Study on Low-Carbon Design Strategy of Block-Scale Science and Technology Industrial Park Based on Solar Energy Utilization Potential and Heat Island Effects

Abstract

This study aims to establish an energy assessment system and provide low-carbon design strategies for block-scale science and technology industrial parks in the Yangtze River Delta region of China. To investigate low-carbon design strategies for these parks, the impact of solar energy utilization potential and heat island effect on the energy consumption of buildings is taken as the entry point. Through an analysis of the spatial characteristics of twenty block-scale science and technology industrial parks in the Yangtze River Delta region of China, two types of idealized park models comprising a total of eighteen variations were established. The simulation process involved six key morphological parameters to describe the specific shape of the parks quantitatively. The Ladybug Tools 1.6.0, Radiance 5.4a, and URBANopt v0.9.2 software were used to simulate the potential for photovoltaic power generation and the energy consumption of the parks. Net Energy Use Intensity (NEUI) and Potential Utilization Ratio of Renewable Energy (PURRE) were selected as the final evaluation indexes to represent the integrated energy performance of the park. The results show that for the park with a circular layout, the optimal integrated energy performance is achieved when the building density is between 35% and 40%; the average building height is designed with lower values within the range of 20 m to 24 m, and the height-to-depth ratio is around 0.3. Finally, based on the results of the analysis, four major low-carbon design strategies were proposed: high-density development, courtyard layout, supporting-function centralized layout, and carbon sink enhancement.

1. Introduction

In China, enterprises engaged in manufacturing or service industries that meet standard requirements and are clustered in limited spaces to carry out production and development activities in the same category of industrial systems can be called science and technology industrial parks. These parks have two characteristics: high-tech and industrial clustering. Science and technology industrial parks play an important role in promoting economic development and technological innovation. However, they are also areas with high resource consumption and carbon emissions. According to data from the Chinese government, there are more than 15,000 science and technology parks in China, contributing more than 30% to the national economy, but also generating 31% of the country’s CO₂ emissions [1]. In addition, the urban heat island effect can significantly affect the air-conditioning energy consumption of office buildings. In most cities, in the hot summer and cold winter region, due to the longer air-conditioning period in summer than the heating period in winter, the higher the heat island intensity, the higher the annual energy consumption intensity of office buildings [2]. The proportion of office buildings in science and technology industrial parks is relatively high, and the impact of the urban heat island effect on the energy consumption of these parks can no longer be ignored.

With the implementation of “Carbon Peaking and Carbon Neutrality Goals” in China, science and technology industrial parks, as concentrated areas of resource consumption and carbon emissions, have become a key focus for energy conservation and emission reduction. Solar energy, as one of the most important renewable energy sources, is characterized by its large scale, safety, and cleanliness. It offers significant environmental benefits and plays a crucial role in optimizing China’s energy structure and is one of the most competitive options among all renewable energy sources [3].

Many developed countries began to pay attention to the study and development of solar energy after the first oil crisis and conducted a large number of related studies. Izquierdo et al. [4] were the first to systematically propose a method for evaluating the solar energy utilization potential of urban buildings. They believed that an integrated evaluation of the solar energy utilization potential of urban buildings could be divided into three steps: first, assessing the physical potential by analyzing the amount of solar radiation that building surfaces can receive; second, assessing the geographical potential by further screening the surfaces that can receive solar radiation based on the installable area of solar energy devices on building surfaces; and finally, assessing the technical potential by calculating the power generation or heat production potential based on the conversion efficiency and system performance of solar energy devices. In addition, studies by De Cristo et al. [5] have shown that green roofs can improve the thermal performance of Mediterranean buildings. The combined use of solar photovoltaics and green vegetation will be beneficial in reducing building energy consumption.

In China, in terms of spatial form and solar energy utilization, Liao et al. [6] were the first to discuss the impact of spatial form on solar energy utilization in urban blocks in 2010. Xu et al. [7] further selected four representative morphological factors (building density, floor area ratio, plan form, and building height) to study their effects on the potential for solar energy utilization in urban blocks. Regarding the relationship between the urban heat island effect and building energy consumption, Yang et al. [8] demonstrated that the heat island effect increases the energy consumption in residential and office buildings in Nanjing; Chen et al. [9] indicated that the urban spatial form is crucial for alleviating urban thermal environment problems; Jiang et al. [10], based on the concept of local climate zones, investigated the relationship between closely climate-related indicators of residential areas with different morphological characteristics and regional building energy consumption.

In China, past studies on the planning and development of science and technology industrial parks have mainly focused on spatial layout, with large-scale park planning being the primary study object [11,12,13]. As the functional complexity of high-tech parks has increased and their planning and positioning have become more precise, studies on the spatial design of different types of parks based on various spatial and industrial types have gradually increased. Zhang [14] summarized the design strategies of three-dimensional public spaces in high-density science and technology parks. Yang [15] argued that science and technology parks should selectively integrate with urban roads of appropriate scale. Wu et al. [16] conducted a comparative study of the spatial morphology of science and technology parks, finding a complex relationship between environmental quality and green space area, and concluding that rational site design is crucial for improving environmental quality.

By reviewing the literature, it is understood that studies on low-carbon design for science and technology parks in China are still in their infancy. Current studies mainly discuss the feasibility and effectiveness of technical pathways and construction plans [17,18], while studies on how low-carbon design concepts influence the site layout and spatial form of building clusters are almost non-existent. Therefore, this paper takes the impact of solar energy utilization potential and the urban heat island effect on building energy consumption as the entry point, aiming to propose low-carbon design strategies with a strong orientation on energy for science and technology parks in the Yangtze River Delta region of China.

This study aims to establish an energy assessment system and provide low-carbon design strategies for block-scale science and technology industrial parks in the Yangtze River Delta region of China. The key study points of this study are threefold. First, through the analysis of the spatial characteristics of 20 block-scale science and technology parks in the Yangtze River Delta region of China, 18 idealized park models were established. Second, an integrated energy evaluation system was constructed, with Net Energy Use Intensity (NEUI) and Potential Utilization Ration of Renewable Energy (PURRE) selected as the ultimate evaluation indicators to represent the overall energy performance. Third, through simulation, it was found that the parks with circular spatial structure and courtyard form layout had the lowest integrated energy consumption, and four major low-carbon design strategies for science and technology parks were proposed. The energy evaluation system established in this study will be helpful for offering low-carbon design guidance for block-scale science and technology industrial parks in the Yangtze River Delta region of China in terms of more specific building parameters and spatial forms.

