Landscape and Ecological Benefits Evaluation of Flowering Street Trees Based on Digital Technology: A Case Study in Shanghai’s Central Urban Area, China

Abstract

Flowering street trees are important carriers of urban landscapes and ecological functions, as well as a significant boost to the construction of “Shanghai Flower City”. Most existing studies focus on the ornamental value or single ecological benefits, and there are insufficient systematic evaluations of the landscape–ecology synergistic effect, especially as there are few quantitative studies on the landscape value during the flowering period and long-term ecological benefits. Scientific assessment of multiple benefits is of great significance for optimizing tree species allocation and enhancing the sustainability of road landscapes. Taking flowering street trees in Shanghai’s central urban area as a case study, this paper verifies the feasibility of using digital technology to evaluate their landscape and ecological benefits and explores ways to enhance these aspects. Landscape, ecological, and comprehensive benefits were quantitatively assessed using digital images, the i-Tree model, and the entropy-weighted method. Influencing factors for each aspect were also analyzed. The results showed the following: (1) Eleven species or cultivars of flowering street trees from six families and ten genera were identified, with the majority flowering in spring, fewer in summer and autumn, and none in winter. (2) The landscape benefits model was: Scenic Beauty Estimation (SBE) = −0.99 + 0.133 × Flowering branches+ 0.183 × Degree of flower display + 0.064 × Plant growth + 0.032 × Artistic conception + 0.091 × Visual harmony with surrounding elements. Melia azedarach L., Prunus × yedoensis ‘Somei-yoshino’, and Paulownia tomentosa (Thunb.) Steud. ranked highest in landscape benefits. (3) Catalpa bungei C. A. Mey., Koelreuteria bipinnata Franch., and Koelreuteria bipinnata ‘integrifoliola’ (Merr.) T.Chen had the highest plant height, diameter at breast height (DBH), and crown width among the studied trees, and ranked top in ecological benefits. (4) Koelreuteria bipinnata, Catalpa bungei, and Melia azedarach showed the best overall performance. The comprehensive benefits model was: Comprehensive Benefits = 0.6889 × Ecological benefits + 0.3111 × Landscape benefits. This study constructs a digital evaluation framework for flowering street trees, quantifies their landscape and ecological benefits, and provides optimization strategies for the selection and application of flowering trees in urban streets.

1. Introduction

Flowering street trees, as a category of street trees, play a significant role in enhancing urban streetscapes by delivering multifunctional services [1], including aesthetic enrichment [2], microclimate regulation [3,4], biodiversity support [5,6], and psychosocial well-being [7,8], which collectively improve environmental resilience and human-centric livability in dense metropolitan areas. Studies consistently reveal a pronounced preference for landscapes incorporating flowering plants. Following visits to the Morton Arboretum in Illinois, a majority of respondents identified captivating vistas featuring vibrant floral displays as particularly impressive [9,10]. Similarly, Japanese residents demonstrated a strong interest in floral elements, with Kobe residents notably prioritizing the preservation of parks featuring formal flower beds [11]. Collectively, these findings suggest a robust human preference for landscapes enhanced by flowering vegetation. Therefore, flowering street tree landscapes have attracted growing attention within the urban planning and landscape architecture domains.

Currently, China has entered a new phase of integrating digital technology into road landscape development. Most objectives can be supported by large volumes of data and the technical capabilities of digital technology, which enable the collection, analysis, evaluation, and visualization of infrastructure performance and environmental information [12]. In the context of landscape planning and design, digital technology is generally applied in three areas: information collection, analysis and evaluation, and visualization [13]. Many scholars have adopted digital technology to integrate and enhance landscapes, introducing new dimensions to existing approaches in landscape construction and management.

For data acquisition, digital platforms are extensively used in cultural heritage conservation due to their efficiency and systematic nature, as demonstrated by the digitization of Dunhuang’s Mogao Grottoes and the Virtual Forbidden City [14]. Regarding analysis and evaluation, data collected through wired and wireless technology are processed using artificial intelligence algorithms to assess images, numerical data, and other inputs, thereby evaluating landscape, ecological, and cultural value [15]. Some have used digital images and machine learning to extract the color characteristics of roads and analyze their emotional impact on tourists [16]. Others have applied street-view images to quantify subjective perceptions of road landscapes, identifying the key visual factors (e.g., greenery and architectural aesthetics) that influence user satisfaction, thereby providing data-driven insights for urban design optimization [17,18,19,20]. In terms of visualization, landscape operations, management, and public engagement are increasingly supported by automation, virtual reality, and landscape information modeling (LIM) technology [21]. Digital technology models such as InVEST, CITYgreen, i-Tree, and ENVI-met have been recommended for scientific and forward-looking visual analytical simulations of urban street tree planning [22,23,24,25]. Among these, CITYgreen and i-Tree are the most widely used. i-Tree, developed by the U.S. Forest Service, is recognized as a leading tool for forestry analysis and ecological benefit assessment. The i-Tree Streets model focuses on urban ecosystem services and the structure of street tree communities. By creating per-tree data entries and calibrating local characteristics and computational inputs, the model can quantify ecological benefits, such as carbon sequestration and pollutant reduction, in monetary terms. This enhances both timeliness and relevance while reducing labor and time costs. For example, Sui Qingyu et al. used the i-Tree and ENVI-met models to evaluate ecosystem services provided by urban street trees in Shenyang’s Shengjing Historic and Cultural Block [26]. i-Tree has also been applied in scenario planning to assess the benefits of street trees under different future development scenarios [27].

In recent years, Shanghai has given priority to optimizing roadside planting, with the aim of creating colorful and fragrant urban streets under the “Shanghai Flower City” initiative. As the central urban area in Shanghai, street trees directly influence the city’s overall visual image. In 2019, the Shanghai Municipal Administration of Greening and Amenities issued the “Four planning outlines of Shanghai park green space”, which clearly points out the existing problems of urban road greening construction and quality improvement requirements, including focusing on urban road coloring construction, aroma construction, and so on. Flowering street trees, a key element of road landscapes, have received particular focus. Beyond general ecological benefits such as air purification and CO₂ absorption, flowering street trees offer additional value through their vivid colors and distinctive scents, contributing positively to both aesthetics and public well-being. Several studies in Shanghai have examined tree selection [28,29,30], current applications [31,32], and safety assessments [33]. Zhu Yun analyzed tree planning and species selection [34], Zhong Junjun investigated plant applications [35], and Sun Haolei conducted landscape evaluations [36]. Others, including Yang Haixia [37], Hu Mou [38], and Kan Liyan [39], studied the role of plants in pollutant reduction.

