. However, most existing studies have focused on hot-summer/warm-winter [27,28] or hot-summer/cold-winter regions [17,29], while evidence on species selection and its thermal-comfort impacts for livable streets in cold-region cities remains limited. In addition, many cold-region studies rely on simplified modeling approaches (e.g., RayMan) or short-term field observations, which may constrain the comparability and transferability of findings [30,31]. Therefore, this study adopts UTCI and employs ENVI-met for street-scale simulations to improve the applicability of the results for microclimate optimization and species selection. Notably, although Shenyang is located in a cold region, pedestrians can still experience substantial heat exposure during summer [32]. Therefore, the selection of street tree species for livable streets in temperate monsoon cities should not only consider their effectiveness in improving thermal comfort but also account for variations in street spatial morphology and the adaptability of vegetation configuration strategies.

• How do different street spatial morphologies (e.g., street orientation and aspect ratio) influence human thermal comfort in livable streets during summer?
• What are the mechanisms and differences in the effects of common street tree species on thermal comfort improvement under varying street spatial conditions?
• How can an optimized strategy for street tree species selection and configuration be developed for livable streets in temperate monsoon cities?

This research employs the ENVI-met simulation tool to investigate the impacts of different street spatial characteristics (such as orientation and aspect ratio) and common street tree species on human thermal comfort. This study aims to fill the existing research gap in this field, providing both theoretical foundations and practical data support for the scientific selection of street trees in livable streets, while also offering insights into thermal environment improvement in urban design within temperate monsoon cities.

2. Materials and Methods

2.1. Study Framework

This study focuses on the impact of different street tree species on summer thermal comfort in livable streets of cold regions and establishes a research framework consisting of four main components: (1) selection of representative streets and common street tree species based on typical livable streets in the urban area of Shenyang; (2) identification of key meteorological factors influencing thermal comfort and their perceived weights through questionnaire surveys; (3) thermal comfort simulations of different street–tree combinations using the ENVI-met numerical model and the BIO-met tool; and (4) comparative analysis of thermal comfort improvement effects under various tree species configurations based on the three factors of temperature, humidity, and wind, as well as the UTCI index, followed by the proposal of optimized tree species configuration strategies. An overview of the research framework is illustrated in Figure 2.

2.2. Study Area

2.2.1. Selected Area

This study selects Shenyang, a typical cold city in northeastern China, as the research area [33]. Shenyang is located between 122°25′–123°48′ E and 41°12′–42°17′ N, characterized by a temperate monsoon climate with four distinct seasons, a long and cold winter, and a short but hot summer. The hottest month is July, with an average daily maximum temperature of 28 °C and a minimum of approximately 21 °C [34].

To ensure both data representativeness and systematic investigation, typical livable streets were selected within Shenyang’s old urban districts for field inventory. The study area covers Heping, Tiexi, Shenhe, Huanggu, and Dadong Districts. Based on the definition of livable streets, 36 representative street samples were initially selected (Figure 3). Field measurements were conducted to record key spatial elements, including roadway width, sidewalk width, building height, and street tree species. These data provide essential support for identifying the spatial morphological characteristics of streets in the study area and serve as a foundation for subsequent simulations and analyses.

2.2.2. Data Collection and Processing

To construct thermal comfort simulation scenarios suitable for livable streets in temperate monsoon cities, this study systematically collected and processed data from three perspectives: street spatial morphology, meteorological parameters, and street tree structure.

First, based on field surveys of 36 representative livable streets in the old urban districts of Shenyang, geometric parameters of streets were obtained. The results indicate that street widths generally range from 15 to 30 m, with building heights concentrated between 21 and 24 m, and an average street width of 22 m (

Table 1. Basic information of living street space in the main urban districts of Shenyang city.

Administrative Region	Street Name	Street Height (m)	Street Width (m)	Street Aspect Ratio	Street Orientation
Heping District	Shiwuwei Road	24	20	1.2	E–W
	Siping Street	21	18	1.2	S–N
	Nansijing Street	18	20	0.9	S–N
	Nanwujing Street	24	18	1.3	S–N
	Zhenxing Street	21	15	1.4	EN–WS
	Jiaxing Street	21	15	1.4	EN–WS
	Yunji Street	21	18	1.2	S–N
Tiexi District	Guihe Street	21	25	0.8	EN–WS
	Beisizhong Road	21	25	0.8	SE–NW
	Jingxing North Street	20	32	0.6	EN–WS
	Xingshun Street	18	22	0.8	EN–WS
	Beisanzhong Road	18	26	0.7	SE–NW
	Xiaobeisanzhong Road	18	31	0.6	SE–NW
	Xiaobeisizhong Road	18	22	0.8	SE–NW
	Xiaobeierzhong Road	18	30	0.6	SE–NW
	Qixianbei Street	18	34	0.5	EN–WS
Shenhe District	Changqing Road	18	26	0.7	E–W
	Fengle Third Street	21	23	0.9	S–N
	Fengle First Street	21	23	0.9	S–N
	Wenfu Road	24	23	1.0	S–N
	Wencui Road	21	26	0.8	E–W
	Nanta East Street	21	20	1.1	S–N
	Wenfu North Road	21	21	1.0	E–W
Huanggu District	Tianshan Road	21	30	0.7	E–W
	Yushan Road	21	24	0.9	E–W
	Cangshan Road	21	18	1.2	E–W
	Jinshui Street	18	24	0.8	EN–WS
	Minglian Road	18	30	0.6	E–W
Dadong District	Liaoshen Third Street	24	20	1.2	EN–WS
	Ruyi Second Road	18	21	0.9	EN–WS
	Liaoshen 1st Street	18	20	0.9	S–N
	Jixiang 4th Road	24	22	1.1	EN–WS
	Xinsheng 2nd Street	24	27	0.9	S–N
	Xinsheng 3rd Street	15	25	0.6	S–N
	Liaoshen 2nd Street	18	20	0.9	S–N
	Jixiang 2nd Road	24	21	1.1	EN–WS

). Sidewalk widths typically fall between 4 and 6 m, while roadway widths mostly range from 8 to 14 m. Building height estimates were derived from the Residential Design Code, which specifies a standard floor height of 2.8 m for ordinary residential buildings. For consistency with actual façade designs, a uniform value of 3 m per floor was adopted for estimation [35]. Street widths were measured using an infrared rangefinder. Statistical analysis revealed that the aspect ratios (H/W) of sampled streets were primarily distributed between 0.6 and 1.4, with orientations dominated by south–north (S–N) and east-northeast–west-southwest (ENE–WSW). These findings provide the baseline data for defining typical street morphologies and azimuth angles in the simulations.