2. Simulation Methods

2.1. Integrated Energy Performance Evaluation System

The study proposes an integrated energy performance system for block-scale science and technology industrial parks that explicitly incorporates urban heat island effects. The specific steps of the evaluation framework are illustrated in Figure 1:

• Parametric Modeling: By summarizing from twenty cases, two major types of park spatial forms were identified, and eighteen ideal park models were constructed based on seven established rules. Geometric attributes of the idealized models were translated into parametric modeling within Rhinoceros 7 and Grasshopper by extruding footprint polylines into volumetric building envelopes.
• Select Key Parameters: To systematically capture the spatial layout characteristics of science and technology industrial parks and to ensure broad applicability during early-stage planning and design, six key morphological parameters were identified as study variables.
• Simulation Workflow: The energy consumption simulation of parks was conducted using energy simulation software such as Ladybug 1.6.0, Radiance 5.4a, and URBANopt 0.9.2 to assess the photovoltaic power generation potential and the impact of the urban heat island effect. Based on the simulation results, the photovoltaic power generation intensity, energy use intensity, and integrated energy performance indicators were calculated.
• Results Analysis: By comparing the integrated energy performance of various idealized park models, the street patterns with superior energy performance were identified.
• Design Strategies: Based on the analysis results of the study, planning and design strategies for science and technology industrial parks were proposed, which focused on optimizing the integrated energy performance of the building complex.

To facilitate subsequent simulation analyses and comparisons while controlling extraneous variables, all idealized park models were assumed to be located within a consistent urban environment, with Shanghai’s standard-year weather file uniformly adopted as the baseline meteorological dataset.

In this study, the main functional modules used were Honeybee and Dragonfly. The Honeybee plugin calls on the Radiance software kernel to calculate solar radiation for parks. Dragonfly was used to establish the thermal zones of the 3D models and set parameters. After consolidating the information, it was input into the URBANopt simulation platform for corresponding simulation calculations. The UWG (Urban Weather Generator) module under the Dragonfly module can incorporate the impact of the urban heat island effect into the energy consumption simulation process. The above software or engines have been used in numerous studies and their reliability has been verified through measurements [19,20,21,22,23]. Therefore, the results of this study are relatively reliable.

2.2. Idealized Models of Science and Technology Industrial Park

To make the study more targeted, the study divides the science and technology industrial parks into two major categories based on land scale, industrial scale, functional positioning, business complexity, and development model: urban-scale and block-scale. Urban-scale parks generally cover an area of more than one square kilometer, with a construction volume of millions of square meters. They are large-scale constructions and part of the city, often referred to as science and technology development zones. They can accommodate the complete industrial chains within a single industrial system and can also support different positions in the industrial chains of multiple systems. They include various types of land use and can undertake complex urban functions. The development model mainly focuses on land concession and regional development. In contrast, block-scale science and technology industrial parks typically cover an area ranging from several to dozens of hectares. The development plots are either single or multiple, and the total construction area is generally in the hundreds of thousands of square meters. Block-scale parks have a single production mode and limited functions, mainly undertaking production and some living functions in certain urban areas. The production function (or service function) is the most important functional attribute of this type of park. The development model usually follows the real-estate-leasing approach, aiming to achieve overall planning and development of the plots.

Based on the characteristics of the two types of parks mentioned above, the study selected block-scale parks as the research object for the following reasons: First, block-scale parks have a moderate land area, and the building layout has a significant impact on the potential for solar energy utilization and the microclimate of the area, which is conducive to identifying patterns. Second, these parks have a dominant industry, integrated development, highly related supporting construction and business formats, and a certain degree of independence of the site, with less influence from external factors on their spatial structure. All subsequent descriptions of science and technology industrial parks in this paper refer to block-scale parks.

Through preliminary case-study research, 20 science and technology industrial parks in the Yangtze River Delta region were selected as the research and analysis objects. These parks cover four provinces: Shanghai, Jiangsu, Zhejiang, and Anhui. They have different functions and include various industries. The selected samples are diverse, and all the parks were constructed after year 2000, which can better reflect the developmental status and spatial characteristics from the beginning of the 21st century. Subsequently, ideal park models were abstracted based on these 20 cases.

The spatial structure of a science and technology industrial park refers to the spatial positioning and combination of various elements that constitute the park, which is the result of the interaction between different buildings and environmental factors. The focus of this study is on three key elements that can clearly describe the functional and spatial characteristics of a science and technology park: the park center, study-and-development or production clusters, and supporting facilities.

By analyzing the spatial characteristics of 20 block-type science and technology industrial parks in the Yangtze River Delta region of China, it was found that the spatial composition follows a common logic, with building layouts exhibiting two tendencies: centripetal and de-centralized. The first is the circular spatial structure (Figure 2a), where supporting functions or landscape elements form the park center and development or production clusters are arranged in a circle around the center, creating a center–periphery spatial structure. This pattern is more common in medium- and large-sized parks. The second is the evenly distributed cluster structure (Figure 2b), where the park is de-centralized or the centripetal relationship of the park center is weakened. The building clusters are placed side by side, and the spatial layout pursues a flat structure. This pattern is more common in small- and medium-sized parks.

Based on the industrial characteristics, basic spatial features, and surveyed building parameters of block-type science and technology parks in the Yangtze River Delta region of China, and considering the urban traffic regulations on block size and road network layout, the following rules are applied to abstract idealized parks from actual cases to represent a category of spatial characteristics:

• Unified Park Plot and Floor Area Ratio: The model area is a rectangular plot measuring 300 m × 300 m (total area of 9 hm²), with urban roads surrounding the four boundaries of the plot. The FAR of the plot is set at 1.6.
• Building Density: The building density should be no less than 25% and no more than 50%.
• Functional Proportion: The proportion of building functions is approximately 80:15:5 for the study-and-development office area, commercial-and-catering supporting area, and cultural service supporting area, respectively. Other building functions are ignored.
• Building Form: The building form is simplified, with shapes mainly being rectangular, “L”-shaped, and “C”-shaped. All buildings have a due-south orientation. The buildings are primarily multi-story, with a maximum height of 50 m for any single building.
• Floor Height: The first floor of any building is 4.5 m high. For buildings with study-and-development office functions, the floor height is 4.2 m, while for those with supporting functions, it is 4.5 m.
• Building Layout Relationship: Building clusters are formed through three geometric operations of array, symmetry, and enclosure. The overall layout of the building cluster follows the principle of being lower in the south and higher in the north.
• Supporting Function Layout Principle: When supporting functions are centrally arranged, they are located in the center of the site. When they are dispersed, they are evenly distributed. When both arrangements are combined, the dispersed supporting functions are placed around the periphery of the site.

Based on the seven rules, two types of layouts were ultimately derived: circular-shaped layout and evenly distributed cluster layout. Four variations (A1, A2, B1, B2) of each type resulted in a total of eighteen idealized park models (Figure 3) to represent science and technology industrial parks with various spatial characteristics.