Scholarly inquiry into flowering street trees by international researchers encompasses plant selection criteria and application methodologies [40,41,42]. Relative to Chinese scholarship, international studies exhibit a pronounced emphasis on identifying the preferences and recommendations pertaining to street tree selection among diverse demographic groups [43,44]. Furthermore, significant research efforts are directed towards landscape perception and assessment [45], as well as investigations into ecological service provision [46,47,48]. However, a notable research gap persists both internationally and domestically: studies specifically targeting flowering street trees remain limited. Existing research primarily addresses biodiversity outcomes [49] and ecological benefits [50]. Crucially, the field currently lacks a comprehensive analytical framework capable of systematically evaluating and visualizing the integrated landscape and ecological benefits conferred by flowering street trees.

This study investigates the current utilization of flowering street trees within Shanghai’s central urban districts. The primary objectives are threefold: (1) to construct a novel evaluation framework for flowering street tree landscapes and their ecological benefits, (2) to develop an evaluation model of landscape, ecological, and comprehensive benefits by integrating the methodology of “digital technology–benefit evaluation” based on digital street-view images [51,52,53] and ecological data-processing techniques [54,55], and (3) to explore perceptual differences regarding flowering tree aesthetics among diverse demographic groups and to analyze growth parameters in relation to ecological benefits. The findings aim to provide a scientific basis for optimizing flowering street tree landscapes and ecological design. This optimization seeks to address residents’ growing need for proximal access to nature and enriched aesthetic experiences, thereby contributing to enhanced public well-being. Critically, this research adopts a data-driven framework featuring an equitable and standardized digital assessment. This framework is designed to provide replicable digital evaluation pathways for the precise management and sustainable planning of roadside vegetation in diverse regions, particularly biodiversity hotspots. Ultimately, this study seeks to increase street landscape diversity and seasonal balance, promoting the sustainability of urban road landscapes and associated ecosystem services, which are both academically innovative and practically popularized.

2. Materials and Methods

2.1. Study Site

Shanghai, located at the estuary of the Yangtze River in eastern China, is a major first-tier city. In this study, by utilizing the basic road data from the Shanghai public data platform and digital street-view images, flowering street trees in the green belts of 213 roads in Shanghai’s central urban area were identified (

Table 1. Application of flowering trees on urban streets. The geographical distribution of individual flowering street trees was ascertained through the integration of street view mapping platforms [56] and comprehensive field investigations.

Plants	Application Frequency	Plant Number	Street Number	Area
Koelreuteria bipinnata ‘integrifoliola’	47.89	17,026	102	Huangpu, Xuhui, Changning, Jing’an, Putuo, Hongkou, Yangpu, Pudong New Area (Within the outer ring road)
Magnolia grandiflora	17.84	4311	38	Huangpu, Xuhui, Changning, Jing’an, Putuo, Hongkou, Yangpu, Pudong New Area (Within the outer ring road)
Prunus × yedoensis ‘Somei-yoshino’	8.45	1785	18	Huangpu, Jing’an, Pudong New Area (Within the outer ring road), Xuhui, Yangpu, Changning, Putuo
Paulownia tomentosa	5.63	665	12	Xuhui, Changning, Hongkou, Pudong New Area (Within the outer ring road)
Koelreuteria bipinnata	4.69	548	10	Hongkou
Aesculus chinensis	4.69	874	10	Xuhui, Changning, Jing’an, Putuo, Yangpu, Pudong New Area (Within the outer ring road)
Melia azedarach	4.69	654	10	Hongkou, Jing’an, Pudong New Area (Within the outer ring road), Putuo, Yangpu, Changning
Catalpa bungei	4.23	721	9	Hongkou, Huangpu, Xuhui
Yulania denudata	0.94	48	2	Jing’an, Pudong New Area (Within the outer ring road)
Michelia chapensis	0.47	28	1	Jing’an
Malus sp.	0.47	43	1	Changning

).

2.2. Establishing a Framework for Evaluating the Landscape and Ecological Benefits of Flowering Street Trees

The framework consists of three components. First, street tree data are collected via digital maps, while tree species and growth indices are gathered through field surveys. Second, the database is refined with growth data, and the landscape and ecological benefits are quantified using SBE and i-Tree models v.5.0. Third, a comprehensive evaluation model is established to support future landscape construction projects, aiming to balance aesthetics and ecological functions while avoiding homogeneous design trends (Figure 1).

2.2.1. Establishing a Digital Information Database for Flowering Street Trees

In accordance with Lan Siren’s definition [57], flowering street trees in Shanghai are classified based on characteristics such as bright or variable flower colors, unique flower forms, attractive plant posture, notable inflorescence, large or fragrant blossoms, and special textures. The digital database includes data regarding species, plant height, DBH, crown width, flower size, inflorescence length, usage frequency, image records, aesthetic value, carbon sequestration, oxygen release, energy savings, rainwater retention, and air purification. Altimeters, tape measures, vernier calipers, and the NCS Index 2050 (NCS Colour AB Box 49022, SE-100 28 Stockholm, Sweden) were used to measure plant height, DBH, crown width, flower diameter, inflorescence length, and flower color under steady light.

2.2.2. Evaluating the Landscape Benefits of Flowering Street Trees

The SBE method, which is recognized for its intuitive and quantitative evaluation of landscape aesthetics, was used to assess the landscape benefits of flowering street trees based on digital images. The evaluation protocol is executed as follows:

(1) Sample collection: Photographs of distinct flowering street trees were acquired exclusively during clear weather conditions between 9:00 am and 11:00 am. To ensure consistency, a single operator utilized identical photographic equipment (HUAWEI Mate 70, Shanghai, China) throughout the data collection period. All images were captured at a standardized height of 1.6 m, approximating the average human eye level, employing a horizontal (landscape) compositional frame. Care was taken to minimize the inclusion of excessive non-landscape elements within the frame to reduce potential observer-induced bias. Photographs were taken from the sidewalk and the center of the roadway to provide a view of the flowering street trees while maintaining safety, and were shifted approximately 30 degrees to the left or right, simulating the viewing angle typical of pedestrian movement based on a horizontal angle. A total of 250 photographs were taken of the 11 types of flowering street trees. Photos that were low-quality, duplicates, or those taken from similar angles were removed, and 33 representative images were selected for evaluation (Figure 2).

(2) Evaluators and approach: To encompass diverse cognitive perspectives and ensure the comprehensiveness and scientific and social applicability of the results, questionnaires were distributed to three groups: college students (not majoring in landscape or other related fields), reflecting people’s first impressions and instinctive aesthetic responses to the landscape; landscape experts, providing an authoritative, professional perspective characterized by heightened expertise and analytical assessment; and local residents, representing the primary actual users of the street tree landscapes, capable of reflecting tangible lived experiences and societal expectations. The evaluation utilized an online questionnaire platform. Within this platform, each image was presented to evaluators for a precisely timed exposure duration of 8 s [58], following established experimental protocols [40]. Individual evaluators subsequently assessed each photograph according to the criteria specified in

Table 2. SBE evaluated levels. Each person was asked to rate three photos of each type of flowering street tree on each of the following seven levels, and the average of the three photos was taken to represent each person’s final landscape score for that type of flowering street tree. The scores, in descending order, represent the evaluator’s favoritism of the flowering street trees.

