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Dynamic impacts of vegetation growth and urban development on microclimate and building energy consumption
植被生长和城市发展对小气候和建筑能耗的动态影响

Hanxun Fu * ©, Yuanhao Jiao © Lirui Deng, Wei Wang ©
傅汉勋 * © ， 焦元浩 © 邓丽瑞 ， 王©伟School of Architecture, Southeast University, Sipailou #2, Nanjing, Jiangsu Province 210096, China
东南大学建筑学院，中国江苏省南京市四排楼#2 210096

A R T I C L E I N F O

Keywords:  关键字：

Dynamic growth  动态增长
Vegetation  植被
Microclimate  小气候
Building energy  建筑能源
Scenario analysis  情景分析

Abstract  抽象

Urban heatwaves have emerged as a critical global challenge, exacerbating extreme weather conditions and contributing to higher building energy consumption. These challenges become particularly acute during the initial phases of urban expansion. While numerous studies have independently investigated vegetation’s effects on outdoor microclimates and building energy efficiency, the dynamic interactions between vegetation growth, urban development, and heatwave mitigation remain underexplored. This research examines these evolving interrelationships through integrated analysis of outdoor microclimate parameters and indoor energy performance in the context of climate change, using the new campus of Southeast University in China as a case study. Four scenarios are developed based on different stages of tree growth and urban construction: climate change only, as plan, stop construction, add trees in plan. Both simulations and experiments are conducted to assess the impact of the dynamic changes in these scenarios. The long-term impacts of these factors on microclimate and building energy consumption are assessed by ENVI-met and EnergyPlus tools. The results indicate that after 60 years, vegetation growth can reduce the average site temperature by approximately 3 ∘ C 3 ∘ C 3^(@)C3{ }^{\circ} \mathrm{C}, expand areas of enhanced thermal comfort, and reduce total building cooling energy consumption by 15.5 % 15.5 % 15.5%15.5 \%. Comparative analysis of the scenarios reveals that preserving existing trees or implementing early-stage plantation can significantly mitigate heatwave and reduce cooling energy demand. This research provides valuable insights for future urban renewal and sustainable city development, offering practical guidance for urban planners to optimize urban environments and improve energy efficiency.
城市热浪已成为一项关键的全球挑战，加剧了极端天气条件，并导致建筑能耗增加。在城市扩张的初始阶段，这些挑战变得尤为严峻。虽然许多研究独立调查了植被对室外小气候和建筑能源效率的影响，但植被生长、城市发展和缓解热浪之间的动态相互作用仍未得到充分探索。本研究以中国东南大学新校区为案例研究，通过对气候变化背景下的室外微气候参数和室内能源性能的综合分析来检验这些不断发展的相互关系。根据树木生长和城市建设的不同阶段，制定了四种情景：仅气候变化、作为计划、停止建设、在计划中增加树木。模拟和实验都是为了评估动态变化在这些场景中的影响。这些因素对微气候和建筑能耗的长期影响由 ENVI-met 和 EnergyPlus 工具进行评估。结果表明，60 年后，植被生长可以使平均场地温度降低约 3 ∘ C 3 ∘ C 3^(@)C3{ }^{\circ} \mathrm{C} ，扩大增强热舒适度的区域，并将建筑物的总冷却能耗降低 15.5 % 15.5 % 15.5%15.5 \% 。对情景的比较分析表明，保护现有树木或实施早期种植园可以显着缓解热浪并减少冷却能源需求。这项研究为未来的城市更新和城市可持续发展提供了宝贵的见解，为城市规划者优化城市环境和提高能源效率提供了实用指导。