Second, to obtain the microclimatic boundary conditions of streets, this study conducted meteorological monitoring using a Kestrel 5000 portable weather station (Kestrel Instruments, Nielsen-Kellerman, Boothwyn, PA, USA). This instrument offers high accuracy and durability, capable of real-time recording of key climatic variables such as wind speed, air temperature, relative humidity, atmospheric pressure, and altitude (technical specifications are provided in

Table 2. Table of instrument technical parameters.

Instrument Photos	Measurement Parameters	Measuring Range	Accuracy	Resolution
	Wind speed	0.6–40 m/s	±3%	0.1 m/s
	Air temperature	−29–70 °C	1 °C	0.1 °C
	Relative humidity	5–95%	3%	0.1%

). The monitoring was carried out on 1 August 2022, from 9:00 to 17:00, with data collected at 30 min intervals. Measurement points were set at a height of 1.5 m above ground level to correspond to human thermal perception height. On the survey day, air temperature ranged from 22 to 32 °C, with prevailing southerly winds at Beaufort force 3–4, consistent with typical hot-weather conditions in Shenyang (Figure 4).

Finally, to simulate the microclimatic regulation effects of greening configurations, structural parameters of street trees were collected. Field inventory showed that most livable streets in Shenyang’s old districts adopt single-tree pit planting, with common species including Yinzhong Poplar and the Pagoda Tree, with an average spacing of approximately 5 m. Previous studies have demonstrated that morphological characteristics such as tree height, crown width, clear bole height, and canopy density are closely related to changes in the street thermal environment [36]. Accordingly, this study conducted on-site measurements of these structural indicators for the major street tree species: crown width, clear bole height, and tree spacing were measured directly, while tree height was obtained using a laser rangefinder. Considering adaptability, representativeness, and data integrity, five common species were ultimately selected as simulation objects: the Pagoda Tree, Ginkgo, Golden Elm, Yinzhong Poplar, Chinese Willow (

Table 3. Basic information on street tree species in the typical livable streets of Shenyang.

	Street Name	Types of Roadside Trees	Street Tree Spacing (M)	Average Crown Size (M)	Average Tree Height (M)	Average Height Below Branches (M)
Heping District	Shiwuwei Road	Pagoda Tree	5	6.8	12.3	2.9
	Siping Street	Pagoda Tree	5	7.1	13.0	2.9
	Nansijing Street	Yinzhong Poplar	5	10.0	15.0	5.6
	Nanwujing Street	Pagoda Tree	5	6.3	11.7	2.6
	Zhenxing Street	Pagoda Tree	5	6.4	11.9	2.8
	Jiaxing Street	Yinzhong Poplar	6	6.5	12.0	2.8
	Yunji Street	Yinzhong Poplar	5	10.2	15.1	5.7
Tiexi District	Guihe Street	Yinzhong Poplar	5	8.6	13.8	3.8
	Beisizhong Road	Yinzhong Poplar	5	9.0	14.1	4.2
	Jingxingbei Street	Yinzhong Poplar	5	9.2	14.2	4.1
	Xingshun Street	Yinzhong Poplar	4	8.4	13.6	3.5
	Beisanzhong Road	Ginkgo	4	8.2	13.2	3.4
	Xiaobeisanzhong Road	Yinzhong Poplar	5	6.2	11.3	3.3
	Xiaobeisizhong Road	Yinzhong Poplar	5	8.4	13.7	3.6
	Xiaobeierzhong Road	Pagoda Tree	7	8.2	13.3	3.5
	Qixianbei Street	Ginkgo	4	8.4	13.7	3.5
Shenhe District	Changqing Road	Chinese Willow	4	7.3	13.2	3.1
	Fengle 3rd Street	Golden elm	5	6.5	11.8	3.6
	Fengle 1st Street	Chinese Willow	5	5.5	11.4	2.5
	Wenfu Road	Pagoda Tree	5	6.2	12.8	2.5
	Wencui Road	Yinzhong Poplar	5	5.4	11.2	2.4
	Nantadong Street	Yinzhong Poplar	5	6.8	12.2	2.8
	Wenfubei Road	Pagoda Tree	5	9.3	14.3	4.2
Huanggu District	Tianshan Road	Chinese Willow	5	8.7	13.8	3.8
	Yushan Road	Pagoda Tree	6	6.6	12.0	2.8
	Cangshan Road	Chinese Willow	6	6.4	12.1	2.7
	Jinshui Street	Pagoda Tree	6	8.6	13.8	3.9
	Minglian Road	Pagoda Tree	6	7.2	13.2	3.0
Dadong District	Liaoshen 3rd Street	Yinzhong Poplar	5	5.8	12.0	2.6
	Ruyi 2nd Road	Pagoda Tree	5	6.7	12.3	2.8
	Liaoshen 1st Street	Pagoda Tree	5	10.0	14.9	5.5
	Jixiang 4th Road	Yinzhong Poplar	5	9.1	14.2	4.0
	Xinsheng 2nd Street	Yinzhong Poplar	5	10.2	15.3	5.7
	Xinsheng 3rd Street	Yinzhong Poplar	5	5.4	11.3	2.5
	Liaoshen 2nd Street	Yinzhong Poplar	4	7.2	13.3	3.2
	Jixiang 2nd Road	Yinzhong Poplar	5	9.5	14.6	4.7

and

Table 4. Information on commonly used street tree species in northern China.

Tree Species	Average Crown Width (m)	Average Height (m)	Average Crown Base Height (m)
Sophora japonica (Pagoda Tree)	6.8	12.4	2.9
Ginkgo biloba (Ginkgo)	6.5	11.4	3.5
Ulmus pumila ‘Iinye’ (Golden Elm)	6.2	12.8	2.5
Populus alba × P. berolinensis (Yinzhong Poplar silver)	9.1	14.2	4.3
Salix matsudana (Chinese Willow)	5.7	11.3	2.5

). These species are widely applied in cities of cold regions and have been validated in previous studies, such as those conducted in Harbin, for their strong capacity to regulate microclimates and improve thermal comfort [37].