Very Unattractive	Unattractive	Less Unattractive	Moderate	More Attractive	Attractive	Very Attractive
−3	−2	−1	0	1	2	3

. A total of 300 questionnaires were distributed, and 290 valid responses were collected (response rate: 96.67%). The respondents consisted of 120 college students, 50 landscape experts, and 120 residents.

To establish a predictive model for landscape benefits, 14 evaluation factors were identified based on prior research [51,59,60] (

Table 3. SD Evaluated factors of flowering street trees. The total number of evaluation factors was 14, divided into 4 quantitative factors as well as 8 qualitative factors and 2 unordered categorical variables. The quantitative factors and unordered categorical variable scores were obtained based on a survey, and the 8 qualitative factors were given an overall score by 30 evaluators who viewed 3 photographs of each type of flowering street tree.

Quantitative Factors
X1 Average plant height, X2 Average crown width, X3 Flower diameter size, X4 Inflorescence length
Qualitative Factors
		1	2	3	4	5
X5	Flower branches: Number of flowering branches	Few	A few	Moderate	Many	Numerous
X6	Degree of flower display: Color vibrancy and bloom density	Very low	Low	Moderate	High	Very high
X7	Plant growth: Foliage density, presence of pests and diseases	Very poor	Poor	Moderate	Good	Very good
X8	Visual harmony with surrounding elements: the degree of the color palette, texture balance, and style consistency, blending cohesively with the surroundings.	Very poor	Poor	Moderate	Good	Very good
X9	Spatial scale coordination with surrounding elements: harmonization of the scale of the planting community in terms of height, crown width, etc., with the surrounding buildings and other environmental elements	Very poor	Poor	Moderate	Good	Very good
X10	Tidiness: the maintenance level impacting visual order and safety.	Very poor	Poor	Moderate	Good	Very good
X11	Openness: the perceived accessibility and spatial generosity of the landscape.	Very poor	Poor	Moderate	Good	Very good
X12	Artistic conception: the emotional/narrative resonance evoked by the design.	Very poor	Poor	Moderate	Good	Very good
Disorderly Categorical Variable Factors
X13	Flower color	Yellow	Purple	White	Pink
X14	Plant community richness: Number of planting community structure layers	Single layer tree	Trees and shrubs

). Since landscape factors should not be evaluated in the same way as SBE evaluators, and the SD (semantic differential) method requires that the evaluators be able to accurately evaluate the landscape features, 30 graduate students, teachers of landscape-related majors, and practitioners in relevant landscape industries were selected to conduct the SD evaluation. The experiment was conducted in the form of an online questionnaire, and the evaluators were required to select the adjective that they thought best described the landscape characteristics of the sample photo in each group of evaluation items according to each sample photo.

(3) Data processing: The SBE standardized values for each flowering street tree, as provided by all judges, were summed and then averaged to obtain the final SBE standardized score for each tree. Here, Z_xy is the standardized value of the x_th photo by the y_th judge; R_xy is the raw score; R_y is the mean score given by the y_th judge across all samples; S_xy is the standard deviation of the scores by the y_th judge; Z_x is the final standardized score for the x_th photo; and N_y is the number of judges.

$$Z_{xy}=(R_{xy}-R_y)/S_{xy}$$

$$Z_x={\sum }_y{Z}_{xy}/N_y$$

(4) Landscape evaluation modeling: After converting unordered categorical variables, SBE as the dependent variable, and 14 landscape factors as independent variables, stepwise linear regression was conducted using SPSS 21.0. Irrelevant variables were eliminated, and a predictive model for beauty was developed.

2.2.3. Evaluating the Ecological Benefits of Flowering Street Trees

This paper utilizes the i-Tree model v.5.0 (

Table 4. Calculation method for quantifying ecological benefits in the i-Tree model v.5.0.

Ecological Benefits	Calculation Principle	Price
CO2 absorption	Based on the carbon equations of different tree species, calculate the annual reduction of CO2 emissions achieved by the trees and obtain the economic benefits brought by the annual absorption of CO2 by the trees according to the carbon emission tax collection standards	Carbon Tax
Air purification	Taxes needs to be paid when the total amount of pollutants emitted and removed by trees is the same each year	Taxes on air pollutants
Rainwater interception	When the canopy directly intercepts, indirectly protects water quality, and controls the same amount of rainfall, the government needs to invest funds in managing rainwater	Government management of rainwater funds
Energy savings	The electricity and natural gas consumed by cooling and heating equipment when reducing or increasing the same temperature	Local electricity and natural gas prices

[26]) to evaluate the ecological benefits of 11 types of flowering street trees. Firstly, the climate of Shanghai is compared with the 16 climate zones in the United States, and the climate zone most compatible with Shanghai’s subtropical climate zone is identified as “Coastal Plain”. Secondly, we entered the serial numbers of the road section, land use type, site type, growth index data (plant height, diameter at breast height, crown width, etc.), sidewalk damage, power line conflicts, other site conditions, the length and width of the sidewalk, etc. Then, based on the Shanghai Municipal Development and Reform Commission, the Taxation Bureau, and other published Shanghai Citizen’s Price Information Guidelines (2024) [61], the applicable tax standards for environmental protection against air pollutants and water pollutants [62], including electricity prices, natural gas prices, pollutant emission tax collection, and other economic data (

Table 5. Related prices of the ecological benefits calculation. The economic data are from the Shanghai Citizen’s Price Information Guide (2024) [61] and applicable environmental tax rates [62].

Object	Price	Unit
Electricity price	0.62	CNY/kWh
Natural gas price	3.00	CNY/m3
SO2 tax amount	7.59	CNY/kg
NOx tax amount	8.54	CNY/kg
Tax amount for other pollutants	1.26	CNY/kg

), were fed into the i-Tree Streets model v.5.0 together with the growth data of each flowering street tree obtained from the survey. Then, the model derived the total ecological benefits of the 11 types of flowering street trees and the roads where they are located in the four aspects of absorbing CO₂, purifying the air, intercepting rainwater, and saving energy, as well as the average single plant of each woody plant. These data were converted into economic benefits to quantify the ecological value by model.

When investigating the growth index data, plant height was measured from the base of the tree to the top of the tree using an altimeter. Tree breast diameter was measured by using a tape measure to measure the diameter from the base of the main trunk of the tree at 1.3 m upwards, and when the tree height was less than 1.3 m, the bifurcation was taken as the criterion. The crown width was measured with a tape measure, taking the average of the maximum width in the east–west and north–south directions.

The growth data of each species and local economic data from Shanghai were fed into the i-Tree Streets model v.5.0. The model calculated the ecological benefits of each tree with regard to CO₂ absorption, air purification, rainwater retention, and energy savings.