1. Introduction  1. 引言

Urban expansion has become a growing trend in developing countries, driven by the need to accommodate rapidly expanding populations and economic growth (Chirisa et al., 2016; Moser, 2018). In China alone, by 2016, approximately 3500 new districts were planned to meet the demands of future population growth (K. Feng, 2018). However, this development often exacerbates environmental and climate challenges, particularly the urban heatwave. New urban areas typically experience temperatures 2 − 3 ∘ C 2 − 3 ∘ C 2-3^(@)C2-3^{\circ} \mathrm{C} higher than older districts (Taheri Shahraiyni et al., 2016; You et al., 2023), primarily due to low vegetation cover and a high concentration of built infrastructure, such as roads and buildings (Kwak et al., 2020). This kind of climate risks also generates a range of social issues, including inefficient land use and reduced urban vitality (Forsyth & Peiser, 2021; Fu et al., 2024), further complicating the sustainability of these areas. In light of this, it is essential to explore how urban development can be designed to mitigate heatwave, particularly
在适应快速增长的人口和经济增长的需求的推动下，城市扩张已成为发展中国家的一个日益增长的趋势（Chirisa et al.， 2016;Moser，2018 年）。仅在中国，到 2016 年，就规划了大约 3500 个新区，以满足未来人口增长的需求（K. Feng，2018 年）。然而，这种发展往往会加剧环境和气候挑战，尤其是城市热浪。新城区的温度通常 2 − 3 ∘ C 2 − 3 ∘ C 2-3^(@)C2-3^{\circ} \mathrm{C} 高于旧城区（Taheri Shahraiyni et al.， 2016;You et al.， 2023），主要是由于植被覆盖率低和道路和建筑物等建筑基础设施的高度集中（Kwak et al.， 2020）。这种气候风险也产生了一系列社会问题，包括土地利用效率低下和城市活力降低（Forsyth & Peiser，2021;Fu et al.， 2024），使这些领域的可持续性进一步复杂化。有鉴于此，必须探索如何设计城市发展以缓解热浪，特别是
through the strategic use of green infrastructure, such as trees. While many studies have examined the role of trees in improving urban environments, there is still limited understanding of how vegetation growth interacts with urban development and building energy consumption over time. This study seeks to contribute to this area by examining these dynamic relationships in the context of climate change, emphasizing the potential benefits of integrating vegetation into early-stage urban planning to enhance both outdoor environmental quality and indoor energy efficiency.
通过战略性地使用绿色基础设施，例如树木。虽然许多研究已经考察了树木在改善城市环境方面的作用，但对植被生长如何随着时间的推移与城市发展和建筑能源消耗相互作用的理解仍然有限。本研究旨在通过在气候变化的背景下研究这些动态关系来为这一领域做出贡献，强调将植被纳入早期城市规划以提高室外环境质量和室内能源效率的潜在好处。
To address these environmental challenges, great efforts have been made, focusing not only on optimizing urban form (e.g., land cover, vegetation and height variation) (Shen et al., 2024), but also improving high-performance building constructions. For example, Elnabawi et al. found that urban heat island effect can be effectively mitigated by using cool roofs and ‘super cool’ materials, to ensure thermal comfort of residents (Elnabawi et al., 2023). Leibnitz et al. developed a sustainable building material that can efficiently store indoor temperatures, thereby
为了应对这些环境挑战，人们付出了巨大的努力，不仅专注于优化城市形态（例如土地覆盖、植被和高度变化）（Shen et al.， 2024），还关注改进高性能建筑结构。例如，Elnabawi 等人发现，通过使用凉爽的屋顶和 “超冷 ”的材料，可以有效缓解城市热岛效应，以确保居民的热舒适度（Elnabawi et al.， 2023）。Leibnitz 等人开发了一种可持续的建筑材料，可以有效地储存室内温度，从而
reducing the reliance on mechanical system in buildings (Leibnitz et al., 2024). These advancements not only enhance the urban energy efficiency but also promote urban energy transition. Another way is to use effective tools to evaluate energy use in urban buildings during urban planning and design(Salvalai et al., 2024). In those years, urban building energy modeling (UBEM), a tool enabling large-scale energy simulations of buildings, facilitates the calculation of energy demand and saving potentials at various stages. Its advantage can adapt to various building quantities and types, building systems, boundary conditions, weather data types, and model calibration methods (Kamel, 2022), thereby assisting in low-energy urban planning and design (Ghosh, Kumar & Kumari, 2022).
减少建筑物对机械系统的依赖（Leibnitz et al.， 2024）。这些进步不仅提高了城市能源效率，还促进了城市能源转型。另一种方法是在城市规划和设计过程中使用有效的工具来评估城市建筑的能源使用情况（Salvalai et al.， 2024）。在那些年里，城市建筑能源建模 （UBEM） 是一种能够对建筑物进行大规模能源模拟的工具，有助于计算各个阶段的能源需求和节约潜力。它的优势可以适应各种建筑数量和类型、建筑系统、边界条件、天气数据类型和模型校准方法（Kamel，2022），从而协助低能耗城市规划和设计（Ghosh， Kumar & Kumari， 2022）。
Urban green spaces are also important for regulating microclimate and improving quality since they have a strong correlation with the vegetation coverage of the area. Trees, as essential components of urban green spaces, play a critical role in environmental regulation (Ibsen et al., 2024). Trees are found effective in ameliorating heatwaves and regulating the microclimate (de Quadros & Mizgier, 2023; Ghosh et al., 2022b; Ziaul & Pal, 2020). Guerri et al. found that improving the canopy cover significantly enhances the cooling effect depending on tree characteristics. (Guerri et al., 2023). Some scholars have also found that the effectiveness of trees in regulating microclimates varies according to species (Teshnehdel et al., 2020), and arrangement (Wu et al., 2019). In addition to regulating the climate, trees have been widely shown to effectively enhance human comfort in urban street environments (Morakinyo et al., 2017; Tan et al., 2016). Current research focuses on the relationship between the intrinsic properties of trees and climatic factors, as well as surrounding buildings (Susca et al., 2023). In terms of tree-climate dynamics, most of the research has concentrated on fields such as forestry and botany. For instance, Matula et al. investigated forests in the Czech Republic and discovered that climate change has shortened the growing season of trees, resulting in decreased productivity (Matula et al., 2023). Franceschi et al. found that overheated climate negatively impacts the growth of certain tree species (Franceschi et al., 2023). While existing studies have explored the effects of climate change on tree growth (Kibria et al., 2024), there remains a significant need for further investigation into localized temperature changes linked to future tree growth. Recently, some scholars have started to address this gap. Reitberger et al. introduced a GIS-based approach that simulates urban tree growth, integrating CityTree to assess the impact of tree growth on solar radiation and urban microclimates (Reitbergeret al., 2025). This work underscores the importance of considering tree growth dynamics alongside climate factors. Further research is necessary to explore the evolving relationship between tree growth and urban development.
城市绿地对于调节小气候和提高质量也很重要，因为它们与该地区的植被覆盖率有很强的相关性。树木作为城市绿地的重要组成部分，在环境监管中发挥着关键作用（Ibsen et al.， 2024）。树木被发现能有效地缓解热浪和调节小气候（de Quadros & Mizgier，2023;Ghosh 等人，2022b;Ziaul & Pal，2020 年）。Guerri 等人发现，根据树木特性，改善树冠覆盖率会显着增强冷却效果。（Guerri 等人，2023 年）。一些学者还发现，树木调节小气候的有效性因物种（Teshnehdel et al.， 2020）和排列（Wu et al.， 2019）而异。除了调节气候外，树木还被广泛证明可以有效提高人类在城市街道环境中的舒适度（Morakinyo 等人，2017 年;Tan et al.， 2016）。目前的研究重点是树木的内在特性与气候因素以及周围建筑物之间的关系（Susca et al.， 2023）。在树木气候动力学方面，大多数研究集中在林业和植物学等领域。例如，Matula 等人调查了捷克共和国的森林，发现气候变化缩短了树木的生长季节，导致生产力下降（Matula 等人，2023 年）。Franceschi 等人发现，过热的气候会对某些树种的生长产生负面影响（Franceschi 等人，2023 年）。虽然现有研究已经探讨了气候变化对树木生长的影响（Kibria 等人，2024 年），但仍需要进一步研究与未来树木生长相关的局部温度变化。 最近，一些学者开始解决这一差距。Reitberger 等人引入了一种基于 GIS 的方法，用于模拟城市树木生长，整合 CityTree 来评估树木生长对太阳辐射和城市小气候的影响（Reitbergeret al.， 2025）。这项工作强调了将树木生长动态与气候因素一起考虑的重要性。需要进一步的研究来探索树木生长与城市发展之间不断变化的关系。
Most of these microclimate studies utilize measured data (Ali & Patnaik, 2019), wind tunnel simulations (Zhao et al., 2023), and computational fluid dynamics (CFD) software. Commonly used CFD tools include FLUENT, PHOENICS, and ENVI-met (Toparlar et al., 2017). Among these tools, ENVI-met features a more comprehensive plant module, allowing for better simulation of the interactions between vegetation and the thermal environment, and is thus widely employed in research related to green spaces and plant dynamics (Morakinyo et al., 2018). For instance, Wong et al. utilized ENVI-met software alongside GIS to demonstrate that a reduction in green space in line with the National University of Singapore’s master plan would lead to an approximate increase of 1 ∘ C 1 ∘ C 1^(@)C1^{\circ} \mathrm{C} in campus temperatures (Wong & Jusuf, 2008). Similarly, Pastore et al. integrated ENVI-met with the building energy simulation program Energy Plus and found that a green environment could lower internal building temperatures by approximately 3.4 ∘ C 3.4 ∘ C 3.4^(@)C3.4^{\circ} \mathrm{C} (Pastore et al., 2017). To validate and test what kinds of benefits of performance under different design schemes, scenario analysis is widely used to assist ENVI-met or other simulation tools to assess the role of the impact of each variable to effectively analyze future uncertainty (Aboelata & Sodoudi, 2019). In microclimate studies, Antoniou et al. analyzed the impacts of climate change on urban microclimates
这些微气候研究大多利用测量数据（Ali & Patnaik，2019）、风洞模拟（Zhao et al.， 2023）和计算流体动力学（CFD）软件。常用的 CFD 工具包括 FLUENT、PHOENICS 和 ENVI-met（Toparlar 等人，2017 年）。在这些工具中，ENVI-met 具有更全面的植物模块，可以更好地模拟植被与热环境之间的相互作用，因此被广泛用于与绿色空间和植物动力学相关的研究（Morakinyo et al.， 2018）。例如，Wong 等人利用 ENVI-met 软件和 GIS 来证明，根据新加坡国立大学的总体规划减少绿色空间将导致校园温度的 1 ∘ C 1 ∘ C 1^(@)C1^{\circ} \mathrm{C} 大约升高（Wong & Jusuf，2008）。同样，Pastore 等人将 ENVI-met 与建筑能源模拟程序 Energy Plus 集成，发现绿色环境可以将建筑物内部温度降低约 3.4 ∘ C 3.4 ∘ C 3.4^(@)C3.4^{\circ} \mathrm{C} （Pastore 等人，2017 年）。为了验证和测试在不同设计方案下性能有哪些好处，场景分析被广泛用于协助 ENVI-met 或其他模拟工具评估每个变量的影响作用，以有效地分析未来的不确定性（Aboelata & Sodoudi，2019）。在小气候研究中，Antoniou 等人分析了气候变化对城市小气候的影响
and pedestrian thermal comfort within a complex district in Nicosia, Cyprus, using measured data from 2010 and future scenarios for 2050 (Antoniou et al., 2024). Haeri et al. assessed various strategies to mitigate daytime microclimate effects across five scenarios for a shopping street in Kuala Lumpur, Malaysia (Haeri et al., 2023).
以及塞浦路斯尼科西亚一个复杂区内的行人热舒适度，使用 2010 年的测量数据和 2050 年的未来情景（Antoniou 等人，2024 年）。Haeri 等人在马来西亚吉隆坡的一条购物街的五种情景中评估了减轻白天小气候影响的各种策略（Haeri 等人，2023 年）。
Despite the known benefits of vegetation in regulating urban climates, their role is often limited during the early stages of urban development, as they typically remain at the seedling stage. Existing studies focus primarily on static analyses of vegetation or building impacts, overlooking the dynamic processes occurring during the planning and construction phases. This study tries to fill this gap by employing scenario analysis to explore the dynamic relationship between vegetation growth, urban development, and climate changes. Using the Southeast University campus in Nanjing, China, as a case study, this research model different development pathways to predict microclimate and energy consumption outcomes under various scenarios. Fig. 1 illustrates the associated variables and dynamic mechanisms in this study. The results will offer valuable data to guide urban planners in optimizing the construction timeline, providing practical solutions to mitigate urban heatwave and reduce energy consumption in newly developed urban districts.
尽管植被在调节城市气候方面具有已知的好处，但在城市发展的早期阶段，它们的作用往往是有限的，因为它们通常仍处于幼苗阶段。现有的研究主要集中在植被或建筑影响的静态分析上，而忽略了规划和施工阶段发生的动态过程。本研究试图通过采用情景分析来探索植被生长、城市发展和气候变化之间的动态关系，从而填补这一空白。本研究以中国南京的东南大学校园为案例研究，对不同的发展路径进行建模，以预测各种情景下的微气候和能源消耗结果。图 1 说明了本研究中的相关变量和动态机制。研究结果将提供有价值的数据，指导城市规划者优化施工时间表，为缓解城市热浪和减少新开发城区的能源消耗提供实用的解决方案。