2.2.3. Human Thermal Comfort

This study adopts the Universal Thermal Climate Index (UTCI) as the primary indicator for assessing thermal comfort to reflect the thermal environmental effects of different street morphologies and street tree configurations. UTCI is currently one of the most widely applied comprehensive indices in outdoor thermal comfort research worldwide and is particularly suitable for urban microclimate analysis [38].

UTCI is defined as the equivalent air temperature under reference environmental conditions that would elicit the same human thermal stress response as the actual environment. The reference environmental conditions were defined such that the mean radiant temperature was equal to the air temperature, with a wind speed of 0.5 m s⁻¹ at a 10 m height and 50% relative humidity (with water vapor pressure capped at 20 hPa when Ta > 29 °C) [39]. UTCI employs a multi-node dynamic model that can simulate human physiological responses under varying thermal stress conditions, including sweating, shivering, and vasoconstriction/vasodilation. This makes UTCI more accurate in reflecting human thermal load under complex climatic conditions [40].

UTCI comprehensively integrates meteorological parameters such as air temperature, mean radiant temperature, wind speed, and relative humidity, while also accounting for human activity levels and clothing insulation. It thus offers stronger climatic adaptability and cross-regional applicability [39]. The UTCI values in this study were obtained using the BIO-met module in ENVI-met based on microclimate simulation outputs. Considering that the study area is a cold-region urban street environment subject to significant climatic variation, UTCI provides a more comprehensive means of evaluating the impacts of summer thermal environments on human thermal comfort.

According to the classification proposed by Bröde et al., thermal stress levels in UTCI are defined as follows: 9–26 °C, “no thermal stress”; 26–32 °C, “moderate heat stress”; 32–38 °C, “strong heat stress”; and above 38 °C, “extreme heat stress” [39]. The UTCI settings and classification standards adopted for the summer simulations in this study are presented in

Table 5. Table showing UTCI parameter setting information and the classification of UTCI.

Simulation Parameter	Assigned Value	UTCI Range (°C)	Thermal Perception	Physiological Stress Level
Gender	Male	<9	Very Cold	Strong Cold Stress
Age	35 years	9–26	Comfortable	No Thermal Stress
Height	1.75 m	26–32	Warm	Moderate Heat Stress
Weight	75 kg	32–38	Hot	Strong Heat Stress
Body Surface Area	1.91 m2	>38	Very Hot	Extreme Heat Stress
Walking Speed	1.34 m/s
Metabolic Rate (Met)	164.49 W/m2

.

To verify the consistency between model evaluation results and residents’ actual perceptions, a subjective thermal comfort questionnaire survey was conducted in parallel. Five streets with similar aspect ratios but different street tree species were selected as survey sites, namely Ruyi 2nd Road, Jixiang 2nd Road, Fengle 3rd Street, Fengle 1st Street, and Wenfu Road. The survey was carried out from 1 to 2 August 2022, during the period of 14:00–17:00, under stable environmental conditions. The mean daily temperature was 26.9 °C, relative humidity was 82.7%, average wind speed was 2 m/s, and the prevailing wind direction was southerly. Thirty questionnaires were collected per street, yielding a total of 150 responses. Field questionnaire survey scenes are shown in Figure 4, while the questionnaire design and detailed survey results are provided in Appendix B (Figure A1).

The questionnaire, designed with reference to previous studies on thermal environment surveys, consisted of two parts. The first part gathered personal information of respondents, including time, location, clothing, activity type, and duration of outdoor exposure. To match UTCI model parameters, the sample screening was standardized to male subjects aged 35 years (height: 1.75 m, weight: 75 kg). The second part assessed thermal comfort perception, including a five-point Thermal Comfort Vote (TCV): +2 (very comfortable), +1 (comfortable), 0 (neutral), −1 (uncomfortable), and −2 (very uncomfortable). Respondents were also asked to indicate the subjective factors they perceived as influencing their current thermal sensation [41] (

Table 6. Average thermal comfort votes for streets with different tree species.

Indicator	Golden Elm	Chinese Willow	Pagoda Tree	Yinzhong Poplar	Ginkgo
Average thermal comfort score	0	0	−1	−1	−2

and

Table 7. Distribution of perceived factors influencing outdoor thermal sensation.

	Air Temperature	Humidity	Wind Speed	Others
Proportion of perceived factors (%)	28%	16%	52%	4%
No. of questionnaires	42	24	78	6

).

2.3. Numerical Model Construction

To quantitatively evaluate the impacts of different street morphologies and street tree configurations on human thermal comfort, a three-dimensional urban microclimate numerical model was developed using ENVI-met v5.7. ENVI-met is a multi-scale urban climate simulation system based on non-hydrostatic computational fluid dynamics (CFD) principles. It dynamically couples processes of aerodynamics, heat transport, shortwave and longwave radiation exchange, and soil–vegetation–atmosphere interactions. The model has been widely applied in studies of urban heat island analysis, green infrastructure assessment, and microclimate interventions at the street scale [42,43].

2.3.1. Simulation Domain Parameters

A typical livable street in Shenyang was selected as the simulation object. The model domain was set to 100 m × 100 m × 50 m, with a grid resolution of 1 m in both horizontal and vertical directions, meeting the spatial resolution requirements for thermal comfort simulations. Model construction incorporated parameters such as street aspect ratio, orientation, ground surface materials, building density, and street tree configurations. To ensure consistency between simulations and field conditions, boundary meteorological inputs were derived from hourly observational data recorded on 1 August 2022, including air temperature, wind speed, and relative humidity, while solar radiation was obtained from ENVI-met outputs [44,45]. The simulation was initialized at 00:00 and ran for 24 h (

Table 8. The initial parameters of the ENVI-met calibration model simulations.

Parameter Settings	Value Setting
Simulation date	1 August 2022
Start time	00:00
Simulation duration/h	24
Wind speed at 10 m/s	2.0
Wind direction at 10 m	South wind
Initial temperature/°C	28.7
Parameter settings	Value setting
Initial humidity/%	60.6
2500 m relative humidity/(g.kg)	8
Cloud cover	None

).

Based on the combinations of two street orientations (S–N and ENE–WSW) and five aspect ratio categories (0.6–1.4), together with five representative street tree species, a total of 50 models were constructed to represent different spatial–vegetation configurations (

Table 9. 3D model construction information.