2.3. Quantification of Key Morphological Parameters

Block-scale morphological parameters can quantitatively describe district-level building clusters from both urban-planning and architectural-design perspectives, and they adequately capture the spatial-layout characteristics of block-scale science and technology industrial parks.

There have been numerous studies that have utilized and demonstrated the impact of various parameters, such as building density, building shape factor, green space ratio, and others, on building energy consumption [10,20,24,25,26]. Following the synthesis of existing studies [27,28,29], parameters influencing photovoltaic potential fall into two categories: development intensity and block spatial form. Similarly, factors affecting building-cluster energy demand can be grouped into four categories: development intensity, block spatial form, land-use composition, and landscape greenery [29].

Aligned with the study’s objectives, we focused on the first two categories—development intensity and spatial form to examine their impacts on overall energy performance while accounting for heat island effects and microclimate influences. To ensure broad applicability in planning, six key parameters were selected. Their definitions and calculation methods are summarized in

Table 1. Six key morphological parameters.

Morphological Parameters	Equations/Prescribed Limits	Definitions	Schematic Diagram
Building Density (BD)	$\mathrm{BD}={\displaystyle \frac{{\sum }_{\mathrm{i}=1}^{\mathrm{n}}\mathrm{C}\mathrm{i}}{\mathrm{A}}}$	The ratio (%) of the aggregate footprint area of all buildings to the gross site area within a given land parcel. It indicates the compactness of park development.
Green Space Ratio (GSP)	$30\mathrm{\%}\le \mathrm{G}\mathrm{S}\mathrm{P}\le 40\mathrm{\%}$ $\mathrm{G}\mathrm{S}\mathrm{P}+\mathrm{B}\mathrm{D}\le 70\mathrm{\%}$	The percentage of the total area of all green spaces to the gross site area within a given land parcel, indicating the degree of landscaping and vegetative coverage.
Average North–South Building Spacing (ABS)	$\mathrm{A}\mathrm{B}\mathrm{S}={\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{D}}_{\mathrm{i}}$	The minimum perpendicular distance between the exterior facades of two adjacent buildings or structures measured along the north–south axis; distances exceeding 50 m are excluded from the calculation. This metric indicates the vertical morphology of the building cluster.
Average Building Height (ABH)	$\mathrm{A}\mathrm{B}\mathrm{H}={\displaystyle \frac{{\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{H}}_{\mathrm{i}}·{\mathrm{V}}_{\mathrm{i}}}{{\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{V}}_{\mathrm{i}}}}$	The average height of all individual buildings within a given site, reflecting the overall vertical profile of the buildings.
Height-to-Depth Ratio (HDR)	$\mathrm{H}\mathrm{D}\mathrm{R}={\displaystyle \frac{{\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{H}}_{\mathrm{i}}·{\mathrm{V}}_{\mathrm{i}}}{{\sum }_{\mathrm{i}=1}^{\mathrm{n}}{{\mathrm{K}}_{\mathrm{i}}·\mathrm{V}}_{\mathrm{i}}}}$	The volume-weighted average height of the building cluster divided by its volume-weighted average depth, quantifying the vertical-to-horizontal proportions of the park.
Building-Cluster Shape Factor (SF)	$\mathrm{S}\mathrm{F}={\displaystyle \frac{{\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{S}}_{\mathrm{i}}}{{\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{V}}_{\mathrm{i}}}}$	The ratio of the external envelope area exposed to outdoor air to the enclosed volume of the building, indicating the geometric complexity of the built form and the extent of the heat-exchange surface.

Where C = footprint area of the ground floor (m²); A = site area (m²); H = building height (m); S = external surface area of a single building (m²); V = volume of a single building (m³); D = perpendicular (north–south) distance between adjacent buildings (m); K = short-edge length of the building (m).

. For all eighteen idealized park models, the six parameters were computed after maximizing plantable green space within allowable limits: green-space ratio was rounded to the upper bound of the prescribed range, capped at 40% and floored at 30%.

After modeling the eighteen idealized park models, the six key morphological parameters for each sample were calculated according to the calculation methods in Table 1. The relevant results are shown in

Table 2. The specific values of six key morphological parameters.

Spatial Structure	Idealized Models	BD	GSP	ABS	ABH	HDR	SF
Circular layout	A1-1	37.3%	32%	20.00 m	18.50 m	0.347	0.131
	A1-2	32.0%	38%	40.00 m	23.78 m	0.396	0.114
	A1-3	37.0%	33%	15.63 m	18.50 m	0.680	0.162
	A1-4	35.6%	34%	20.00 m	19.43 m	0.547	0.140
	A2-1	36.4%	34%	22.86 m	18.75 m	0.382	0.133
	A2-2	36.7%	33%	20.00 m	18.98 m	0.622	0.149
	A2-3	41.3%	30%	35.00 m	16.79 m	0.294	0.140
	A2-4	40.0%	30%	25.56 m	17.22 m	0.316	0.131
	A2-5	35.8%	34%	21.33 m	19.37 m	0.645	0.154
	A2-6	41.3%	30%	26.92 m	16.49 m	0.231	0.150
Evenly distributed cluster layout	B1-1	39.2%	31%	26.67 m	18.48 m	0.394	0.137
	B1-2	32.0%	38%	15.00 m	21.43 m	0.714	0.138
	B1-3	35.6%	34%	20.00 m	19.55 m	0.391	0.151
	B1-4	40.0%	30%	23.82 m	17.23 m	0.278	0.153
	B2-1	32.0%	38%	35.00 m	21.81 m	0.685	0.130
	B2-2	39.0%	31%	28.00 m	18.30 m	0.654	0.160
	B2-3	36.1%	34%	20.00 m	19.22 m	0.384	0.151
	B2-4	37.0%	33%	28.63 m	18.75 m	0.412	0.151

.

3. Simulation Results

3.1. Photovoltaic Potential Assessment and Simulation Results

3.1.1. Photovoltaic Potential Evaluation Metric

Active building photovoltaics have become the preferred renewable energy technology in most newly developed science and technology industrial parks and thus constitute a highly representative solar energy strategy. Accordingly, the study adopts photovoltaic potential as the sole indicator of renewable energy availability.

Solar Energy Generation Intensity (SEGI) is introduced as the metric for evaluating photovoltaic potential. It is defined as the annual maximum photovoltaic yield per unit gross floor area of the building cluster, expressed in kWh/(m²·a). It is calculated as follows:

$$\mathrm{SEGI} = {\displaystyle \frac{\mathrm{E}{\mathrm{G}}_{\mathrm{T}}}{{\mathrm{A}}_{\mathrm{T}}}},$$

(1)

where

• EG_T = annual total photovoltaic electricity output [kWh/a];
• A_T = total gross floor area of the building cluster [m²].