2. Methodology  2. 方法

2.1. The logic of this study
2.1. 本研究的逻辑

Fig. 2 presents the methodological flow of this study, which consists of four parts.
图 2 显示了本研究的方法流程，它由四个部分组成。
Firstly, based on actual measurements, research data, and planning documents, a climate change database, a tree growth model library, and a building model library were constructed for the study area. These models not only reflect the climate changes resulting from future urban development and morphological changes during tree growth but also encompass the impacts of planning and construction scenarios on construction volume. Subsequently, four scenarios were selected, and two time points were extracted for each scenario to serve as the basis for simulation. In the third step, microclimate and energy consumption were simulated for each development scenario, considering both the direct effects of tree growth on climatic factors and the indirect contributions of these changes to building energy consumption. Finally, the simulation results for each scenario were compared to analyze variations in temperature, wind speed, and comfort, as well as the energy consumption of buildings under these changes, leading to the identification of a more appropriate development path. This study not only elucidates the complex relationships between climate change, tree growth, and building energy consumption but also provides a significant theoretical foundation and practical guidance on the scientific utilization of vegetation resources, optimization of the microclimate environment, and the achievement of energy savings and emission reductions in the planning, construction, and subsequent management of new areas.
首先，基于实际实测数据、研究资料和规划文件，为研究区构建了气候变化数据库、树木生长模型库和建筑模型库;这些模型不仅反映了未来城市发展和树木生长过程中的形态变化，还涵盖了规划和施工情景对施工量的影响。随后，选择了 4 个场景，并为每个场景提取了 2 个时间点作为仿真的基础。在第三步中，模拟了每种开发情景的小气候和能源消耗，同时考虑了树木生长对气候因素的直接影响以及这些变化对建筑能耗的间接贡献。最后，对每种情景的仿真结果进行比较，分析温度、风速和舒适度的变化，以及这些变化下建筑物的能耗，从而确定更合适的发展路径。本研究不仅阐明了气候变化、树木生长和建筑能耗之间的复杂关系，而且为新区规划、建设和后续管理中的植被资源的科学利用、微气候环境的优化以及实现节能减排提供了重要的理论基础和实践指导。