2.2.4. Evaluating the Comprehensive Benefits of Flowering Street Trees

Since landscape and ecological benefits are measured using different methods, normalization was applied [63,64]. To minimize subjective bias, the entropy-weighted method was used to calculate indicator weights. The entropy-weighted method can maximize the objectivity and scientific accuracy of the weighting of each index factor and reduce the influence of human factors. The standardized value contains the total value of the ecological benefits of CO₂ absorption, air purification, energy saving, and rainwater retention of each flowering street tree and the SBE value of landscape benefits in two items. The steps of the entropy value calculation are as follows.

Data panning is performed on standardized data. In order to eliminate the effect of the data outline, the original data are standardized, but this may lead to the appearance of 0 in the data as the subsequent calculation process will occur in the case of logarithms, and taking logarithms of numbers less than or equal to 0 will not yield results, so the standardized data should be data shifted, and the data base is added to the data with a “shifted value”. This value is the absolute value of the minimum value of the data +0.0001, thus meeting the arithmetic requirements of the entropy-weighted method. x′ is the normalized value, x is the original value, and min(x) and max(x) are the minimum and maximum values of the indicator, respectively.

$$x^{\prime }={\displaystyle \frac{x-min\left(x\right)}{max\left(x\right)-min\left(x\right)}}$$

(1)

The entropy weight e_j is calculated using n, which is the number of samples, p_ij, which is the normalized value for sample i on indicator j, and ln, which refers to the base-10 logarithm.

$$e_j=-{\sum }_{i=1}^n{p}_{ij}\ln p_{ij},0\le e_{ij}\le 1$$

(2)

According to the standardized indicator values, the entropy weight method was used to calculate the weight of each indicator. The weight W_j of each indicator is then derived using m, which is the total number of indicators.

$$W_j={\displaystyle \frac{1-e_j}{{\sum }_{j=1}^m(1-e_j)}}$$

(3)

After determining the weights of the indicators, the comprehensive benefit value of the flowering street trees was calculated by the linear weighting method, and its arithmetic formula is as follows. Finally, comprehensive benefit E is calculated by combining normalized indicator values r_ij with their respective weights W_j.

$$E={\sum }_{i=1}^n{r}_{ij}\times W_j$$

(4)

3. Results

3.1. Database of Flowering Street Trees

3.1.1. Types of Flowering Street Trees

Eleven species from six families and ten genera were utilized as flowering street trees in Shanghai’s central urban area, including two evergreen and nine deciduous species. Koelreuteria bipinnata ‘integrifoliola’ was the most frequently used (over 45%), followed by Magnolia grandiflora (17.84%) and Prunus × yedoensis ‘Somei-yoshino’ (8.45%). The relatively low usage of Yulania denudata (0.94%) may be due to its sensitivity to soil and climate conditions [65].

According to China’s seasonal division [66], species richness peaked in April (46.15%), with lower richness in March, May, August, and September (23.08% each). No flowering trees were recorded in winter, highlighting a seasonal gap (Figure 3).

3.1.2. Growth Indices

Flower color was categorized into yellow, purple, white, and pink (

Table 6. Growth indices of flowering street trees.

Plants	Color	Diameter/cm	Length/cm	Height/m	DBH/cm	Crown Width/m
Koelreuteria bipinnata ‘integrifoliola’	NCS S 1040-G90Y	2~3	35~70	11.4~15.6	28.7~35.6	6.5~9.5
Koelreuteria bipinnata	NCS S 1040-G90Y	2~3	35~70	10.8~13.3	26.8~33.6	6.6~8.7
Melia azedarach	NCS S 2010-R50B	2~3	10~30	9.2~13.3	36.5~45.3	7.5~9.5
Catalpa bungei	NCS S 1505-R10B NCS S 1515-R10B	3~5	10~20	10.2~13.1	26.7~40.1	5.5~8.1
Paulownia tomentosa	NCS S 1020-R50B	5~7	40~50	10.8~13.8	22.5~38.5	6.1~8.5
Prunus × yedoensis ‘Somei-yoshino’	NCS S 0505-R20B	4~5	5~10	5.1~7.6	16.8~26.3	5.4~6.9
Aesculus chinensis	NCS S 0601-R	2~3	15~25	9.2~12.6	17.1~28.3	6.8~7.4
Yulania denudata	NCS S 0601-R	10~16	-	5.3~7.5	17.3~25.7	5.6~7.7
Magnolia grandiflora	NCS S 0601-R	15~20	-	9.3~11.8	23.5~30.8	5.3~8.6
Michelia chapensis	NCS S 0601-R	8~10	-	8.5~10.7	22.3~25.6	5.6~7.5
Malus sp.	NCS S 0601-R	4~5	5~10	4.3~5.7	16.2~25.7	5.1~6.3

). More than half of the species bear white flowers (NCS S 0601-R). Purple-flowered species include Melia azedarach, Catalpa bungei, and Paulownia tomentosa, yellow-flowered species are from the genus Koelreuteria, and Prunus × yedoensis ‘Somei-yoshino’ has pink blooms. Blooming types include single flowers (e.g., Yulania denudate and Magnolia grandiflora) and inflorescences (e.g., Paulownia tomentosa), some of which are over 30 cm in length.

Plant growth data (Table 6) indicated that most species were in a stable growth phase. Their heights were greater than 5 m, DBH ranged from 17 to 46 cm, and crown widths were between 3 and 10 m. Magnolia grandiflora, Koelreuteria bipinnata, Melia azedarach, and others had the highest values. The tallest species exceeded 10 m, and most had DBH values of over 20 cm, with the exceptions of Aesculus chinensis, Prunus × yedoensis ‘Somei-yoshino’, Malus sp., and Yulania denudata. Melia azedarach and Catalpa bungei reached a DBH > 40 cm. All species had crown widths over 4.5 m; Koelreuteria bipinnata and Melia azedarach had crown widths approaching 10 m.

3.2. Landscape Benefits of Flowering Street Trees

3.2.1. SBE Values of Flowering Street Trees

The landscape evaluation data presented a high reliability coefficient of 0.933, which indicated strong reliability. The Kaiser–Meyer–Olkin measure of sampling adequacy (KMO) value was 0.676 (>0.6), suggesting acceptable validity. Using the standardization formula, SBE values were calculated for college students, experts, and the public. The top three species were Melia azedarach, Prunus × yedoensis ‘Somei-yoshino’, and Paulownia tomentosa, while the species with the lowest scores were Magnolia grandiflora, Michelia chapensis, and Malus sp. (

Table 7. Results of SBE values from three groups of populations.