3.1.2. Photovoltaic Potential Calculation

In accordance with Chinese photovoltaic regulations and established calculation methods [27,28], the annual electricity yield of the photovoltaic system on each building surface is computed using the following equation:

$${\mathrm{E}}_{\mathrm{P}} ={ \mathrm{H}}_{\mathrm{A}} \times { \mathrm{A}}_{\mathrm{pv}} \times \mathsf{\eta } \times \mathrm{K} \times {\left(1-{\mathrm{R}}_{\mathrm{d}}\right)}^{\mathrm{N}-1},$$

(2)

where

• E_P = annual electricity yield of the photovoltaic system on the building surface [kWh/a];
• H_A = annual solar irradiation on the corresponding building surface [kWh/(m²·a)];
• A_pv = installable area of photovoltaic modules on the corresponding building surface [m²];
• η = photovoltaic conversion efficiency [%];
• K = integrated efficiency coefficient [%];
• R_d = degradation rate of the photovoltaic system [%];
• N = lifecycle of the photovoltaic system [a].

At present, there are three mainstream photovoltaic technology routes in China: crystalline silicon PV technology, thin-film PV technology, and new-type material PV technology such as perovskite [30]. According to the roadmap for the development of China’s photovoltaic industry [31], the share of PERC monocrystalline silicon solar cells in China’s photovoltaic cell market is the highest. Therefore, this study selects the parameters of PERC monocrystalline silicon PV modules as the reference for calculation in various formulas.

Based on the data statistics in the roadmap [31], the value of η is set at 23.2%. The value of K is taken as the midpoint of the commonly estimated range of 75% to 85%, which is 80%. Referring to the relevant provisions of China’s photovoltaic manufacturing industry standards and the market parameters of PERC monocrystalline silicon solar cells, and considering that PV technology will continue to gradually advance in the foreseeable future, the lifecycle N is set at 25 years. The degradation rate R_d is set at no more than 1.5% in the first year.

In the calculation of H_A, only the surfaces with annual solar irradiation exceeding the threshold are considered. The A_pv is computed using the following equation:

$${\mathrm{A}}_{\mathrm{pv}} ={ \mathrm{A}}_{\mathrm{rf}} \times {\mathrm{C}}_{\mathrm{rf}} \times { \mathrm{C}}_{\mathrm{T}},$$

(3)

where

• A_pv = installable area of photovoltaic modules on the corresponding building surface [m²];
• A_rf = area of the roof or corresponding facade [m²];
• C_rf = installation coefficient, equal to C_r for roof surfaces and 1.0 for facades;
• C_T = proportion of the roof or corresponding facade area with solar irradiation exceeding the threshold [%].

The installation coefficient C_rf is equal to C_r for roof surfaces. According to relevant studies [28], the rooftop installation coefficient C_r is influenced by factors such as the rooftop area coefficient, shading coefficient, equipment coefficient, and the effective area coefficient of photovoltaic modules, and can be calculated using following equation:

$${\mathrm{C}}_{\mathrm{r}}={\mathrm{C}}_{\mathrm{r}\mathrm{a}}\times {\mathrm{C}}_{\mathrm{f}}\times {\mathrm{C}}_{\mathrm{s}\mathrm{t}}\times {\mathrm{C}}_{\mathrm{s}}\times {\mathrm{C}}_{\mathrm{r}\mathrm{t}}\times {\mathrm{C}}_{\mathrm{c}\mathrm{o}}$$

(4)

where

• C_r = rooftop installation coefficient;
• C_ra = rooftop area coefficient;
• C_f = equipment coefficient;
• C_st = solar thermal coefficient;
• C_s = shading coefficient;
• C_rt = rooftop type coefficient;
• C_co = effective area coefficient.

Based on the investigation results, C_ra is set at 1.0. New buildings typically have dedicated equipment rooms, and the proportion of rooftop area occupied by equipment is relatively low. Hence, C_f is set at 0.9. Since solar water heating systems are not considered in this study, C_st is set at 1.0. Given that shading and obstruction are already accounted for in the radiation simulation process, C_s is set to 1.0. As the buildings in the park are predominantly flat-roofed, C_rt is set at 1.0. In accordance with relevant Chinese regulations, C_co is set at 0.8 for the Shanghai area. Consequently, Cr can be calculated to be 0.72 according to Equation (4), which can be directly employed to determine the installable area of rooftop photovoltaic modules.

After the simulation analysis obtains the radiation potential distribution of the building cluster in the park, it is necessary to screen out the building surfaces with a radiation intensity that is greater than or equal to the set threshold and calculate the proportion of the area exceeding the threshold. Based on existing studies [27,28], the threshold of radiation can be calculated by transforming the balance equation of the minimum radiation value required for photovoltaic modules to meet the system’s input and output balance over the entire life cycle. The calculation formula for the radiation threshold is shown as follows:

$$\mathrm{T}={\displaystyle \frac{{\mathrm{C}}_{\mathrm{s}\mathrm{y}\mathrm{s}}}{\mathsf{\eta }\times \mathrm{K}\times {\mathrm{C}}_{\mathrm{e}\mathrm{l}\mathrm{e}\mathrm{c}}\times {\left(1-{\mathrm{R}}_{\mathrm{d}}\right)}^{\mathrm{N}-1}}}$$

(5)

where

• T = radiation threshold [kWh/(m²·a)];
• C_sys = cost of per unit area for photovoltaic systems over entire life cycle [RMB/m²];
• η = photovoltaic conversion efficiency [%];
• K = integrated efficiency coefficient [%];
• C_elec = feed-in tariff for photovoltaic power [RMB/kWh];
• R_d = degradation rate of the photovoltaic system [%];
• N = lifecycle of the photovoltaic system [a].

Consistent values are used for the same parameters throughout the paper. Considering the feed-in guidance tariff for photovoltaic power specified in Shanghai and the subsidy policy for newly built distributed photovoltaic projects, C_elec is set at 0.5146 RMB/kWh. The value of C_sys is obtained using Equation (6) [27,28], which is a simplified formula for cost calculation. In this study, the time value of money is not considered.

$${\mathrm{C}}_{\mathrm{s}\mathrm{y}\mathrm{s}}={\mathrm{C}}_{\mathrm{i}\mathrm{n}\mathrm{s}}\times {\mathrm{P}}_{\mathrm{d}}\times \left(1+{\mathrm{R}}_{\mathrm{A}\mathrm{n}\mathrm{n}}\times \mathrm{N}\right)$$

(6)

where

• C_sys = cost of per unit area for photovoltaic systems over entire life cycle [RMB/m²];
• C_ins = initial installation cost of the photovoltaic system [RMB/W];
• P_d = power density of photovoltaic modules [W/ m²];
• R_Ann = annual system maintenance coefficient [%];
• N = lifecycle of the photovoltaic system [a].