2.2. Field measurement  2.2. 现场测量

The Jiulonghu Campus of Southeast University selected for the study is located in Jiangning Development District, Nanjing City, Jiangsu Province, China (longitude: 118.83, latitude: 31.88), which belongs to the northern subtropical monsoon climate zone with a mild climate and an annual average temperature of 17.6 ∘ C 17.6 ∘ C 17.6^(@)C17.6^{\circ} \mathrm{C}, with a maximum temperature of 40.4 ∘ C 40.4 ∘ C 40.4^(@)C40.4^{\circ} \mathrm{C} in summer. As the new campus of Southeast University, the campus is an important part of Nanjing Jiangning Development District. Covering 2.5 km 2 2.5 km 2 2.5km^(2)2.5 \mathrm{~km}^{2}, the campus has been operational since 2006, featuring phased development patterns.
入选研究的东南大学九龙湖校区位于中国江苏省南京市江宁开发区（经度：118.83，纬度：31.88），属于北亚热带季风气候区，气候温和，年平均气温为 17.6 ∘ C 17.6 ∘ C 17.6^(@)C17.6^{\circ} \mathrm{C} ，夏季最高气温为 40.4 ∘ C 40.4 ∘ C 40.4^(@)C40.4^{\circ} \mathrm{C} 。作为东南大学的新校区，该校区是南京江宁开发区的重要组成部分。覆盖 2.5 km 2 2.5 km 2 2.5km^(2)2.5 \mathrm{~km}^{2} ，园区自 2006 年开始运营，采用分阶段开发模式。
According to the latest remote sensing image, the main buildings on the campus are already in use. In comparison to the campus plan, there remain significant undeveloped areas in the northern and eastern sections. Through the field research, it is determined that the primary street
根据最新的遥感图像，园区内的主要建筑已经投入使用。与校园规划相比，北部和东部仍有大量未开发区域。通过实地调研，确定主要街道
Fig. 1. The associated variables and dynamic mechanisms in this study.
图 1.本研究中的相关变量和动态机制。
Fig. 2. The logic of the proposed framework in this study.
图 2.本研究中拟议框架的逻辑。
and shade tree on the campus is Cinnamomum camphora, while the predominant tree species in the plaza is Magnolia grandiflora. Both trees are approximately 15 years old, and photographs illustrating their current condition are presented in Fig. 3.
校园内的遮荫树是 Cinnamomum camphora，而广场上的主要树种是 Magnolia grandiflora。这两棵树的树龄约为 15 年，图 3 显示了它们当前状况的照片。
Aerial photography and infrared remote sensing scanning were conducted using a DJI Mavic 3T drone to obtain surface temperature
使用 DJI Mavic 3T 无人机进行航拍和红外遥感扫描，以获取表面温度
measurements of the site (measurement date: July 31, 2024, 15:00). Fig. 4 illustrates the measurement tools employed during the site survey, as well as the eight locations where surface temperature was recorded. The data obtained from these measurements will serve as the basis for model validation in Section 3.1.
现场测量（测量日期：2024 年 7 月 31 日 15：00）。图 4 说明了现场勘测期间使用的测量工具，以及记录表面温度的八个位置。从这些测量中获得的数据将作为第 3.1 节中模型验证的基础。
Fig. 3. Basic measurement of site and trees. (a)Location of the study area. (b) Remote sensing images of the current situation. © Planning of the study area. (d) Photos of the current status of the trees.
图 3.场地和树木的基本测量。（一）研究区域的位置。（b） 当前情况的遥感图像。© 研究区域的规划。（d） 树木现状的照片。