Street Aspect Ratio	Pagoda Tree	Ginkgo	Golden Elm	Yinzhong Poplar	Chinese Willow
0.6
0.8
1.0
1.2
1.4

). In all scenarios, both sides of the street were uniformly set as single-row tree-pit plantings with a fixed spacing of 5 m.

2.3.2. 3D Plant Modeling and Structural Parameter Setting

The default plant library (Albero) provided by ENVI-met is largely derived from temperate European species [17], whose leaf area index (LAI), crown base height, crown width, and photosynthetic parameters are not suitable for cold-climate regions in northeastern China. To ensure the regional adaptability of plant responses in the model, this study reconstructed three-dimensional plant models for five representative local street tree species based on field measurements and the characteristics of urban forestry in northern cold regions of China.

For each tree species, the following parameters were explicitly defined in the model: tree height, crown width, crown base height (CBH), LAI, and leaf area density (LAD) distribution functions across vertical layers. Because the ENVI-met modeling framework is based on a regular cubic grid system, crown dimensions must be set to odd numbers to align with grid cells and avoid spatial mismatches. Accordingly, the structural parameters of all selected tree species were standardized and are summarized in

Table 10. ENVI-met model of street tree species.

Plant Name	Filed Photograph	Plant Model	Adjusted Crown Width	Adjusted Height	Adjusted Clear Trunk Height
Pagoda Tree			7 m	12 m	3 m
Ginkgo			7 m	11 m	4 m
Golden Elm			5 m	13 m	3 m
Yinzhong Poplar			9 m	14 m	4 m
Chinese Willow			5 m	11 m	3 m

.

2.3.3. LAD Distribution Function and LAI Calibration

In ENVI-met, the thermal regulation capacity of tree canopies is determined by the vertical distribution of LAD. To represent realistic canopy structures while maintaining consistency in total LAI, this study adopted the empirical LAD vertical distribution function proposed by Fahmy [43], expressed as follows:

$$\mathrm{LAI}={{\displaystyle \int }}_0^{\mathrm{h}}\mathrm{LAD}\left(\mathrm{z}\right)\mathrm{dz}= {{\displaystyle \int }}_0^{\mathrm{h}}{\mathrm{L}}_{\mathrm{m}}{\left({\displaystyle \frac{\mathrm{h}-{\mathrm{z}}_{\mathrm{m}}}{\mathrm{h}-\mathrm{z}}}\right)}^{\mathrm{n}}\exp \left[\mathrm{n}\left(1-{\displaystyle \frac{\mathrm{h}-{\mathrm{z}}_{\mathrm{m}}}{\mathrm{h}-\mathrm{z}}}\right)\right]\mathrm{dz}$$

where h is tree height, z is any given height, ${\mathrm{z}}_{\mathrm{m}}$ is the height at which LAD reaches its maximum, ${\mathrm{L}}_{\mathrm{m}}$ is the maximum LAD value, and n is an exponential term controlling the rate of change in leaf distribution. The parameter n was segmented according to height as follows:

$$\mathrm{n}=\{\begin{array}{c}6, 0\le \mathrm{z}\le {\mathrm{z}}_{\mathrm{m}}\\ 0.5, {\mathrm{z}}_{\mathrm{m}}\le \mathrm{z}\le \mathrm{h}\end{array}$$

This function effectively simulates the gradient of leaf area density within the canopy, thereby accurately representing the capacity of different canopy structures to regulate solar radiation and aerodynamic properties. The total LAI values were determined by integrating field measurements from urban forestry studies in Beijing, Tianjin, and Harbin [46], calibrated for individual species and subsequently incorporated into the ENVI-met vegetation module (see Appendix A (

Table A1. Parameter settings of leaf area of roadway in ENVI-met software.

Plant Name	Height	Crown Width	LAD 1	LAD 2	LAD 3	LAD 4	LAD 5	LAD 6	LAD 7	LAD 8	LAD 9	LAD 10
Pagoda Tree	12	7	0.00	0.00	0.24	0.32	0.40	0.44	0.41	0.35	0.31	0.22
Ginkgo	11	7	0.00	0.42	0.56	0.77	0.90	1.16	0.98	0.92	0.74	0.30
Golden Elm	13	5	0.00	0.00	0.12	0.19	0.24	0.28	0.23	0.19	0.13	0.10
Yinzhong Poplar	14	9	0.00	0.00	0.16	0.23	0.28	0.32	0.27	0.23	0.17	0.14
Chinese Willow	11	5	0.00	0.21	0.32	0.41	0.46	0.54	0.48	0.33	0.26	0.15

)).

Through this modeling procedure, the ENVI-met microclimate model developed in this study achieves strong regional adaptability in terms of structural hierarchy, spatial resolution, and physiological parameterization, providing a robust basis for quantitative simulations and visualization of the thermal comfort contributions of different street tree species.

2.3.4. Model Validation

To evaluate the accuracy and reliability of the ENVI-met microclimate simulation results, this study employed the root mean square error (RMSE) and the coefficient of determination (R²) as the primary statistical validation indices, comparing simulated data with field measurements. RMSE is commonly used to quantify the deviation between predicted and observed values; a smaller RMSE indicates a closer agreement between simulation outputs and actual measurements, and hence a higher model accuracy [47,48]. The formula for RMSE is expressed as follows:

$$\mathrm{RMSE}=\sqrt{{\displaystyle \frac{1}{\mathrm{n}}}{\displaystyle \sum }_{\mathrm{i}=1}^{\mathrm{n}}{\left({\mathrm{y}}_{\mathrm{i}}^{*}-{\mathrm{y}}_{\mathrm{i}}\right)}^2}$$

where ${\mathrm{y}}_{\mathrm{i}}^{*}$ is the simulated value, ${\mathrm{y}}_{\mathrm{i}}$ is the observed value, and n is the sample size.