According to the data statistics [31], C_ins is set at 3.79 RMB/W; P_d is set at 230 W/m²; and R_Ann is set at 2%. The C_sys can be calculated to be 1307.55 RMB/m². By substituting C_sys into Equation (5), the value of T in this study for Shanghai is calculated to be 652.62 kWh/(m²·a). Further comparisons can be made to calculate the proportion of the roof or corresponding facade area in the park where the radiation value exceeds the threshold T, denoted as C_T.

Given the significant differences in installable area for photovoltaic systems on roofs versus facades, and the notable impact of installation methods and angles on photovoltaic utilization, it is stipulated that photovoltaic modules are installed parallel to each building surface. The electricity generation of the photovoltaic systems on the roofs and each facade (E_p) is calculated separately and then summed to obtain the total photovoltaic electricity generation (EG_T) for the building cluster. The overall SEGI is subsequently computed using Equation (1). The simulation workflow is illustrated in Figure 1.

3.1.3. Simulated Photovoltaic Potential

The SEGI values were calculated for the 18 idealized park models following the aforementioned approach. As shown in Figure 4, the total SEGI values exhibit significant differences. The minimum total SEGI value is found in the evenly distributed cluster layout, specifically in the B1-2 type, with a value of 30.60 kWh/(m²·a). The maximum total SEGI value is observed in the circular layout, specifically in the A2-6 type, with a value of 40.14 kWh/(m²·a), which is approximately 1.31 times the minimum value. The A2-3 and A2-6 type have notably higher total SEGI values than other types, primarily because these clusters have higher building density and more uniform building volume distribution, resulting in less shading between buildings and self-shading, which allows for more effective utilization of solar radiation and thus higher photovoltaic generation potential.

In terms of average values, the average total SEGI for the circular layout is 35.31 kWh/(m²·a), while for the evenly distributed cluster layout, it is 34.52 kWh/(m²·a), with the former being higher than the latter. However, when comparing the median values, the median total SEGI for the circular layout is 33.99 kWh/(m²·a), which is lower than the evenly distributed cluster layout’s median of 35.31 kWh/(m²·a). The chart results reflect that the photovoltaic potential under the circular layout is more significantly influenced by the block morphology, and adopting a rational layout can potentially achieve photovoltaic utilization effects beyond expectations. In contrast, in the evenly distributed cluster layout, the layout’s positive impact on overall photovoltaic potential is limited, and it is more important to avoid the negative effects of shading between buildings.

3.2. Energy Use Intensity Assessment and Simulation Results

3.2.1. Energy Use Intensity Evaluation Metric

The study adopts Energy Use Intensity (EUI) as the evaluation metric, defined as the ratio of the building cluster’s annual total energy consumption (primarily cooling, heating, lighting, and equipment energy use) to its total floor area, expressed in kWh/(m²·a). It provides a straightforward indication of the overall energy use of the park. In this study, EUI will be calculated both with and without considering the impact of the urban heat island effect. The difference between the two will be used as an indicator to evaluate the impact of the urban heat island effect on building energy consumption under different layouts. The EUI calculation in this study, which accounts for the impact of the urban heat island effect on building energy consumption, is as follows:

$$\mathrm{E}\mathrm{U}\mathrm{I}={\displaystyle \frac{{\mathrm{E}\mathrm{U}}_{\mathrm{T}}}{{\mathrm{A}}_{\mathrm{T}}}},$$

(7)

where

• EU_T = Total annual energy consumption of the building cluster [kWh/a];
• A_T = Total floor area of the building cluster [m²].

3.2.2. Energy Use Intensity Evaluation Calculation

The energy consumption simulation workflow constructed in this study comprehensively considers the impact of both building cluster morphology and microclimate factors on energy use. It is built using the Ladybug Tools suite, primarily using the Dragonfly module for microclimate calculations and the model setup, and the Honeybee module for setting occupancy behaviors, operation schedules, and thermal envelope parameters. The final simulation is executed via the URBANopt platform, yielding the cooling, heating, lighting, and other electrical equipment energy consumption for each idealized park building cluster. These are summed to obtain the total energy consumption, from which EUI is calculated using Equation (7).

The specific simulation process employs the Urban Weather Generator (UWG) component within the Dragonfly module. The original UWG was first developed by Bruno Bueno for his PhD thesis at MIT in 2013. The simulation workflow is illustrated in Figure 1.

The study primarily refers to Chinese national standards to set parameters such as the thermal transmittance (K-value) of opaque and transparent envelope components, solar heat gain coefficient (SHGC), and visible light transmittance. Based on existing envelope construction sets, a new set is established for study purposes, with specific values for each part of the envelope detailed in

Table 3. Building cluster envelope parameters.

Envelope Component	K-Value [W/m2·K]	SHGC
roof	0.4	/
exterior wall	0.8	/
floor	0.8	/
exterior window	2.2	0.4

. When inputting these parameters, the thermal transmittance K-value is first converted into the corresponding thermal resistance R-value before being entered into the relevant functional component. The two values are equivalent in representing the thermal performance of the same envelope component.

Human behavior and equipment schedules involve multiple parameters in energy simulation, including occupancy rates, metabolic heat gains, lighting schedules, lighting power density, and equipment schedules. Since this study primarily focuses on the impact of block morphology on building energy consumption, human behavior and equipment parameters for the three types of functional spaces are set as constants to minimize their influence on the study. For HVAC room temperature settings, the study refers to Chinese green building and energy-saving standards, setting the winter heating temperature at 20 °C for office spaces and 18 °C for other spaces, with heating activated below these temperatures. The summer cooling temperature is uniformly set at 26 °C, with cooling activated above this temperature.

The Dragonfly tool is used in conjunction with building facade information to rapidly convert 3D building massing models into energy models that include both facade and thermal zone information. To meet the 3D modeling requirements for energy simulation, the facade information needed includes the number of floors, floor-to-floor height, and window-to-wall ratio. Buildings with the same function have certain similarities in floor-to-floor height and window-to-wall ratio. To eliminate differences in building energy performance caused by variations in these parameters in real buildings, these values are uniformly set during modeling. The facade information for each functional space is detailed in

Table 4. Window opening information settings.

Functional Type	Orientation	Window-to-Wall Ratio	Sill Height	Window Height	Window Spacing	Floor-to-Floor Height
study-and- development office	South facade	0.25	0.9 m	2.4 m	3 m	4.2 m
	North facade	0.25
	East facade	0.2
	West facade	0.2
commercial-and- catering	South facade	0.2	0.9 m	2.4 m	3 m	4.5 m
	North facade	0.2
	East facade	0.1
	West facade	0.1
cultural services	South facade	0.3	0.9 m	2.7 m	4.5 m	4.2 m
	North facade	0.2
	East facade	0.15
	West facade	0.15

.