2.3. Model setup and simulation
2.3. 模型设置和仿真

2.3.1. Data acquisition and scenario setting
2.3.1. 数据采集和场景设置

This study utilizes two primary types of data: morphological data and climate data. The morphological data encompasses tree growth data and building morphology data. The tree data is sourced from field measurements Zhang, 2010), with specific values presented in Table 1. Since the model building involves only the geometric accuracy of the trees, changes in tree height and crown width are selected as the indicators of tree growth. The building morphology data is obtained from remote sensing satellite imagery and the master plan of Southeast University Jiulonghu Campus in 2021, which indicates an increase in development intensity, resulting in a total floor area approximately 1.5 times greater than the existing area.
本研究利用两种主要类型的数据：形态数据和气候数据。形态数据包括树木生长数据和建筑形态数据。树木数据来自田间测量 Zhang，2010 年），具体值如表 1 所示。由于模型构建仅涉及树木的几何精度，因此选择树木高度和树冠宽度的变化作为树木生长的指标。建筑形态数据来源于遥感卫星影像和东南大学九龙湖校区 2021 年总体规划，表明开发强度增加，导致总建筑面积比现有面积大约 1.5 倍。
The climate data consists of meteorological parameters for current microclimate simulation input file (EPW) reflecting current conditions, sourced from the official EnergyPlus meteorological file (https://www. energyplus.net/weather). This file includes data on temperature, wind speed, precipitation, and other relevant factors for Nanjing, the city in which the study area is located.
气候数据包括反映当前状况的当前微气候模拟输入文件 （EPW） 的气象参数，来源于官方 EnergyPlus 气象文件 （https://www。energyplus.net/weather）。此文件包含研究区域所在城市南京的温度、风速、降雨量和其他相关因素的数据。
Future climate data is derived from the IPCC AR6 Future Scenario Projections with Government Intervention Representative Concentration Pathways (RCPs) (Strandsbjerg Tristan Pedersen et al., 2021). In this study, the RCP4.5 scenario is selected for future climate projections.
未来气候数据来自 IPCC AR6 未来情景预测与政府干预代表性集中路径 （RCP）（Strandsbjerg Tristan Pedersen 等人，2021 年）。在本研究中，选择了 RCP4.5 情景作为未来的气候预测。
It represents a medium emission scenario and is highly consistent with China’s climate policies. The projected climate changes under RCP4.5 (e.g., moderate temperature increases and precipitation pattern shifts) have a relatively mild impact on vegetation growth, making it suitable for studying the dynamic interactions between vegetation and urban development. The characteristics and comparisons of each commonly used RCP are shown in Table 2.
它代表了中等排放情景，与中国的气候政策高度一致。RCP4.5 下预估的气候变化 （例如，适度的温度升高和降水模式变化） 对植被生长的影响相对较小，因此适合研究植被与城市发展之间的动态相互作用。表 2 显示了每种常用 RCP 的特性和比较。
The EPW files are generated using Meteonorm 8.2, a widely recognized climate data software with advanced algorithms to simulate extreme weather years. Python are then utilized to modify the Meteonorm-generated EPW files, ensuring that the building energy simulations accurately reflected the combined impacts of climate change and vegetation growth.
EPW 文件是使用 Meteonorm 8.2 生成的，Meteonorm 8.2 是一款广受认可的气候数据软件，具有模拟极端天气年份的高级算法。然后使用 Python 修改 Meteonorm 生成的 EPW 文件，确保建筑能源模拟准确反映气候变化和植被生长的综合影响。
The scenarios are divided into four categories: BL, A, B, and C, with three time periods selected for each: the present, 30 years in the future, and 60 years in the future. The BL scenario serves as the baseline, not accounting for tree growth and considering only climate data as a variable. Scenario A involves constructing the campus according to the original planning sequence and building specifications. Scenario B entails halting all construction from the present time. Scenario C focuses on retaining more trees based on the original plan, allowing only tree growth as a variable. Nine simulations (N, BL1, BL2, A1, A2, B1, B2, C1, C2) will be conducted across these four scenarios to investigate the relationship between tree growth, building construction, climate, and
这些情景分为四类：BL、A、B 和 C，每类都选择了三个时间段：现在、未来 30 年和未来 60 年。BL 情景作为基线，不考虑树木生长，仅将气候数据视为变量。场景 A 涉及根据原始规划顺序和建筑规范建造园区。情景 B 需要从现在开始停止所有建设。情景 C 侧重于在原始计划的基础上保留更多树木，只允许树木生长作为变量。将在这四种情景中进行九次模拟（N、BL1、BL2、A1、A2、B1、B2、C1、C2），以研究树木生长、建筑施工、气候和
Fig. 4. Field measurements of mean radiant temperature. Measurement tool description (left) and Aerial photos and thermal image of 8 typical measurement points. (right).
图 4.平均辐射温度的现场测量。测量工具描述（左）和 8 个典型测量点的航拍照片和热图像。（右）。
Table 1  表 1
The measured growth data of trees.
测量的树木生长数据。
Species  物种 Age  年龄 Height(m)  高度（m） Crown width(m)  冠宽（m）
Cinnamomum camphora  樟树 Cinnamomum camphora 15 6.4 4.2
45 13.2 10.6
75 15.41 18.33
Magnolia grandiflora  大花木兰 15 5.3 3.8
45 14.5 9.2
75 18.97 16.72
Species Age Height(m) Crown width(m) Cinnamomum camphora 15 6.4 4.2 45 13.2 10.6 75 15.41 18.33 Magnolia grandiflora 15 5.3 3.8 45 14.5 9.2 75 18.97 16.72| Species | Age | Height(m) | Crown width(m) | | :--- | :--- | :--- | :--- | | Cinnamomum camphora | 15 | 6.4 | 4.2 | | | 45 | 13.2 | 10.6 | | | 75 | 15.41 | 18.33 | | Magnolia grandiflora | 15 | 5.3 | 3.8 | | | 45 | 14.5 | 9.2 | | | 75 | 18.97 | 16.72 |
Table 2  表 2
Comparison of RCP scenarios.
RCP 场景的比较。
RCP scenario  RCP 方案 Radiative forcing  辐射强迫 Emission pathways  排放途径 Applicability  适用性
RCP 2.6 2.6 W / m 2 2.6 W / m 2 2.6W//m^(2)2.6 \mathrm{~W} / \mathrm{m}^{2} Low  低 Over-optimism and underestimation of future climate change challenges
对未来气候变化挑战的过度乐观和低估
RCP 4.5 4.5 W / m 2 4.5 W / m 2 4.5W//m^(2)4.5 \mathrm{~W} / \mathrm{m}^{2} Medium  中等 Fits in with current policies and suitable for vegetation studies
符合当前政策，适用于植被研究
RCP 6.0  RCP 6.0 版本 6.0 W / m 2 6.0 W / m 2 6.0W//m^(2)6.0 \mathrm{~W} / \mathrm{m}^{2} Medium  中等 Lower policy coherence  较低的策略一致性
RCP 8.5 8.5 W / m 2 8.5 W / m 2 8.5W//m^(2)8.5 \mathrm{~W} / \mathrm{m}^{2} High  高 Frequency of extreme weather events affecting the stability of vegetation growth
影响植被生长稳定性的极端天气事件频率
RCP scenario Radiative forcing Emission pathways Applicability RCP 2.6 2.6W//m^(2) Low Over-optimism and underestimation of future climate change challenges RCP 4.5 4.5W//m^(2) Medium Fits in with current policies and suitable for vegetation studies RCP 6.0 6.0W//m^(2) Medium Lower policy coherence RCP 8.5 8.5W//m^(2) High Frequency of extreme weather events affecting the stability of vegetation growth| RCP scenario | Radiative forcing | Emission pathways | Applicability | | :--- | :--- | :--- | :--- | | RCP 2.6 | $2.6 \mathrm{~W} / \mathrm{m}^{2}$ | Low | Over-optimism and underestimation of future climate change challenges | | RCP 4.5 | $4.5 \mathrm{~W} / \mathrm{m}^{2}$ | Medium | Fits in with current policies and suitable for vegetation studies | | RCP 6.0 | $6.0 \mathrm{~W} / \mathrm{m}^{2}$ | Medium | Lower policy coherence | | RCP 8.5 | $8.5 \mathrm{~W} / \mathrm{m}^{2}$ | High | Frequency of extreme weather events affecting the stability of vegetation growth |
energy consumption. The graphical representations of the scenarios are provided in Table 3-Table 6.
能量消耗。表 3-表 6 中提供了这些场景的图形表示。