R² is a statistical indicator that evaluates the goodness of fit between predicted and observed values, ranging from 0 to 1, with values closer to 1 indicating better model performance. It is typically calculated based on the least-squares linear regression model, as expressed below:

$${\mathrm{R}}^2=1-{\displaystyle \frac{\mathrm{SSE}}{\mathrm{SST}}}=1-{\displaystyle \frac{\Sigma \left(\mathrm{yi}-\stackrel{-}{\mathrm{y}}\right)^2}{\Sigma \left(\mathrm{yi}-\hat{\mathrm{y}}\mathrm{i}\right)^2}}$$

where, ${\mathrm{y}}_{\mathrm{i}}$ is the observed value, $\stackrel{-}{\mathrm{y}}$ is the mean of observations, and $\hat{\mathrm{y}}\mathrm{i}$ is the predicted value. To avoid spurious improvements in model performance caused by the inclusion of multiple independent variables, this study further adopted the adjusted coefficient of determination (Adjusted R²) for correction [47], defined as follows:

$$\mathrm{Adjusted} {\mathrm{R}}^2=1-\left[\left(1-{\mathrm{R}}^2\right)*{\displaystyle \frac{\left(\mathrm{n}-1\right)}{\left(\mathrm{n}-\mathrm{k}-1\right)}}\right]$$

where k is the number of independent variables in the model, and n is the total sample size.

2.4. Field Validation Design and Instrument Calibration

2.4.1. Field Validation Site and Measurement Layout

For model validation, Shisiwei Road in Heping District, Shenyang, was selected as the field measurement site. The street has an aspect ratio of 1.0 and a spatial layout characterized by a “one-plate–two-belt” structure, with relatively rich greening features. Most segments employ a belt-style street tree configuration, while some sections adopt a tree-pit layout. The dominant tree species is the Pagoda Tree, with a smaller presence of Chinese Willow. Buildings along both sides of the street are predominantly residential, with well-developed supporting facilities. The street experiences high pedestrian density, with approximately 80% of users being residents (Figure 5).

To ensure representativeness and diversity in sampling, four measurement points were established along Shisiwei Road, located within sidewalks and roadside greenbelts. Measurement Point 1 was placed at the center of the sidewalk, while Points 2–4 were in the greenbelt areas on both sides of the street. These points primarily record microclimatic variables such as temperature, humidity, and wind speed, providing essential data for validating the ENVI-met simulations against field measurements.

2.4.2. Instrument Calibration and Data Consistency Control

To guarantee the accuracy of field measurements, this study adopted the instrument calibration procedure applied by Zhang et al. in their field campaign in Nanjing [4,49]. Prior to monitoring, all instruments were calibrated under uniform conditions. As illustrated in Figure 6, instruments were operated in parallel in the same environment, and minor adjustments were made according to deviations in air temperature and relative humidity. Calibration was completed once the inter-instrument temperature differences were within 0.3 °C and relative humidity differences within 5%.

Finally, the field measurements were compared with ENVI-met simulation outputs using RMSE and R² to evaluate the model’s goodness of fit, thereby assessing the applicability and accuracy of the model in simulating thermal environments along urban livable streets.

2.5. Statistical Analysis

To enhance the robustness of the results, paired difference tests were applied to the simulated outputs. First, under the same street morphology, interspecies differences were quantified by calculating paired differences:

$$△{\mathrm{UTCI} = \mathrm{UTCI}}_{\mathrm{speciesA}} -{\mathrm{UTCI}}_{\mathrm{speciesB}}$$

When the paired differences were approximately normally distributed, a paired-sample t-test was used to test the null hypothesis that the mean difference equals zero; the Wilcoxon signed-rank test was additionally applied as a robustness check.

To evaluate street-morphology effects, orientations were compared under the same species and the same H/W using the following:

$$△{\mathrm{UTCI} = \mathrm{UTCI}}_{\mathrm{SE}\text{-}\mathrm{NW}} -{ \mathrm{UTCI}}_{\mathrm{E}\text{-}\mathrm{W}}$$

In addition, within each orientation, H/W effects were assessed relative to the reference condition, H/W = 1.2, by calculating the following:

$$△{\mathrm{UTCI} = \mathrm{UTCI}}_{\mathrm{H}/\mathrm{W}}-{\mathrm{UTCI}}_{1.2}$$

All tests were two-sided, with a significance level of α = 0.05.

3. Results

3.1. ENVI-Met Model Validation

To validate the accuracy of the simulation results, correlation analyses were conducted between ENVI-met outputs and field measurements. Figure 7 presents the scatter plots of simulated versus observed air temperature and relative humidity, where the X-axis represents measured values, and the Y-axis represents model outputs. The results indicate that simulated values increased consistently with observed values, showing an overall linear positive correlation.

For temperature simulations, R² values ranged from 0.83 to 0.95, with root mean square error (RMSE) values between 1.96 °C and 3.77 °C. For humidity simulations, R² values ranged from 0.88 to 0.95, with RMSE values between 2.14% and 3.04%. The distribution of data across measurement points demonstrates a strong agreement between simulated and observed values, with particularly stable prediction performance in the moderate thermal load range.

It is noteworthy that during extreme high-temperature periods (>30 °C), simulated values at certain points were slightly higher than the observations, which may be related to thermal inertia assumptions in the model under high-temperature boundary conditions. Overall, ENVI-met exhibited strong responsiveness to variations in air temperature and relative humidity at the street scale. Its outputs showed high consistency with field data in both spatial distribution and numerical accuracy, confirming that ENVI-met can effectively simulate the microclimatic characteristics of livable streets under typical summer conditions and is therefore suitable for subsequent quantitative analyses of the thermal comfort regulation effects of street tree species.

3.2. Street Morphology and Thermal Comfort Simulation Results

The simulation results indicate that street aspect ratio and orientation exert significant effects on human thermal comfort (Figure 8). Across all aspect ratio combinations, streets oriented southeast–northwest (SE–NW) exhibited overall higher thermal comfort levels compared with those oriented east–west (E–W). Under the SE–NW orientation, the ranking of aspect ratios in terms of thermal comfort improvement was: 1.2 > 1.4 > 0.8 > 1.0 > 0.6. For the E–W orientation, the order was: 1.2 > 0.8 > 1.0 > 0.6 > 1.4. Streets with an aspect ratio of 1.2 consistently demonstrated the best thermal comfort in both orientations, yielding the lowest UTCI values and the most stable comfort range (

Table 11. Statistical table of street aspect ratio recommended for planting various tree species.

Tree Species	SE–NW Orientation	E–W Orientation
Pagoda Tree	0.8	1.0
Ginkgo	0.6	0.6
Golden Elm	1.2	1.4
Yinzhong Poplar	1.0	0.8
Chinese Willow	1.4	1.2

). Paired tests further confirmed the above findings, as shown in Appendix A (

Table A2. Paired statistical comparisons of street orientation and aspect ratio effects on mean UTCI.