The Dragonfly tool is employed to perform batch modeling of building volumes based on facade information, and to assign corresponding thermal zones according to function, as well as to configure human behavior and operation schedules, equipment parameters, and envelope parameters. Considering the significant differences in thermal zone partitioning in public buildings, thermal zones with the same function are merged to enhance modeling and simulation efficiency. Additionally, building protrusions and recesses within 2 m are simplified during modeling, and no shading devices are adopted for any of the idealized building cluster samples.

The study utilizes the Urban Weather Generator (UWG) to meet the needs of performance-oriented building cluster thermal environment planning. Initially developed by Bueno [32] in 2012, UWG is a physics-based simulation model designed to quantify energy exchanges between the environment and climate. Subsequent improvements by various scholars have enabled it to assess urban heat island effects and building energy consumption at the block scale. The reliability of this model in performance-oriented early block planning has been repeatedly verified across diverse climatic environments and block morphologies [23,32,33].

To incorporate microclimate impacts into the energy simulation process, the study constructs a microclimate assessment module on the Grasshopper platform using the Dragonfly module. First, the DF Model containing all building modeling information established in the first process block is read. Subsequently, the standard meteorological year data file for Shanghai is imported, and parameters such as traffic intensity, vegetation, and sensible heat are set. Finally, the influence of block morphology on the microclimate is calculated using the built-in UWG functions in Dragonfly, and new EPW files are generated based on the altered temperature, humidity, and other meteorological data. These files are then used for the final energy simulation. The microclimate parameters affecting building energy consumption are shown in

Table 5. Microclimate simulation parameters.

Data Type	Value	Data Source/Basis
meteorological data	Shanghai Standard Weather Data	https://www.ladybug.tools/epwmap/, accessed on 28 November 2023
sensible heat (excl. buildings)	5 W/m2	UWG recommended value
greenspace ratio	calculated from Table 1	as determined in this study
road albedo	0.05	the study by Boccalatte et al. [34]
vegetation albedo	0.25	the study by Boccalatte et al. [34]

. Since this study primarily focuses on the impact of building cluster morphology on overall energy consumption, conditions such as vegetation and paving are uniformly set, and the green-space ratio is calculated according to the formula in Table 1.

The disaggregated energy demand of a park building cluster is quantified for four end-uses: cooling, heating, lighting, and other equipment. The simulation engine delivers 8760 hourly values for each end-use, which are then aggregated to annual totals. The overall annual energy consumption of the cluster is the sum of these individual end-use demands.

3.2.3. Simulated Energy Use Intensity

Figure 5 presents the total energy performance of each park under the influence of the urban heat island effect. The 18 idealized park models exhibit only marginal variation in total EUI. The lowest value, 101.26 kWh/(m²·a), is found in the circular cluster A2-5, whereas the highest, 108.08 kWh m⁻² yr⁻¹, occurs in the same layout type (A2-3). The absolute difference is 6.82 kWh/(m²·a), corresponding to a 6.74% increase from the minimum. Disaggregated results reveal that, for a fixed gross floor area and nearly identical functional mixes, lighting, and other-equipment EUIs remain almost constant and together account for roughly half of the total demand. Cooling and heating EUI drive the observed differences, with cooling demand markedly exceeding heating demand. Circular layouts yield a slightly lower mean total EUI (114.74 kWh/(m²·a)) than evenly distributed clusters (115.36 kWh/(m²·a)). Nevertheless, the circular layout also presents the widest spread, encompassing both the maximum and minimum values, indicating that its energy performance is more sensitive to detailed morphological choices.

To make the impact more intuitive, the difference between the EUI considering heat island effect and without considering it is used as a measure of the impact of the heat island effect on energy consumption. As shown in Figure 6, the total EUI all increased due to heat island effect. The range of the increase was from 1.62 kWh/(m²·a) to 4.49 kWh/(m²·a), with the maximum value being 2.77 times the minimum value. The proportion of the increase in total EUI ranged from 1.39% to 4.02%. There was a significant difference in the increase in the total EUI among different parks. The layout with the least impact from heat island effect was the circular layout. A rational circular layout can reduce the impact of the heat island effect.

3.3. Integrated Energy Performance Assessment and Results

3.3.1. Integrated Energy Performance Metrics

This study evaluates the integrated energy performance of idealized park models from two complementary perspectives. First, Net Energy Use Intensity (NEUI) is employed to quantify the net energy demand under varying spatial layouts. Second, Potential Utilization Rate of Renewable Energy (PURRE) is introduced to gauge the prospective degree of on-site energy self-sufficiency afforded by each layout. The synthesis of these two metrics yields a comprehensive assessment of the energy performance of the eighteen idealized models.

• Net Energy Use Intensity (NEUI)

The Net Energy Use Intensity (NEUI) is defined, under specified conditions, as the annual difference per unit floor area between energy consumption and renewable generation, each multiplied by its respective energy conversion factor. It quantifies the absolute net energy demand of a park once renewable generation is accounted for, directly revealing the potential surplus or deficit between production and use. Consequently, NEUI serves as a core metric for evaluating integrated energy performance.

As this study treats solar PV as the sole renewable source, NEUI is computed as the difference between the heat-island-adjusted Energy Use Intensity (EUI) and the Solar Energy Generation Intensity (SEGI). A lower NEUI indicates a lower absolute net energy demand. The calculation is as follows:

$$\mathrm{NEUI} = \mathrm{EUI}-\mathrm{SEGI},$$

(8)

2.

Potential Utilization Ratio of Renewable Energy (PURRE)

The renewable-energy utilization ratio denotes the percentage of annual on-site solar electricity generation relative to total energy consumption on a per-unit-floor-area basis, thereby reflecting the energy self-sufficiency level of a park. In this study, full utilization of the generated solar energy is assumed. Hence, the concept of renewable-energy utilization ratio is extended to “potential” utilization, signifying the ratio of photovoltaic potential (SEGI) to total energy demand (EUI) rather than the ratio of actual PV output to demand.

Potential Utilization Ratio of Renewable Energy (PURRE) is therefore calculated as the quotient of Solar Energy Generation Intensity (SEGI) and heat-island-adjusted Energy Use Intensity (EUI). The calculation is as follows:

$$\mathrm{PURRE} = {\displaystyle \frac{\mathrm{SEGI}}{\mathrm{EUI}}} \times 100\%,$$

(9)

3.3.2. Integrated Energy Performance Calculation Results

Figure 7 presents the NEUI values for the 18 idealized park models. A pronounced spread is evident: the minimum NEUI of 72.09 kWh/(m²·a) occurs in the circular cluster A2-6, whereas the maximum of 85.93 kWh/(m²·a) is observed in the evenly distributed cluster B2-1, yielding an absolute difference of 13.84 kWh/(m²·a) (a 20% increase from the minimum). When the 18 idealized park models are ranked separately—first by descending total SEGI and then by ascending NEUI—A2-6, A2-3, and B2-2 recur in the top five of both lists. This repetition demonstrates that photovoltaic generation potential exerts a strong influence on the net energy use intensity. Clusters with NEUI < 80 kWh/(m²·a) typically adopt grid or courtyard layouts, distributing buildings evenly across the site without excessive clustering or pronounced height variations—thereby limiting mutual shading while maintaining relatively high building density. Moreover, the overall distribution for the circular layout remains below that of evenly distributed ones, indicating that, on average, circular layouts deliver superior energy performance.