2.3.2. Microclimate simulation
2.3.2. 微气候模拟

The study area for ENVI-met is digitized using a grid resolution of 15 m × 15 m × 5 m m × 15 m × 5 m m xx15 m xx5mm \times 15 m \times 5 \mathrm{~m}, resulting in a total of 100 × 100 × 50 100 × 100 × 50 100 xx100 xx50100 \times 100 \times 50 grids, which corresponds to an overall extent of 1500 m × 1500 m 1500 m × 1500 m 1500mxx1500m1500 \mathrm{~m} \times 1500 \mathrm{~m}. This model is specifically designed for the designated study area of ENVI-met. Based on the current satellite imagery and planning documents, the locations of trees and buildings are accurately incorporated into the model, as illustrated in Fig. 5. According to the field research and remote sensing image, appropriate underlay and building materials are selected in the software, and the material selection and specific parameters are presented in Table 7.
ENVI-met 的研究区域使用 15 的格网分辨率进行数字化 m × 15 m × 5 m m × 15 m × 5 m m xx15 m xx5mm \times 15 m \times 5 \mathrm{~m} ，从而得到格网总数 100 × 100 × 50 100 × 100 × 50 100 xx100 xx50100 \times 100 \times 50 ，这对应于 1500 m × 1500 m 1500 m × 1500 m 1500mxx1500m1500 \mathrm{~m} \times 1500 \mathrm{~m} 的总体范围。该模型是专门为 ENVI-met 的指定研究区域设计的。根据当前的卫星图像和规划文件，树木和建筑物的位置被准确地纳入模型，如图 5 所示。根据野外调研和遥感影像，在软件中选择合适的衬垫和建筑材料，材料选择和具体参数如表 7 所示。
3D plant models are created in the Albero component in ENVI-met, which allows customization of parameters such as height, crown
在 ENVI-met 的 Albero 组件中创建 3D 植物模型，允许自定义参数，例如高度、冠部
width, and foliage density. (Karimi et al., 2020) In this study, two typical trees on the campus, Cinnamomum camphora and Magnolia grandiflora, are modeled. Both species have broadleaf shapes, a leaf shortwave albedo of 0.18 , and a leaf shortwave transmittance of 0.3 , referenced from studies in the same climatic zones and cities (Feng et al., 2021; Lai et al., 2023). The settings for tree height and crown spread at three growth stages are shown in Fig. 6.
宽度和叶子密度。（Karimi 等人，2020 年）在这项研究中，对校园内的两种典型树木 Cinnamomum camphora 和 Magnolia grandiflora 进行了建模。这两个物种都具有阔叶形状，叶片短波反照率为 0.18，叶片短波透射率为 0.3，参考自同一气候区和城市的研究（Feng et al.， 2021;Lai et al.， 2023）。图 6 显示了三个生长阶段的树高和树冠展开设置。
The 15-hour simulation (07:00-22:00 local time) captures diurnal thermal variations. A typical summer day in Nanjing, specifically July 31, was selected as the simulation date to accurately reflect the impact of trees on site temperature. The meteorological data input into the model for this date is presented in Table 8.
15 小时的模拟（当地时间 07：00-22：00）捕获了昼夜热变化。选择南京典型的夏日，特别是 7 月 31 日作为模拟日期，以准确反映树木对场地温度的影响。表 8 显示了输入到模型中的该日期的气象数据。
Physiological Equivalent Temperature (PET) is a thermal index derived from the Munich Energy Balance Model for Individuals (MEMI), which simulates the thermal conditions of the human body. This model considers the influence of key meteorological parameters, activity levels, clothing, and individual characteristics on thermal comfort, allowing for the assessment of thermal sensation and tolerance in various studies. The model assumes that the body reaches thermal equilibrium at:
生理等效温度 （PET） 是从慕尼黑个人能量平衡模型 （MEMI） 得出的热指数，该模型模拟人体的热条件。该模型考虑了关键气象参数、活动水平、服装和个人特征对热舒适度的影响，从而可以在各种研究中评估热感觉和耐受性。该模型假设物体在以下时间达到热平衡：
M + W + R + C + E D + E R e + E S w + S = 0 M + W + R + C + E D + E R e + E S w + S = 0 M+W+R+C+E_(D)+E_(Re)+E_(Sw)+S=0M+W+R+C+E_{D}+E_{R e}+E_{S w}+S=0
In Eq. (1), M M MM refers to metabolic rate, W W WW refers to physical work output, R R RR refers to net radiation of the body, C C CC refers to convective heat flow, E D E D E_(D)E_{D} represents latent heat flow to evaporate water into water vapor diffusing through the skin, E R e E R e E_(Re)E_{R e} represents sum of heat flows for heating and hu midifying the inspired air, E S w E S w E_(Sw)E_{S w} represents heat flow due to evap oration of sweat, and S S SS refers to storage heat flow for heating or cooling the body mass (Höppe, 1999). In this study, the PET of the site is calculated by the Biomet module of ENVI-met, and the values for all grids at a height of 1.5 m are exported and analyzed.
在方程（1）中， M M MM 指代谢率， W W WW 指体力劳动产出， R R RR 指身体的净辐射， C C CC 指对流热流， E D E D E_(D)E_{D} 表示潜热流将水蒸发成水蒸气，扩散到皮肤中， E R e E R e E_(Re)E_{R e} 表示用于加热和胡使吸入空气的热流之和， E S w E S w E_(Sw)E_{S w} 表示由于汗液蒸发而产生的热流， 并 S S SS 是指用于加热或冷却体重的储存热流（Höppe，1999）。在本研究中，通过 ENVI-met 的 Biomet 模块计算了现场的 PET，并导出和分析了 1.5 m 高度处所有网格的值。