Effect	Grouping	ΔUTCI Definition	n (Paired Units)	Mean ΔUTCI (°C)	95% CI (°C)	p-Value
Orientation	All H/W (0.6–1.4) × all species	SE–NW − E–W	25	−2.41	−3.14 to −1.69	<0.001
Aspect ratio (vs 1.2)	SE–NW	H/W = 0.6 − H/W = 1.2	5	1.1	0.53 to 1.68	0.006
Aspect ratio (vs 1.2)	SE–NW	H/W = 0.8 − H/W = 1.2	5	0.57	0.20 to 0.94	0.013
Aspect ratio (vs 1.2)	SE–NW	H/W = 1.0 − H/W = 1.2	5	0.58	0.52 to 0.65	<0.001
Aspect ratio (vs 1.2)	SE–NW	H/W = 1.4 − H/W = 1.2	5	0.45	0.12 to 0.78	0.020
Aspect ratio (vs 1.2)	E–W	H/W = 0.6 − H/W = 1.2	5	0.7	0.10 to 1.31	0.032
Aspect ratio (vs 1.2)	E–W	H/W = 0.8 − H/W = 1.2	5	0.33	−0.08 to 0.75	0.090
Aspect ratio (vs 1.2)	E–W	H/W = 1.0 − H/W = 1.2	5	0.46	0.41 to 0.50	<0.001
Aspect ratio (vs 1.2)	E–W	H/W = 1.4 − H/W = 1.2	5	0.7	0.26 to 1.14	0.011

Notes: Orientation effect is ΔUTCI = UTCI(SE–NW) − UTCI(E–W), computed from paired comparisons across all species × H/W (n = 25). Aspect ratio effect is ΔUTCI = UTCI(H/W) − UTCI(1.2) within each orientation, paired by species (n = 5). Positive ΔUTCI indicates warmer conditions (worse thermal comfort). p-values are from two-sided paired t-tests.

).

For example, with Golden Elm, the simulated UTCI under the SE–NW orientation with an H/W = 1.2 was 25.4 °C, representing an improvement of 1.7 °C compared with H/W = 0.6 (27.1 °C) for the same species. Under the E–W orientation, Chinese Willow at H/W = 1.2 achieved a UTCI of 25.9 °C, 1.4 °C lower than at H/W = 1.4 (27.3 °C). Similarly, for Yinzhong Poplar, H/W = 1.2 consistently yielded superior results, with temperature improvements ranging from 0.7 to 1.2 °C across different scenarios.

3.3. Comparison Between Tree Species Thermal Comfort Simulations and Subjective Questionnaire Results

To further validate the reliability of the simulations, UTCI outputs from ENVI-met were compared with residents’ TCVs obtained from the field questionnaire survey. The results reveal significant differences in the thermal comfort regulation capacity among different street tree species, with subjective evaluations showing strong consistency with simulation trends (Figure 4).

Streets planted with Ginkgo were rated as “uncomfortable” by most respondents, consistent with simulation results showing that their UTCI values were generally higher than those of other species. By contrast, streets with Golden Elm and Chinese Willow exhibited lower simulated UTCI values (25.4 °C and 25.9 °C, respectively) and correspondingly received a higher proportion of “comfortable” and “very comfortable” ratings. Streets with the Pagoda Tree and Yinzhong Poplar were predominantly rated as “neutral”, with simulated UTCI values in the range of 26.5–27.5 °C, suggesting a moderate regulation capacity for these species (Figure 9).

In addition, an analysis of influencing factors reported by respondents indicated that wind speed, air temperature, and relative humidity were the three most critical variables, with wind speed identified as the most significant regulatory factor. This finding aligns with the simulation results: in SE–NW-oriented streets with relatively favorable ventilation conditions, subjective thermal comfort ratings were clearly higher than those in E–W-oriented streets.

It is noteworthy that some respondents also reported that commercial activity density and building enclosure influenced their thermal experiences. In street segments with high concentrations of commercial functions and pedestrian flows, even well-designed tree configurations were sometimes offset by localized heat sources and reduced air circulation, leading to discrepancies in subjective thermal perception. This suggests that relying solely on physical microclimatic parameters in simulations may overlook thermal disturbances introduced by human activities and socio-spatial structures.

4. Discussion

4.1. Effects of Street Morphology on Microclimate Regulation Mechanisms

The geometric characteristics of urban streets, particularly aspect ratio (H/W) and orientation, are fundamental physical variables that shape street-level microclimates. Previous studies have shown that street morphology profoundly influences the spatial distribution of thermal environments and human thermal comfort responses by regulating the angle of solar radiation entry, local airflow dynamics, and pathways of heat release from building thermal mass [49,50].

Under typical summer conditions, this study shows that the optimal street morphology for cities in a severe-cold climate zone is achieved through a balance between radiative control and ventilation (

Table 12. Comparison of thermal comfort adjustment ability of different tree species under different street orientations and different street aspect ratios.

Tree Species	SE–NW Orientation (Ranking of H/W)	E–W Orientation (Ranking of H/W)
Pagoda Tree	1.2 > 1.4 > 0.8 > 1 > 0.6	1.2 > 0.8 > 1.0 > 0.6 > 1.4
Ginkgo	1.2 > 0.8 > 0.6 > 1 > 1.4	1.2 > 0.8 > 0.6 > 1.0 > 1.4
Golden Elm	1.2 > 1.4 > 1 > 0.8 > 0.6	1.2 > 1.4 > 1.0 > 0.8 > 0.6
Yinzhong Poplar	1.2 > 1.4 > 1 > 0.8 > 0.6	1.2 > 0.8 > 1.0 > 0.6 > 1.4
Chinese Willow	1.2 > 1.4 > 1 > 0.8 > 0.6	1.2 > 1.4 > 1.0 > 0.8 > 0.6

). In particular, streets with H/W = 1.2 exhibit thermal-comfort advantages across multiple spatial scales and demonstrate the strongest UTCI reduction in the simulations. This finding differs from the “low H/W first, ventilation-dominated” strategy emphasized in some studies of tropical cities [24] and also from conclusions in hot–dry environments that highlight deeper street canyons for reducing radiative load through shading [23,49]. These results indicate that under Shenyang-like mid-latitude monsoonal conditions, a more effective configuration should simultaneously constrain excessive solar/radiative exposure while avoiding substantial ventilation weakening.