Figure 8 presents PURRE for the 18 idealized park models. The majority of values cluster around 30%, and none exceed 40%. Ranked from highest to lowest, the top four cases—A2-6, A2-3, B2-2, and A2-4—are all courtyard or hybrid layouts with relatively high building densities. Mean PURRE is marginally higher in circular clusters (30.8%) than in evenly distributed clusters (29.9%). Overall, half of the circular cases fall below 30%, yet a few achieve markedly higher self-sufficiency, indicating that judicious planning can unlock greater renewable energy potential. Conversely, evenly distributed clusters exhibit more uniform PURRE values—just above 30%—with some under-performing examples. Collectively, circular science park layouts offer the larger potential for renewable energy utilization.

3.4. Summary of Simulation Results

All the values obtained from the simulation of the total SEGI, total EUI, NEUI, PURRE, and the increased EUI due to the urban heat island effect for the above 18 ideal park models are integrated into

Table 6. Summary results of 18 idealized park models.

Spatial Structure	Idealized Models	Total SEGI (kWh/(m2·a))	Total EUI (kWh/(m2·a))	NEUI (kWh/(m2·a))	PURRE (%)	Increased EUI Due to Heat Island (kWh/m2·a)
Circular layout	A1-1	32.98	113.83	80.85	29.0	2.24
	A1-2	33.4	116.09	82.68	28.8	4.49
	A1-3	33.48	115.76	82.28	28.9	3.03
	A1-4	33.15	115.82	82.67	28.6	3.17
	A2-1	34.49	114.11	79.61	30.2	2.63
	A2-2	35.61	116.27	80.65	30.6	3.29
	A2-3	40.14	118.07	77.94	34.0	1.62
	A2-4	35.62	112.05	76.43	31.8	2.34
	A2-5	32.15	111.26	79.11	28.9	2.14
	A2-6	42.07	114.16	72.09	36.8	2.77
Evenly distributed cluster layout	B1-1	35.14	113.2	78.06	31.0	2.45
	B1-2	30.6	114.63	84.03	26.7	2.44
	B1-3	35.44	115.84	80.39	30.6	2.84
	B1-4	35.47	114.28	78.81	31.0	2.75
	B2-1	31.87	117.8	85.93	27.1	3.20
	B2-2	37.02	114.75	77.73	32.3	2.37
	B2-3	35.3	116.22	80.91	30.4	3.26
	B2-4	35.31	116.18	80.87	30.4	3.60

. The impact of the six key morphological parameters on the simulation results is verified through the Pearson correlation analysis.

The correlation analysis results between the key morphological parameters and the SEGI are shown in Figure 9. The key morphological parameters that have a significant correlation with the total SEGI are BD, GSP, AHB, and HDR. Among them, BD and AHB have a significant level of 0.01, while GSP and HDR have a significant level of 0.05. In addition, the correlation between the total SEGI and the rooftop SEGI (0.959) is significantly higher than that with the facade SEGI (0.518). This indicates that the total SEGI is actually mainly controlled by the rooftop SEGI. Therefore, under a given floor area ratio, increasing the building density in the park, adopting relatively average building heights, and prioritizing the improvement of the rooftop SEGI can effectively increase the total SEGI. The six parks with the largest total SEGI in Table 6 are A2-6, A2-3, B2-2, A2-4, A2-2, and B1-4. Combining these with the specific key morphological parameters in Table 2, it can be seen that parks with the largest total SEGI also have relatively large BD, all close to 40%, and the ABH is as small as possible.

Correlation analysis between the six key morphological parameters and EUI is also shown in Figure 10. None of the parameters exhibits a statistically significant relationship with total EUI, indicating that they do not exert a direct, linear influence on overall energy intensity. Nevertheless, parks with lower cooling and total EUI still share recognizable morphological traits. In conjunction with Figure 5 and the data in Table 2, building groups with densities between 35% and 40% consistently display lower cooling demand. Moreover, all parks in the lower EUI have formed factors below 0.150.

Figure 11 presents the correlation analysis between the six key morphological parameters and both NEUI and PURRE. BD, GSP, AHB, and HDR all show significant associations with the two performance indicators; among them BD, GSP, and AHB reach the 0.01 significance level. This implies that—under a fixed plot ratio and height limit—parks with higher building density, lower average building height, and a more “squat” overall form achieve lower NEUI and higher PURRE. The six parks with the lowest NEUI in Table 6 are A2-6, A2-4, B2-2, A2-3, B1-1, and B1-4; the six with the highest PURRE are A2-6, A2-3, B2-2, A2-4, B1-1, and B1-4. Consistently, these parks exhibit comparatively high BD and low AHB in Table 2.

In summary, among the six key morphological parameters, BD, GSP, AHB, and HDR exert the greatest influence on energy performance. Within 18 idealized park models, four parks—A2-3, A2-4, A2-6, and B2-2—appear repeatedly among the top six performers for SEGI, NEUI, and PURRE. These parks share consistently high BD (≈40%) and low AHB (≈17 m). Most adopt a circular layout, and A2-6 invariably ranks first, demonstrating the best overall energy performance.

4. Low-Carbon Design Strategies

4.1. Morphological Characteristics of Superior Integrated Energy Performance

For new block-scale science and technology industrial parks developed at medium intensity (FAR ≈ 1.6), the following morphological attributes should be targeted during the earliest design stage to secure superior integrated energy performance:

• Development intensity: Within allowable limits, maximize building density. As shown in Table 2 and Table 6, parks with superior integrated energy performance are all high-density developments. The building densities of A2-3, A2-4, A2-6, and B2-2 are 41.3%, 40.0%, 41.3%, and 39.0%, respectively, around 40%. For new developments, a target range of 35–40% is recommended.
• Spatial structure: Adopt a circular layout as the primary form. Among the top four parks with the best overall energy performance, the circular layout dominates. The highest-performing models, A2-6 and A2-3, combine supporting facilities with or within landscaping, while the outer circle integrates research and development clusters and ancillary functions (Figure 3).
• Morphological parameters: Under the study’s prescribed FAR of 1.6, an average building height of 16–18 m (four to five stories) is optimal; where multi-story height limits permit 20–24 m, the lower bound within this range yields improved energy performance. The building-cluster shape coefficient should be minimized—expected to fall between 0.15 and 0.20 after facade articulation. For predominantly mid-rise technology parks, long facades should be oriented north–south and the height-to-depth ratio should be kept to about 0.3. North–south inter-building spacing should generally not be less than 20 m for medium-intensity sites (FAR 1.0–3.0) and should be maximized beyond this threshold to reduce mutual shading.
• Building form: As shown in Figure 3, the four parks with the best integrated energy performance are dominated by simple, rectangular or square footprints and prefer rectangular or square footprints of 2000–4000 m². Avoid complex multi-height assemblies, stagger blocks along the north–south axis, and limit continuous facade length to local code maxima to help integrated energy performance.