2.3.3. Building energy simulation
2.3.3. 建筑能量模拟

The building energy simulation in this study is primarily conducted using the Rhino platform and Grasshopper. Rhino is a widely used modeling software in architectural design and industrial manufacturing, while Grasshopper is a visual programming tool integrated with Rhino. Commonly employed by architects for parametric design and building performance simulation, Grasshopper enables building energy simulations within its environment using the EnergyPlus engine. The building
本研究中的建筑能量模拟主要使用 Rhino 平台和 Grasshopper 进行。Rhino 是建筑设计和工业制造中广泛使用的建模软件，而 Grasshopper 是与 Rhino 集成的可视化编程工具。Grasshopper 通常被建筑师用于参数化设计和建筑性能模拟，它可以使用 EnergyPlus 引擎在其环境中进行建筑能量模拟。建筑
Table 3  表 3
Scenario BL: Only climate change.
情景 BL：只有气候变化。
Model elements  模型元素 Cinnamomum camphora  樟树 Cinnamomum camphora Magnolia grandiflora  大花木兰 Buildings  建筑物 Climate  气候
N: Now  N：现在
qquad\qquad
BL1:30years  BL1：30 岁
qquad\qquad
BL2:60years  BL2：60 岁
qquad\qquad
Model elements Cinnamomum camphora Magnolia grandiflora Buildings Climate N: Now https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-06.jpg?height=63&width=51&top_left_y=437&top_left_x=774 https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-06.jpg?height=68&width=54&top_left_y=439&top_left_x=1018 qquad https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-06.jpg?height=81&width=81&top_left_y=428&top_left_x=1491 BL1:30years https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-06.jpg?height=63&width=51&top_left_y=532&top_left_x=774 https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-06.jpg?height=63&width=54&top_left_y=532&top_left_x=1018 qquad https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-06.jpg?height=79&width=106&top_left_y=524&top_left_x=1483 BL2:60years https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-06.jpg?height=62&width=49&top_left_y=653&top_left_x=775 https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-06.jpg?height=66&width=54&top_left_y=651&top_left_x=1018 qquad https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-06.jpg?height=98&width=158&top_left_y=631&top_left_x=1457| Model elements | Cinnamomum camphora | Magnolia grandiflora | Buildings | Climate | | :--- | :--- | :--- | :--- | :--- | | N: Now | ![](https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-06.jpg?height=63&width=51&top_left_y=437&top_left_x=774) | ![](https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-06.jpg?height=68&width=54&top_left_y=439&top_left_x=1018) | $\qquad$ | ![](https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-06.jpg?height=81&width=81&top_left_y=428&top_left_x=1491) | | BL1:30years | ![](https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-06.jpg?height=63&width=51&top_left_y=532&top_left_x=774) | ![](https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-06.jpg?height=63&width=54&top_left_y=532&top_left_x=1018) | $\qquad$ | ![](https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-06.jpg?height=79&width=106&top_left_y=524&top_left_x=1483) | | BL2:60years | ![](https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-06.jpg?height=62&width=49&top_left_y=653&top_left_x=775) | ![](https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-06.jpg?height=66&width=54&top_left_y=651&top_left_x=1018) | $\qquad$ | ![](https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-06.jpg?height=98&width=158&top_left_y=631&top_left_x=1457) |
Table 4  表 4
Scenario A: As planned.
情景 A：按计划进行。
Model elements  模型元素 Cinnamomum  樟属 Magnolia grandiflora  大花木兰 Buildings  建筑物 Climate  气候
camphora  樟脑
N: Now  N：现在 P ↑ ↑ uarr\uparrow
A1:30years  A1：30 岁
A2:60years  A2：60 岁
Model elements Cinnamomum Magnolia grandiflora Buildings Climate camphora N: Now P uarr https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-06.jpg?height=63&width=110&top_left_y=1135&top_left_x=1231 https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-06.jpg?height=76&width=81&top_left_y=1129&top_left_x=1491 A1:30years https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-06.jpg?height=81&width=60&top_left_y=1219&top_left_x=767 https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-06.jpg?height=81&width=72&top_left_y=1221&top_left_x=1009 https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-06.jpg?height=70&width=118&top_left_y=1229&top_left_x=1225 https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-06.jpg?height=80&width=106&top_left_y=1217&top_left_x=1483 A2:60years https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-06.jpg?height=89&width=70&top_left_y=1329&top_left_x=762 https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-06.jpg?height=107&width=89&top_left_y=1324&top_left_x=1005 https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-06.jpg?height=81&width=175&top_left_y=1337&top_left_x=1194 https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-06.jpg?height=102&width=160&top_left_y=1327&top_left_x=1454| Model elements | Cinnamomum | Magnolia grandiflora | Buildings | Climate | | :--- | :--- | :--- | :--- | :--- | | | camphora | | | | | N: Now | P | $\uparrow$ | ![](https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-06.jpg?height=63&width=110&top_left_y=1135&top_left_x=1231) | ![](https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-06.jpg?height=76&width=81&top_left_y=1129&top_left_x=1491) | | A1:30years | ![](https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-06.jpg?height=81&width=60&top_left_y=1219&top_left_x=767) | ![](https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-06.jpg?height=81&width=72&top_left_y=1221&top_left_x=1009) | ![](https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-06.jpg?height=70&width=118&top_left_y=1229&top_left_x=1225) | ![](https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-06.jpg?height=80&width=106&top_left_y=1217&top_left_x=1483) | | A2:60years | ![](https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-06.jpg?height=89&width=70&top_left_y=1329&top_left_x=762) | ![](https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-06.jpg?height=107&width=89&top_left_y=1324&top_left_x=1005) | ![](https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-06.jpg?height=81&width=175&top_left_y=1337&top_left_x=1194) | ![](https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-06.jpg?height=102&width=160&top_left_y=1327&top_left_x=1454) |
Table 5  表 5
Scenario B: Stop construction.
场景 B：停止施工。
Model elements  模型元素 Cinnamomum camphora  樟树 Cinnamomum camphora Magnolia grandiflora  大花木兰 Buildings  建筑物 Climate  气候
N: Now  N：现在 P ↑ ↑ uarr\uparrow qquad\qquad
B1:30years  B1：30 岁
B2:60years  B2：60 岁
Model elements Cinnamomum camphora Magnolia grandiflora Buildings Climate N: Now P uarr qquad https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-06.jpg?height=81&width=85&top_left_y=1821&top_left_x=1493 B1:30years https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-06.jpg?height=76&width=59&top_left_y=1918&top_left_x=770 https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-06.jpg?height=87&width=74&top_left_y=1913&top_left_x=1010 https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-06.jpg?height=72&width=113&top_left_y=1920&top_left_x=1227 https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-06.jpg?height=83&width=111&top_left_y=1910&top_left_x=1485 B2:60years https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-06.jpg?height=93&width=75&top_left_y=2026&top_left_x=762 https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-06.jpg?height=105&width=89&top_left_y=2022&top_left_x=1005 https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-06.jpg?height=74&width=108&top_left_y=2040&top_left_x=1230 https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-06.jpg?height=98&width=160&top_left_y=2023&top_left_x=1454| Model elements | Cinnamomum camphora | Magnolia grandiflora | Buildings | Climate | | :--- | :--- | :--- | :--- | :--- | | N: Now | P | $\uparrow$ | $\qquad$ | ![](https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-06.jpg?height=81&width=85&top_left_y=1821&top_left_x=1493) | | B1:30years | ![](https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-06.jpg?height=76&width=59&top_left_y=1918&top_left_x=770) | ![](https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-06.jpg?height=87&width=74&top_left_y=1913&top_left_x=1010) | ![](https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-06.jpg?height=72&width=113&top_left_y=1920&top_left_x=1227) | ![](https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-06.jpg?height=83&width=111&top_left_y=1910&top_left_x=1485) | | B2:60years | ![](https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-06.jpg?height=93&width=75&top_left_y=2026&top_left_x=762) | ![](https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-06.jpg?height=105&width=89&top_left_y=2022&top_left_x=1005) | ![](https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-06.jpg?height=74&width=108&top_left_y=2040&top_left_x=1230) | ![](https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-06.jpg?height=98&width=160&top_left_y=2023&top_left_x=1454) |