Comfort: We quantitatively evaluated the relationship between street-axis direction and the solar path, and the results show that an SE–NW axis can reduce direct solar exposure at pedestrian height from noon to mid-afternoon. Consequently, MRT is markedly lower than that of an E–W orientation, reflecting a reduced radiative burden; thus, street orientation acts as a first-order control factor governing solar exposure and thermal comfort. This conclusion is consistent with related studies across different climates and urban scales [18,19,20], suggesting a coupled relationship between spatial orientation and pathways of heat-load transmission. In addition, the relative alignment of the SE–NW axis with the prevailing summer wind direction may strengthen the wind-corridor effect, thereby jointly determining the transport and dissipation efficiency of thermal loads, together with the radiative pathway.

In our results, the UTCI difference for the same tree species across different spatial configurations reaches up to 1.5 °C, confirming that street structure can amplify or attenuate vegetation effects associated with shading, ventilation, and evapotranspiration. Therefore, street morphology should be treated as a primary parameter within the thermal-environment intervention framework, and its roles in shading, ventilation, and thermal-inertia regulation should be reconceptualized and designed within a coupled “space–climate–vegetation” mechanism.

4.2. Structural Differences of Street Tree Species and Their Multidimensional Regulatory Capacity

Previous studies have generally acknowledged that different tree species exhibit significant variation in their ability to regulate the thermal environment of streets. Such differences are primarily determined by key parameters including crown width, LAD distribution, transpiration potential, and crown base height [36,51]. However, most existing research has focused on tropical or subtropical regions, while structural analyses of species-specific regulation mechanisms under cold-climate conditions remain limited.

Based on ENVI-met simulations, this study evaluated the thermal comfort regulation performance of five common street tree species in temperate monsoon cities under various combinations of aspect ratio and street orientation (Figure 10). The results demonstrate that UTCI improvement effects differ markedly across species and exhibit strong spatial dependency. For example, Golden Elm reduced UTCI by 1.38 °C in SE–NW-oriented streets with H/W = 1.2, whereas its improvement effect was less than 0.5 °C in E–W-oriented streets with H/W = 0.6. This indicates that spatial configuration may amplify or suppress the regulatory capacity of specific tree species, extending the “right tree for the right place” principle proposed in street-canyon optimization research [52]. It further suggests that street morphology, as a boundary framework, determines the extent to which canopy-structure parameters of a given species can be translated into thermal-comfort benefits [26].

Further analysis revealed that tree species with compact crowns, stable LAD distributions, and moderate crown base heights were better adapted to street spaces characterized by medium ventilation and higher solar altitude angles. In contrast, species with convergent crown forms or excessively low crown base heights, for example, Ginkgo, showed limited thermal regulation performance in both highly open and highly enclosed streets. This finding is consistent with Li’s conclusion from field experiments in Nanjing that “greater leaf mass ≠ greater comfort” and extends the theoretical framework of “species–space adaptability” to cold-climate contexts [48].

Moreover, certain high-transpiration species performed poorly in enclosed streets, suggesting that transpiration potential alone is insufficient to overcome heat retention effects. Instead, the matching between tree structural traits and street morphology is confirmed as a prerequisite for enhancing thermal comfort [7]. While most existing studies have focused on single-variable species optimization [8,52,53,54], this study established a multidimensional evaluation logic dominated by spatial parameters, emphasizing a structure-first pathway in which vegetation responses are constrained by the spatial framework. Consequently, species selection should not be considered in isolation from street geometry but incorporated into an integrated space–vegetation coupling regulation model. By addressing the specific conditions of cold regions, this study proposes tree species adaptation strategies tailored to different combinations of H/W and street orientation, thereby providing an operational theoretical foundation for structurally informed urban greening design.

4.3. Consistency Between Subjective Thermal Perception and Simulation Results, and Sources of Deviation

In urban thermal comfort research, the degree of consistency between simulated indices and subjective perceptions is a key criterion for evaluating model reliability and practical applicability. Previous studies have shown that widely used thermal comfort indices such as UTCI can quantify the combined effects of radiation, air temperature, humidity, and wind speed; however, they remain limited in their capacity to capture subjective dimensions such as psychological expectations, behavioral adaptation, and socio-spatial factors [47,55,56].

In this study, UTCI distributions simulated by ENVI-met for five tree species under different spatial configurations were compared with residents’ TCVs, collected under the same temporal and spatial conditions. The comparison revealed a high degree of consistency in trend judgments, particularly when UTCI < 26 °C, where TCV scores were concentrated in the “comfortable” and “very comfortable” categories, showing good agreement. For example, in the configurations of Golden Elm (SE–NW and H/W = 1.2) and Chinese Willow (E–W and H/W = 1.2), deviations between UTCI and TCVs were less than 0.5 °C or within one score level, indicating strong explanatory power of the model under thermal comfort optimization scenarios.

However, discrepancies emerged in borderline cases within the “moderate heat stress” range (26–29 °C). In some street segments, especially in commercial zones or high pedestrian density areas, respondents reported feeling “slightly hot” or “uncomfortable” despite relatively low UTCI values. This suggests that non-meteorological factors such as crowding, noise, and traffic disturbance interfered with thermal perception, consistent with the concept of behavioral thermal adaptation and expectation bias [57]. It is worth noting that this study did not incorporate individual thermal adaptation variables like gender, age, and clothing insulation or behavioral parameters like activity intensity and exposure duration, which may have contributed to localized prediction errors. Future work could improve subjective alignment by incorporating individual correction factors into the UTCI framework [58,59]. The ENVI-met simulation results demonstrated a high level of consistency with subjective survey data, particularly in structurally optimized scenarios where predictive performance was strong. Nonetheless, in human-activity-intensive environments, subjective perception biases must be carefully considered, highlighting the need for future thermal comfort modeling to evolve from single meteorological responses toward integrated meteorology, society and behavior frameworks [3].

In addition, the survey results indicate that respondents perceived wind speed as the most influential factor affecting thermal sensation. This suggests that under humid monsoonal summer conditions, wind is critical for convective and evaporative heat dissipation at the pedestrian level, and that a street orientation aligned with the prevailing wind direction can still yield stronger perceived-comfort benefits [26].