Object	College Students	Landscape Experts	Citizens	Comprehensive
Melia azedarach	0.3125	0.3021	0.3144	0.3097
Prunus × yedoensis ‘Somei-yoshino’	0.2759	0.1876	0.3658	0.2764
Paulownia tomentosa	0.3478	0.2166	0.2277	0.2640
Yulania denudata	0.1661	0.1620	0.0907	0.1396
Aesculus chinensis	0.0272	0.1962	0.0421	0.0885
Koelreuteria bipinnata	−0.0104	0.0244	0.0373	0.0171
Koelreuteria bipinnata ‘integrifoliola’	0.0166	−0.0063	−0.1301	−0.0400
Catalpa bungei	−0.1048	−0.2027	−0.1245	−0.1440
Magnolia grandiflora	−0.2202	−0.1384	−0.1038	−0.1542
Michelia chapensis	−0.5068	−0.0761	−0.3658	−0.3163
Malus sp.	−0.3039	−0.6653	−0.3538	−0.4410

). SBE scores followed a normal distribution, and Pearson correlation analysis revealed significant correlations at the 0.01 level among the three groups, indicating consistent aesthetic preferences (

Table 8. Correlation of SBE values among three groups of populations.

	College Students	Landscape Experts	Citizens
College Students	1
Landscape experts	0.738 **	1
Citizens	0.938 **	0.808 **	1

** The SBE values for flowering street trees demonstrated statistically significant positive correlations (p > 0) across three demographic groups, with the strongest correlation observed between college students and citizens.

).

3.2.2. Regression Modeling of Landscape Benefits

A regression model was constructed to clarify how various landscape factors influence perceived beauty. Based on evaluations from 25 experts (

Table 9. Scores of qualitative landscape evaluation factors.

Plants	X5	X6	X7	X8	X9	X10	X11	X12
Koelreuteria bipinnata	4.76	4.68	4.48	4.36	4.56	4.48	3.32	4.56
Michelia chapensis	1.20	1.04	1.92	3.56	4.24	2.40	4.52	1.44
Prunus × yedoensis ‘Somei-yoshino’	4.52	4.72	4.60	3.80	4.60	3.52	4.44	4.28
Melia azedarach	4.80	4.48	4.68	3.84	4.52	4.32	3.48	4.68
Paulownia tomentosa	4.40	4.80	4.44	4.36	3.64	4.84	4.64	4.52
Aesculus chinensis	3.48	4.40	4.40	3.60	4.52	4.56	3.64	3.36
Koelreuteria bipinnata ‘integrifoliola’	4.44	4.64	4.76	4.64	4.68	4.40	4.68	4.32
Catalpa bungei	3.36	4.36	3.00	2.72	2.48	2.56	2.60	2.12
Yulania denudata	4.00	4.84	4.56	4.60	4.04	3.56	3.40	2.44
Magnolia grandiflora	3.20	2.32	4.48	4.48	4.52	4.28	4.64	4.52
Malus sp.	4.40	2.64	3.36	3.12	4.08	3.52	4.64	2.28

) and the earlier factors identified, the SBE value was employed as the dependent variable. Stepwise regression analysis was conducted using SPSS 21.0 Factors with p ≤ 0.05 were retained, and all VIF values were below 5 (

Table 10. Regression analysis of the evaluation results of the three groups of populations.

Regression Analysis of SBE Valuesand Landscape Evaluation Factors for College Students
	Regression coefficient	Standard Error	Standard coefficient	Significance	VIF
(Constant)	−0.369	0.539		0.031
Visual harmony with surrounding elements.	0.039	0.065	−0.187	0.019	2.276
Plant growth	0.085	0.105	0.329	0.035	3.934
Artistic conception	0.108	0.135	0.277	0.026	2.808
Degree of flower display	0.176	0.136	−0.469	0.015	3.097
Flower branches	0.117	0.085	0.5	0.002	3.142
Inflorescence length	0.003	0.118	−0.016	0.008	2.48
R2	0.806
Regression Analysis of SBE Valuesand Landscape Evaluation Factors for Landscape Experts
	Regression coefficient	Standard Error	Standard coefficient	Significance	VIF
(Constant)	−0.332	0.426		0.042
Visual harmony with surrounding elements.	0.121	0.075	−0.465	0.004	4.157
Plant growth	0.043	0.078	0.202	0.022	3.868
Artistic conception	0.288	0.118	0.892	0.043	4.745
Degree of flower display	0.057	0.094	−0.13	0.005	2.289
Flower branches	0.43	0.102	1.09	0.025	3.381
Inflorescence length	0.253	0.092	−0.667	0.03	2.953
R2	0.784
Regression Analysis of SBE Valuesand Landscape Evaluation Factors for Citizens
	Regression coefficient	Standard Error	Standard coefficient	Significance	VIF
(Constant)	−0.781	0.431		0.038
Visual harmony with surrounding elements.	0.285	0.126	0.809	0.009	4.266
Plant growth	0.289	0.102	−0.854	0.015	3.703
Artistic conception	0.104	0.051	0.468	0.036	2.193
Degree of flower display	0.133	0.132	0.255	0.025	2.6
Flower branches	0.066	0.068	−0.347	0.004	4.278
Inflorescence length	0.927
R2	−0.781	0.431		0.038
Regression Analysis of Comprehensive SBE Valuesand Landscape Evaluation Factors
	Regression coefficient	Standard Error	Standard coefficient	Significance	VIF
(Constant)	−0.99	0.391		0.003
Visual harmony with surrounding elements.	0.032	0.108	−0.136	0.037	3.558
Plant growth	0.133	0.081	0.692	0.002	3.039
Artistic conception	0.183	0.15	0.514	0.005	3.007
Degree of flower display	0.064	0.091	0.302	0.011	3.119
Flower branches	0.091	0.195	−0.332	0.061	2.703
Inflorescence length	0.707
R2	−0.99	0.391		0.003

), indicating the absence of multicollinearity and ensuring the reliability and adequacy of the model. Consequently, the model was successfully established.

Regression models were established for three groups of people evaluating the landscape benefits of flowering street trees:

College students: SBE = −0.369 + 0.039 × Visual harmony with surrounding elements + 0.085 × Plant growth + 0.108 × Artistic conception + 0.176 × Degree of flower display + 0.117 × Flower branches + 0.003 × Inflorescence length.

Landscape experts: SBE = −0.332 + 0.121 × Plant growth + 0.043 × Visual harmony with surrounding elements + 0.288 × Flowering branches + 0.057 × Tidiness + 0.430 × Degree of flower display + 0.253 × Artistic conception.

General public: SBE = −0.781 + 0.285 × Flowering branches + 0.289 × Degree of flower display + 0.104 × Plant growth + 0.133 × Artistic conception + 0.066 × Visual harmony with surrounding elements.