4.2. Energy-Performance-Driven Low-Carbon Design Strategies

Drawing on the above findings and contemporary design practices for newly built science and technology industrial parks, four low-carbon design strategies are proposed.

4.2.1. High-Density Development Strategy

For block-scale science and technology industrial parks, land area is typically constrained and floor-area ratios are fixed by statutory plans. To meet low-carbon targets while conserving land, building density should be moderately increased within these limits. The preceding analysis indicates that a medium-intensity park (FAR 1.0–3.0) achieves favorable carbon performance at a building density of 35–40%. Densities above 40% often complicate traffic organization and limit the diversity of building products; hence, they are not recommended.

Within such high-density settings, several key morphological parameters must be carefully controlled. North–south inter-building spacing should be adjusted in accordance with local codes and spatial form. To minimize energy demand, average building height should be kept as low as practicable, with minimal height variation among blocks, and should be dynamically aligned with the prescribed FAR and density. For a FAR of 2.0, an average height at the lower end of the 20–24 m range is advised, with story heights reduced wherever possible. The height-to-depth ratio should be maintained around 0.3, with buildings elongated east–west, and spatial continuity achieved mainly through horizontal adjacency rather than vertical stacking. Individual buildings should adopt simple rectangular or square footprints with limited articulations; L- or C-shaped blocks must retain a principal north–south orientation. The building-cluster form factor should be kept close to 0.15.

4.2.2. Circular Layout Strategy

Circular layouts outperform evenly distributed clusters in overall energy terms, and the A2 type—whose center combines supporting facilities with landscape—produces the greatest number of cases exhibiting low NEUI and high PURRE. Consequently, planners should prioritize circular layouts and concentrate supporting facilities within the core to create an integrated public-service module framed by landscape.

4.2.3. Supporting-Function Centralized Layout Strategy

Supporting facilities are essential to the efficient operation of science and technology industrial parks and typically account for 15–20% of gross floor area, primarily in the form of retail and public services. Clustering identical or similar functions enables targeted, energy-conservation measures tailored to uniform load profiles. The preceding simulations show that A2-type parks, in which ancillary functions are centrally grouped, consistently achieve superior energy performance, confirming the carbon-saving benefits of concentrated support facilities. When site constraints preclude a central location, designers should consolidate these functions in one corner or along one edge of the plot, shaping modestly enclosed courtyards or interior streets that enhance spatial quality while preserving the energy advantages of aggregation.

4.2.4. Carbon Sink Enhancement Strategy

Elevating the green-space ratio simultaneously mitigates the energy penalty imposed by the urban heat island effect and augments carbon sink. For high-density science-and-technology parks, a green-space ratio of 25–30% is optimal; when site constraints limit this to 20–25%, vertical and rooftop greening must be aggressively deployed.

To maximize the landscape system’s carbon sequestration potential, interventions should focus on carbon-sink expansion, multi-layer greening, and low-carbon, resource-efficient technologies.

Carbon-sink expansion entails selecting high-sequestration species and achieving a layered planting of mix-tree/shrub/grass ≈ 4:3:3 by count or cover—that maximizes biomass and soil carbon storage [35].

Multi-layer greening emphasizes vertical complexity: ground-level planting is designed to be ecologically coherent, visually permeable, and integrated with the urban green network, while facades and structures are exploited to create three-dimensional green systems.

5. Discussion and Conclusions

Compared with the cited literature, Yang et al. [8] studied the heat island effect using data from ten local climate zones delineated within Nanjing, providing finer climatic granularity and stronger regional specificity. The present work adopts Shanghai’s climate as representative of the entire Yangtze River Delta, inevitably sacrificing some local precision. Zhu et al. [26] distilled key morphological parameters and rapid-calculation tools for solar-friendly urban planning from a large sample of real cases. Their approach parallels ours—both derive idealized models from a limited base and examine how key parameters affect energy performance. But they employed over 300 real cases and constructed thousands of simulation models through combinatorial methods, achieving a more comprehensive scope in terms of simulation quantity. In comparison, our study selects the most representative cases yet falls short in research quantity. The subject of this study, block-scale science and technology industrial parks, is similar to that of J. Kanters et al. [36], both focusing on urban blocks. The final results of both studies indicate that solar energy utilization provides guidance for early-stage planning.

This study develops an integrated energy-performance assessment framework for block-scale science and technology industrial parks by explicitly coupling solar-utilization potential with urban heat island impacts. The main conclusions are:

• Based on a survey of existing parks, science and technology industrial parks were classified into two spatial typologies—circular and evenly distributed clusters—from which 18 idealized models were generated to represent mid-rise, block-scale developments.
• An assessment methodology incorporating heat-island effects was established. First, six morphological parameters were selected to characterize each idealized model. Then, Ladybug Tools coupled with Radiance simulated surface irradiance and photovoltaic potential, while Dragonfly generated microclimate-adjusted weather files for URBANopt-driven energy simulations. Finally, Net Energy Use Intensity (NEUI) and Potential Utilization Ratio of Renewable Energy (PURRE) were adopted as composite performance indicators.
• Comparative analyses revealed that block-scale parks of circular layouts with high density (about 35–40%), low average building height (about 17–18 m), and a shape factor of about 0.15 generally achieve lower NEUI and higher PURRE than evenly distributed ones. Four low-carbon design strategies were therefore proposed: high-density development guided by morphological-parameter control, circular-based spatial layout, centralized placement of supporting facilities, and carbon-sink enhancement through green technologies.

Although this study has established an evaluation system for the low-carbon design of block-scale science and technology industrial parks in the Yangtze River Delta region of China through simulation methods and proposed low-carbon design strategies to provide some guidance for the construction, there are still limitations in the research. The study uses the Shanghai area to represent the Yangtze River Delta region, which lacks specificity for particular regions. Moreover, the study establishes idealized models based on a limited number of cases for simulation, which have not been verified through real-world cases. The low-carbon design strategies derived from the study mainly focus on early-stage planning, which can be further investigated in depth. Future research will consider a variety of climatic conditions, analyze science and technology industrial parks under multiple development models, and study it in combination with actual buildings.