4.4. Integrated Strategy Recommendations for Greening Optimization in Cold-Region Streets

Based on the findings regarding street spatial configuration, species-specific microclimate regulation mechanisms, and the consistency between subjective thermal perception and simulation outputs, this study proposes an optimization framework for street-scale greening temperate monsoon cities. The framework emphasizes structure–function matching between spatial morphology and vegetation configuration, with simulation-driven analysis and human-centered feedback as the core strategy for enhancing the systemic regulation capacity of greening interventions on an urban thermal environment.

It is recommended that the street aspect ratio be maintained within the range of 1.0–1.2 to balance shading coverage with ventilation accessibility. Street orientations forming a 15–45° angle with prevailing summer wind directions can effectively enhance wind-driven air exchange and synergize with vegetation transpiration potential. Extreme openness or excessive enclosure should be avoided, as both may exacerbate thermal stress through either heat accumulation or loss of cooling capacity. In streets with moderate aspect ratios and balanced ventilation–radiation characteristics, priority should be given to tree species with high LAD, moderate crown spread, and relatively high CBH, such as Golden Elm and Chinese Willow. These species demonstrated superior UTCI cooling effects in simulations, supported by efficient transpiration and stable shading performance. Conversely, species with convergent crown forms, such as Ginkgo, performed poorly under most spatial conditions, and their use should be limited in high-thermal-load street segments to avoid reduced microclimate benefits. Greening strategies should move beyond relying on canopy coverage as a single proxy for thermal regulation capacity. Instead, a tripartite coupling mechanism—“spatial morphology–canopy structure–microclimate response”—should guide the development of a species parameter database. Early design stages are encouraged to integrate three-dimensional simulation tools such as ENVI-met or SOLWEIG for rapid iterative evaluations of space–species combinations, thereby generating ranking-based recommendations for species selection and planting layouts under thermal comfort optimization targets [60].

Resident behavioral characteristics like daily duration of stay, activity frequency, and temporal patterns should be incorporated into thermal regulation frameworks, advancing toward an integrated “meteorology–structure–behavior” configuration model. While consistency between model results and TCVs has been validated, caution is warranted in commercial zones and high-footfall nodes where subjective thermal biases may arise. Real-time correction through dynamic monitoring and multi-variable modeling is necessary to ensure adaptive local regulation.

4.5. Limitations and Further Study

Although this study systematically developed a coupled simulation framework linking street morphology, vegetation structure, and thermal comfort responses for cold-region urban streets and validated the street tree–street morphology adaptation mechanisms across multiple spatial scenarios, several limitations remain that constrain the generalizability of the results and the universality of the model.

First, regarding model inputs, the vegetation parameters in ENVI-met rely on static three-dimensional structures and simplified LAD settings, which do not capture the dynamic canopy evolution across seasons, growth stages, and pruning conditions. This limitation reduces the model’s ability to reflect physiological changes in tree species. Future studies are encouraged to incorporate high-resolution LiDAR data or multi-temporal UAV remote sensing imagery to construct dynamically updated vegetation structure models, thereby improving spatial–physiological consistency [61,62,63].

Second, the meteorological boundary conditions in this study were selected to represent an extremely hot summer day. While representative, this approach does not reveal the complete seasonal variability of street-level microclimate responses. Future research should establish sequential simulations based on multi-period meteorological datasets covering the entire year, enabling the identification and modeling of annual thermal comfort patterns, critical transition thresholds, and intra-annual spatial heterogeneity [51,64].

Third, in terms of subjective thermal validation, questionnaire sampling was limited by temporal and spatial constraints, with relatively narrow respondent coverage [65]. Individual attributes such as gender, age, clothing insulation, and activity intensity were not incorporated, which restricted multidimensional identification of subjective–objective discrepancies in thermal perception. Future studies should combine wearable sensors with multi-source behavioral data to develop integrated datasets linking behavior, physiology, and environment [38,66].

In addition, the model has not yet incorporated non-vegetation thermal environment variables such as surface albedo, the thermal storage capacity of building envelopes, and plant evapotranspiration potential [67]. Nor has it conducted multi-objective trade-off analyses of greening strategies in terms of construction cost, lifecycle carbon balance, and management feasibility [68,69]. Future research should therefore establish a multi-variable-driven integrated evaluation framework linking urban microclimate, ecology, and economics to enhance policy relevance and cross-sector applicability. As an exploratory attempt to simulate street greening and thermal comfort in a cold-climate context, this study has preliminarily constructed an integrative framework combining mechanistic logic with spatial structure. Future work should further advance cross-scale modeling of street-level microclimate regulation, temporal tracking of structural parameters, and integration of non-meteorological factors, thereby supporting the scientific deployment and efficient management of green infrastructure within complex urban climate systems [70].

5. Conclusions

Based on the livable street environment of Shenyang, a representative cold-climate city, this study developed a multidimensional evaluation framework integrating street morphology, street tree structural parameters, and UTCI. Using the ENVI-met model, 50 space–species scenarios were constructed, and field questionnaire survey data were incorporated to systematically assess the thermal comfort regulation capacity of different street tree species under varying aspect ratios and orientations.

Street morphology imposes fundamental constraints on microclimate regulation. Streets with an aspect ratio of 1.2 and SE–NW orientation achieved the optimal balance between ventilation and shading, yielding the lowest simulated UTCI values. Street tree species exhibited significant spatially dependent differences. Golden Elm and Chinese Willow showed the strongest regulatory performance in structurally compatible street contexts, while Ginkgo displayed limited regulation capacity, reflecting spatial maladaptation. High consistency was observed between simulated indices and subjective perceptions. UTCI simulations closely matched TCVs in most scenarios, confirming the applicability of ENVI-met for cold-region street thermal studies. Nonetheless, perceptual deviations occurred in high-density or mixed-use street segments. An integrated optimization strategy was proposed. The “spatial dominance–species adaptability–human-centered feedback” framework provides an operational system of greening configuration guidelines for cold-climate urban streets.

This study offers quantitative theoretical support and empirical simulation evidence for understanding the thermal regulation mechanisms of street greening in temperate monsoon cities. Future research should extend to multi-seasonal, long-term, and cross-scale modeling; strengthen integration of subjective and objective datasets; and incorporate economic and carbon trade-off analyses to support more resilient and precise deployment of urban green infrastructure.