Overall, there was broad consistency among the three groups regarding the aesthetic factors of flowering street trees. However, slight differences emerged in the regression models. All three models included five key variables: flowering branches, degree of flower display, plant growth, artistic conception, and visual harmony with surrounding elements. These were found to be more critical to perceived landscape benefits than factors such as tree height, crown width, flower diameter, or color. In general, better plant growth, more abundant flowering, and greater visual harmony with surrounding elements resulted in higher landscape benefits.

By combining the scores of the three groups, an overall regression model was derived: SBE = −0.99 + 0.133 × Flowering branches + 0.183 × Degree of flower display + 0.064 × Plant growth + 0.032 × Artistic conception + 0.091 × Visual harmony with surrounding elements. The most influential factors in determining landscape benefits were the number of flowering branches, degree of flower display, plant growth, artistic conception, and coordination. Field interviews supported these findings, with many respondents expressing a preference for trees that blended well with their surroundings. Trees with good plant growth and abundant blossoms were seen as enhancing the ambiance of the street and increasing its visual appeal.

3.3. Ecological Benefits of Flowering Street Trees

3.3.1. Average Ecological Benefits of Different Flowering Street Trees

The average ecological benefit per flowering street tree was CNY 255.44 (Figure 4). Species with above-average ecological benefits included Catalpa bungei, Koelreuteria bipinnata, Koelreuteria bipinnata ‘integrifoliola’, Melia azedarach, and Paulownia tomentosa. In terms of CO₂ absorption, air purification, rainwater retention, and energy saving, these species outperformed the others, indicating their ecological superiority among the 11 species under study.

3.3.2. Total Ecological Benefits of Different Flowering Street Trees on 213 Roads

Across 213 roads, the cumulative ecological benefits provided by the 11 species totaled CNY 6,820,935.51 (

Table 11. Evaluated levels of ecological benefits.

Plants	Number	CO2 Absorption			Air Purification			Energy Savings				Rainwater Interception			Comprehensive Benefits/CNY
		Individual/CNY	Net Absorption/kg	Total Benefits/CNY	Individual/CNY	Total/kg	Total Benefits/CNY	Individual/CNY	Electricity Saving/GJ	Gas Saving/GJ	Total Benefits/CNY	Individual/CNY	Total/m3	Total Benefits/CNY	Individual	Total
Koelreuteria bipinnata ‘integrifoliola’	17026	235.68	1,588,208.72	1,871,021.76	6.65	11,699.91	79,328.66	102.47	5440.97	44,107.61	1,249,320.14	100.92	96,685.55	993,967.92	445.72	4,193,638.48
Magnolia grandiflora	4311	97.76	335,656.60	421,443.36	3.38	1145.63	14,571.18	48.55	994.39	5347.07	209,299.05	72.56	27,717.82	312,806.16	222.25	958,119.75
Aesculus chinensis	874	128.37	89,329.79	112,195.38	3.13	21.29	2735.62	48.70	186.69	1475.31	42,563.80	47.49	3684.78	41,506.26	227.69	199,001.06
Catalpa bungei	721	315.22	180,987.57	227,273.62	9.49	982.05	6842.29	142.63	466.58	3174.06	102,836.23	154.25	9861.95	111,214.25	621.59	448,166.39
Paulownia tomentosa	665	179.37	94,988.05	119,281.05	4.81	456.92	3198.65	74.25	220.36	1608.50	49,376.25	72.88	4294.97	48,465.20	331.31	220,321.15
Melia azedarach	654	198.77	103,522.85	129,995.58	5.44	509.70	3557.76	83.97	246.24	1766.50	54,916.38	82.54	4785.14	53,981.16	370.72	242,450.88
Koelreuteria bipinnata	548	312.24	136,262.81	171,107.52	9.38	737.63	5140.24	141.13	350.33	2389.89	77,339.24	151.99	7380.60	83,290.52	614.74	336,877.52
Prunus × yedoensis ‘Somei-yoshino’	1785	53.68	79,267.14	95,818.8	2.06	546.60	3677.10	33.68	253.95	2302.73	60,118.8	23.78	3879.68	42,447.30	113.20	202,062.00
Yulania denudata	48	128.37	4906.43	6161.76	3.13	21.29	150.24	48.70	10.23	81.39	2337.60	47.49	202.43	2279.52	227.69	10,929.12
Malus sp.	43	53.68	1839.00	2308.24	2.06	12.90	88.58	33.68	6.10	55.00	1448.24	23.78	91.00	1022.54	113.20	4867.60
Michelia chapensis	28	69.35	1547.00	1941.80	2.42	5.00	67.76	34.53	4.60	25.00	966.84	54.47	135.00	1525.16	160.77	4501.56
Summary	26,703	-	2,616,515.96	3,158,548.87	-	16,138.92	119,358.08	-	8180.44	62,333.06	1,850,522.57	-	158,718.92	1,692,505.99	-	6,820,935.51
Average individual benefit/CNY	-	-	97.99	118.28	-	0.60	4.47	-	0.31	2.33	69.30	-	5.94	63.38		-

). The proportion of ecological benefits by category was as follows:

• CO₂ absorption: 46.31%;
• Energy saving: 27.13%;
• Rainwater retention: 24.81%;
• Air purification: 1.75%.

In terms of total ecological benefits, the top five species were Koelreuteria bipinnata ‘integrifoliola’, Magnolia grandiflora, Catalpa bungei, Koelreuteria bipinnata, and Melia azedarach. Notably, Koelreuteria bipinnata ‘integrifoliola’ contributed 61.48% of the total ecological benefits. The species with the lowest contribution was Malus sp., accounting for only 0.07% of the total.

The relatively high ecological performance of Catalpa bungei, Koelreuteria bipinnata, Koelreuteria bipinnata ‘integrifoliola’, Melia azedarach, and Paulownia tomentosa can be attributed to superior growth parameters, such as plant height, DBH, and crown width:

• Height: Species including Koelreuteria bipinnata, Koelreuteria bipinnata ‘integrifoliola’, Catalpa bungei, and Paulownia tomentosa had average heights of over 10 m. These tall and healthy trees create continuous shade, reducing ground radiation and temperature. Through convection, they also improve the air conditions of the vicinity. As a result, indoor cooling needs may be reduced, enhancing energy efficiency [67,68].
• DBH: Trees such as Koelreuteria bipinnata, Koelreuteria bipinnata ‘integrifoliola’, Melia azedarach, and Catalpa bungei had DBH values exceeding 25 cm, indicating higher biomass. Larger DBH values correlated with greater CO₂ storage capacities and improved photosynthetic function, aiding in both carbon sequestration and air purification [69,70].
• Crown width: Species such as Koelreuteria bipinnata ‘integrifoliola’ and Melia azedarach had crown widths of over 6 m, with some reaching nearly 10 m. Broad canopies assist in filtering pollutants (SO₂, NO_x, particulate matter), improving air quality through leaf adsorption [71,72]. They also increase the surface area available for stormwater capture and evapotranspiration, enhancing the benefits of rainwater retention [73,74].

3.4. Evaluation of the Comprehensive Benefits of Flowering Street Trees

After normalizing both landscape and ecological benefits, the entropy-weighted method was used to determine weightings (

Table 12. Weight results.

Index	Entropy Weight	Weight
Ecological benefits	0.8256	0.6889
Landscape benefits	0.9212	0.3111

):

Comprehensive Benefits = 0.6889 × Ecological Benefits + 0.3111 × Landscape Benefits

Based on comprehensive scores, three benefit levels were defined (

Table 13. The comprehensive benefits of flowering street trees.

	Ecological Benefits	Landscape Benefits	Comprehensive Benefits
Koelreuteria bipinnata	0.67966745	0.18988572	0.86955317
Catalpa bungee	0.68894936	0.12311956	0.81206892
Melia azedarach	0.34901458	0.31115064	0.66016522
Koelreuteria bipinnata ‘integrifoliola’	0.45064135	0.16622124	0.61686259
Paulownia tomentosa	0.29561309	0.29221077	0.58782386
Yulania denudate	0.15520554	0.24065453	0.39586007
Aesculus chinensis	0.15520554	0.21947668	0.37468223
Prunus × yedoensis ‘Somei-yoshino’	0.00006889	0.29734982	0.29741870
Magnolia grandiflora	0.14783422	0.11889228	0.26672650
Michelia chapensis	0.06452736	0.05171168	0.11623905
Malus sp.	0.00006889	0.00003111	0.00010000

):

Grade I: >0.5;

Grade II: 0.3–0.5;

Grade III: <0.3.

Species classified as Grade I and recommended for priority use in Shanghai roads include Koelreuteria bipinnata, Catalpa bungei, Melia azedarach, Koelreuteria bipinnata ‘integrifoliola’, and Paulownia tomentosa. Grade II and III species may be selectively used depending on conditions or functions.

4. Discussion

4.1. Framework for Evaluating the Benefits of Flowering Street Trees

This study utilized flowering street trees in Shanghai’s central urban area to validate the evaluation framework for the landscape and ecological benefits of street trees within the context of digital infrastructure. It demonstrated a viable approach for integrating digital technology into street tree projects, offering valuable data for the sustainable planning, management, and optimization of urban street trees. Overall, the framework embodied the characteristics of operational replicability, element digitization, and benefit visualization. Differently from previous studies, which analyze the application and evaluation of street trees in the broad sense [75,76,77], this framework paid special attention to the application of flowering street trees on urban roads and combined digital street maps and field surveys to collect the morphological parameters of each tree, such as plant height, DBH, crown width, and growth status, as well as the conditions of the road where it is located, such as the safety of sidewalks, which will provide more convenient and efficient references for the relevant governmental departments to manage and maintain the street tree landscapes. In addition, in contrast to the evaluation of landscape or ecological benefits only, this framework utilized the entropy-weighted method to integrate the comprehensive benefits of each flowering street tree in these two aspects, clearly reflecting their comprehensive application value.

4.2. Evaluation of the Landscape and Ecological Benefits of Flowering Street Trees Based on Digital Technology

This study integrated SBE based on digital images and the i-Tree model v.5.0 to quantify both the landscape and ecological benefits of flowering street trees. It further employed the entropy-weighted method to evaluate comprehensive benefits, offering an innovative approach compared with previous studies that typically assess only one aspect. Based on digital images, this paper analyzed the landscape evaluation of flowering street trees by three groups of people, namely college students not specializing in landscape and other related fields, landscape experts, and citizens, using the SBE-SD method. A consistency in aesthetic preferences was observed among the three respondent groups, which is in line with previous research findings [78,79,80]. Although the core influencing factors were generally the same, some differences were noted. All three groups confirmed that the more harmonious the flowering street trees are with the surrounding environmental elements, the better the plant growth, the better the artistic conception, the higher the degree of a flower display, the higher the number of flower branches, and the higher the landscape value. However, college students, for instance, placed greater emphasis on inflorescence length. They believed that the longer the length of the inflorescence, the more prominent its landscape effect. This may be due to the fact that long-inflorescence plants have a higher visual dwell time than short-inflorescence varieties, and their linear forms are more attractive [81]. Landscape experts regarded the tidiness of street trees as an important factor, which echoes the findings of Gao Rongrong [79], Zhou Junjun [80] and Zhang Mingyue [82]. This indicates that a uniform form and orderly planting can enhance visual coherence, improve road orientation, and highlight the stylistic character of urban spaces.

Many scholars have utilized the i-Tree model v.5.0 to analyze the ecological benefits of trees and have found that factors such as tree age, plant height, DBH, and crown width significantly affect ecological outcomes [55,79,83]. McPherson and Simpson [83] concluded that trees taller than 6 m provide energy-saving benefits to buildings within an 18 m radius. Moreover, successive planting of trees with large crowns can create continuous shade, reduce direct solar radiation on the ground, lower surface temperatures, and reduce the energy consumption of cooling equipment. Larger canopies can also intercept and store greater volumes of rainwater.

In this study, it was found that Catalpa bungei, Koelreuteria bipinnata, Koelreuteria bipinnata ‘integrifoliola’, Melia azedarach, and Paulownia tomentosa, with average crown widths greater than 6 m, produced particularly notable energy-saving and rainwater retention benefits. Hao Xinjie et al. [84] also concluded that woody plants with larger DBH values generally accumulate more biomass, and their leaves could absorb more airborne pollutants over time. Species such as Melia azedarach, Catalpa bungei, Koelreuteria bipinnata ‘integrifoliola’, and Koelreuteria bipinnata had average DBH values exceeding 25 cm and demonstrated stronger air purification benefits compared to other flowering street trees with smaller DBH values. Based on a comparative analysis, this study concluded that flowering street trees with plant heights above 10 m, DBH values over 25 cm, and crown widths greater than 6 m exhibit higher ecological benefits. These three indicators were closely related to plant growth, photosynthetic capacity, and physiological self-regulation.

4.3. Inspirations for Further Urban Street Tree Landscape Construction

At present, China has issued relevant road greening construction documents. For example, in November 2023, the Ministry of Housing and Urban-Rural Development issued the industry standard “Urban Road Greening Design Standard”, which proposes that “in road greening, we should choose plants that are adapted to the road’s standing conditions, have stable growth and resistance, are easy to manage and maintain, have a high ornamental value, have a good environmental benefit, and reflect regional characteristics”. In the “Shanghai greening characteristics of the road to create methods” document also put forward, Shanghai built a “Shanghai flower city” based on urban road coloring construction and aroma construction. At present, Shanghai has expanded the application of flowers in neighborhoods and communities, enhancing the rural environment, which is a key work target. Most of the flowering street trees, in addition to the general ecological value of the street tree itself in terms of color, fragrance, and other landscape values, allow pedestrians to enjoy a close-up view of various types of flowers. However, according to the previous survey, the central urban roads in Shanghai still exhibit fewer types of flowering street trees, with less application, and the seasonal landscape is an obvious problem [34,35]. Therefore, in Shanghai and other countries or regions with similar problems, increasing the application of highly effective flowering street trees should be considered. Referring to the quantitative methods and results in this paper, not only can we screen suitable and comprehensively effective flowering street trees for regions with a similar climate to Shanghai, but we can also provide methodological paths for different countries and regions to apply new species and build more attractive landscapes.

Based on the results of the digital model, the key factors influencing the landscape benefits of flowering street trees include the number of flowering branches, the degree of flower display, plant growth, artistic conception, and visual harmony with surrounding elements. Therefore, tree species with abundant flowering branches, high visual impact, healthy growth, strong artistic appeal, and good integration with the urban landscape should be prioritized. From a landscape perspective, Melia azedarach, Prunus × yedoensis ‘Somei-yoshino’, Paulownia tomentosa, Yulania denudata, and Aesculus chinensis could be considered for well-adapted zones.

Regarding ecological benefits, the most influential indicators are plant height, DBH, and crown width. Species exceeding 10 m in height, 25 cm in DBH, and 6 m in crown width provide stronger ecological services. Therefore, Catalpa bungei, Koelreuteria bipinnata, Koelreuteria bipinnata ‘integrifoliola’, Melia azedarach, and Paulownia tomentosa may offer notable advantages in this regard.

For comprehensive benefits, Koelreuteria bipinnata, Catalpa bungei, Melia azedarach, Koelreuteria bipinnata ‘integrifoliola’, and Paulownia tomentosa could serve as primary selections in Shanghai. Other species such as Aesculus chinensis, Magnolia grandiflora, Prunus × yedoensis ‘Somei-yoshino’, Yulania denudata, Malus sp., and Michelia chapensis may also be incorporated where conditions allow.

Moreover, focusing on the maintenance and management of flowering street trees can artificially improve the overall aesthetics of the tree. Of course, in addition to considering the aesthetics of the flowering street trees, their local habitability should also be considered, and the right species for the local climate and environment should be chosen. Some European countries, for example, need to consider extreme weather, pests, and diseases on the flowering street trees. In addition, on roads with low ecosystem service values, the number of street trees planted should be increased, expanding the area covered by street trees. In addition, the relationship of trees with the commercial management of sidewalks should be properly managed.

4.4. Limitations and Future Work

Regarding the distribution of landscape evaluation questionnaires, the questionnaires were distributed to three groups of people, considering the ease and efficiency of inviting people, as well as privacy issues: non-landscape and other related majors in colleges and universities, landscape experts, and citizens, which ensured the diversity of types to a certain extent. In the future, the questionnaires can be conducted from the perspectives of gender, age, and different occupations to analyze the crowd’s preference characteristics for the landscape of flowering street trees in a wider way.

In the ecological benefits section, it is important to note that the i-Tree model v.5.0 was originally developed based on U.S. economic policies and ecological conditions. In this study, economic parameters were adjusted to reflect local Shanghai data, and the climate type most similar to Shanghai was selected. This partial localization improves model applicability. However, future work should focus on developing localized growth models for different tree species across China’s varied climate zones to fully adapt the model to the regional context. In future applications, the ecological benefits calculation model should be used to analyze how different tree specifications, such as height, DBH, and crown width, affect ecological efficiency. Compared to studies focusing on the micro-characteristics of individual species, these analytical tools are more applicable to tree selection, specification optimization, and greening layout for urban streets in Shanghai, offering better visualization and scientific grounding.

As digital technology continues to advance, its role in visualizing and evaluating landscape and ecological benefits will expand. Digitization can cover the entire life cycle of urban street tree construction, management, maintenance, and operation. Future improvements should include dynamic ecological monitoring of street trees, including air and soil moisture, temperature, plant growth, and disease and pest tracking, to maintain ecological balance and sustainability [85]. Technology such as building information modeling (BIM), object detection, and image information extraction can support unified data platforms, enhance the interoperability and transmission of road maintenance data, speed up data handover, and improve operational efficiency. Moreover, the creation of open-access platforms for sharing street tree data among different cities will facilitate local adaptive management. By employing data-driven equity and standardized digital assessment frameworks, cities can gradually enhance the diversity and seasonal balance of street landscapes. Ultimately, this will promote the sustainability of urban street landscapes and ecosystem services.

5. Conclusions

This study focused on the landscape, ecological, and comprehensive benefits of 11 species of flowering street trees in Shanghai’s central urban area, offering a replicable evaluation method for digital street tree landscapes. By constructing a database and an evaluation framework, the project allows for visualization of plant growth status, aesthetic value, and ecological service performance. A total of 11 species from six families and 10 genera were employed as flowering street trees. Koelreuteria bipinnata ‘integrifoliola’ was the most frequently used, while Michelia chapensis was the least. Winter-flowering species were notably underrepresented. The highest landscape benefits were observed in Melia azedarach, Prunus × yedoensis ‘Somei-yoshino’, Paulownia tomentosa, Yulania denudata, and Aesculus chinensis. The key influencing factors included the number of flowering branches, degree of flower display, plant growth, artistic conception, and visual harmony with surrounding elements. The species with the highest ecological benefits were Catalpa bungei, Koelreuteria bipinnata, Koelreuteria bipinnata ‘integrifoliola’, Melia azedarach, and Paulownia tomentosa, each exceeding 10 m in height, 25 cm in DBH, and 6 m in crown width. A comprehensive benefit model was developed as follows:

Comprehensive Benefits = 0.6889 × Ecological Benefits + 0.3111 × Landscape Benefits

Based on this, Koelreuteria bipinnata, Catalpa bungei, Melia azedarach, Koelreuteria bipinnata ‘integrifoliola’, and Paulownia tomentosa are recommended as priority species for urban streets. Aesculus chinensis, Magnolia grandiflora, Prunus × yedoensis ‘Somei-yoshino’, Yulania denudata, Malus sp.’American’, and Michelia chapensis can be selectively applied.

By integrating the “digital technology–benefit evaluation” methodology, enhancing the collection of digital images and ecological data, and creating a dynamic database of year-round flowering street trees, it is possible to apply models such as SBE, i-Tree v.5.0, and the entropy-weighted model to prioritize street tree selection. A replicable digital pathway can be provided for the delicacy management and sustainable planning of street tree landscapes, especially in biodiversity hotspots. As interdisciplinary collaboration heats up, urban planners can leverage a range of digital technology to improve landscape planning strategies that align with broader objectives, such as carbon sequestration, heat island mitigation, and habitat connectivity. Additionally, in cities such as Shanghai —where winter-flowering street trees are scarce—there is a clear need to adapt plant selection to the local climate and introduce more winter-flowering street trees.