functions in the study area are categorized based on current research findings and planning drawings. Table 9 presents the parameter settings for different building types on campus. Based on the microclimate simulation results, python is employed to modify the EPW weather files. These modified EPW files, which incorporate the climatic effects of tree growth, are then used as input files for building energy consumption simulations. The specific parameter settings for building energy consumption simulations are detailed in Table 10.
研究区域中的功能根据当前的研究结果和规划图纸进行分类。表 9 显示了园区内不同建筑类型的参数设置。基于微气候模拟结果，使用 python 修改 EPW 天气文件。这些修改后的 EPW 文件包含树木生长的气候影响，然后用作建筑能耗模拟的输入文件。表 10 详细介绍了建筑能耗模拟的具体参数设置。
The study compares actual monthly electricity consumption with
该研究将每月实际用电量与
building energy model simulations. The calibration employs mean absolute error (MAE) and cross validation root mean squared error (CVRMSE) metrics, following a 20 % error margin from prior research (Lin et al., 2023; Nagpal & Reinhart, 2018). This approach has been widely applied and recognized in the field. For models that do not meet the accuracy requirements, parameter adjustments are made iteratively, with each adjustment step set to 0.1 . The calibration objective is to align the UBEM-simulated monthly energy consumption with the actual monthly electricity consumption of the buildings. Table 9 illustrates the
建筑能量模型模拟。校准采用平均绝对误差 （MAE） 和交叉验证均方根误差 （CVRMSE） 指标，遵循先前研究的 20% 误差范围（Lin 等人，2023 年;Nagpal & Reinhart，2018 年）。这种方法已在该领域得到广泛应用和认可。对于不满足精度要求的模型，将迭代进行参数调整，每个调整步长设置为 0.1 。校准目标是使 UBEM 模拟的每月能源消耗量与建筑物的实际每月用电量保持一致。表 9 说明了
Table 6  表 6
Scenario C: Add trees in plan.
方案 C：在计划中添加树木。
Model elements  模型元素 Cinnamomum  樟属 Magnolia  木兰 Buildings  建筑物 Climate  气候
camphora  樟脑 grandiflora  大花
N: Now  N：现在
C1:30years  C1：30 岁
C2:60years  C2：60 岁
Model elements Cinnamomum Magnolia Buildings Climate camphora grandiflora N: Now https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-07.jpg?height=74&width=87&top_left_y=436&top_left_x=756 https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-07.jpg?height=74&width=87&top_left_y=436&top_left_x=1006 https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-07.jpg?height=74&width=115&top_left_y=436&top_left_x=1229 https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-07.jpg?height=81&width=85&top_left_y=428&top_left_x=1489 C1:30years https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-07.jpg?height=87&width=96&top_left_y=524&top_left_x=754 https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-07.jpg?height=85&width=96&top_left_y=525&top_left_x=1001 https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-07.jpg?height=81&width=130&top_left_y=527&top_left_x=1219 https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-07.jpg?height=87&width=117&top_left_y=522&top_left_x=1475 C2:60years https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-07.jpg?height=110&width=117&top_left_y=629&top_left_x=743 https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-07.jpg?height=106&width=115&top_left_y=631&top_left_x=992 https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-07.jpg?height=92&width=184&top_left_y=640&top_left_x=1190 https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-07.jpg?height=107&width=162&top_left_y=626&top_left_x=1455| Model elements | Cinnamomum | Magnolia | Buildings | Climate | | :--- | :--- | :--- | :--- | :--- | | | camphora | grandiflora | | | | N: Now | ![](https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-07.jpg?height=74&width=87&top_left_y=436&top_left_x=756) | ![](https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-07.jpg?height=74&width=87&top_left_y=436&top_left_x=1006) | ![](https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-07.jpg?height=74&width=115&top_left_y=436&top_left_x=1229) | ![](https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-07.jpg?height=81&width=85&top_left_y=428&top_left_x=1489) | | C1:30years | ![](https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-07.jpg?height=87&width=96&top_left_y=524&top_left_x=754) | ![](https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-07.jpg?height=85&width=96&top_left_y=525&top_left_x=1001) | ![](https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-07.jpg?height=81&width=130&top_left_y=527&top_left_x=1219) | ![](https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-07.jpg?height=87&width=117&top_left_y=522&top_left_x=1475) | | C2:60years | ![](https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-07.jpg?height=110&width=117&top_left_y=629&top_left_x=743) | ![](https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-07.jpg?height=106&width=115&top_left_y=631&top_left_x=992) | ![](https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-07.jpg?height=92&width=184&top_left_y=640&top_left_x=1190) | ![](https://cdn.mathpix.com/cropped/2025_07_08_c3f36b97690e233cd5f9g-07.jpg?height=107&width=162&top_left_y=626&top_left_x=1455) |
Fig. 5. Top map of the microclimate model. (Taking scenario A as an example).
图 5.小气候模型的顶图。（以场景 A 为例）。
Table 7  表 7
Material settings in the model.
模型中的材质设置。
Type  类型 Name  名字 ID Information  信息
Soil and  土壤和 Loamy Soil  肥沃土壤 000000 Albedo:0.00; Z0 Roughness
反照率：0.00;Z0 粗糙度
Surface  表面 Length:0.015  长度：0.015
Soil and  土壤和 Deep Water  㵠 0000WW Albedo:0.00; Z0 Roughness
反照率：0.00;Z0 粗糙度
Surface  表面 Length:0.01  长度：0.01
Soil and  土壤和 Concrete  混凝土 0000PG Albedo:0.30; Z0 Roughness
反照率：0.30;Z0 粗糙度
Surface  表面 Pavement Gray  人行道灰色 Length:0.01  长度：0.01
Vegetation  植被 Grass 25 cm aver. dense
草 25 厘米。稠		 010000 Albedo:0.20; Plant height:0.25
反照率：0.20;株高：0.25
Buildings  建筑物 Default Wall moderate insulation
默认墙体中等隔断		 000000 Default thickness:0.01; Absorption:0.70; Reflection:0.30
默认厚度：0.01;吸收率：0.70;反射：0.30
Type Name ID Information Soil and Loamy Soil 000000 Albedo:0.00; Z0 Roughness Surface Length:0.015 Soil and Deep Water 0000WW Albedo:0.00; Z0 Roughness Surface Length:0.01 Soil and Concrete 0000PG Albedo:0.30; Z0 Roughness Surface Pavement Gray Length:0.01 Vegetation Grass 25 cm aver. dense 010000 Albedo:0.20; Plant height:0.25 Buildings Default Wall moderate insulation 000000 Default thickness:0.01; Absorption:0.70; Reflection:0.30| Type | Name | ID | Information | | :--- | :--- | :--- | :--- | | Soil and | Loamy Soil | 000000 | Albedo:0.00; Z0 Roughness | | Surface | | | Length:0.015 | | Soil and | Deep Water | 0000WW | Albedo:0.00; Z0 Roughness | | Surface | | | Length:0.01 | | Soil and | Concrete | 0000PG | Albedo:0.30; Z0 Roughness | | Surface | Pavement Gray | | Length:0.01 | | Vegetation | Grass 25 cm aver. dense | 010000 | Albedo:0.20; Plant height:0.25 | | Buildings | Default Wall moderate insulation | 000000 | Default thickness:0.01; Absorption:0.70; Reflection:0.30 |
range of parameter adjustments made during the calibration process. The results presented in Section 3.3 all derive from this calibration.
校准过程中进行的参数调整范围。第 3.3 节中介绍的结果均来自此校准。
For building energy consumption simulation results, the data are exported via the Grasshopper platform. Python is employed to extract and visualize information on temperature, wind speed, comfort, and building energy consumption for each scenario. This information is summarized and processed to identify the most suitable development path.
对于建筑能耗模拟结果，数据通过 Grasshopper 平台导出。Python 用于提取和可视化每个场景的温度、风速、舒适度和建筑能耗等信息。对这些信息进行汇总和处理，以确定最合适的开发路径。

3. Results  3. 结果

3.1. Model validation  3.1. 模型验证

Fig. 7 shows the simulated results of the surface temperatures represented by the eight selected points compared with the measured data. These points cover the main functional areas of the campus ( a a aa : roof; b b bb : water body; c: public square; d: grassland; e: soil; f: road; g: sports field; h: car park) and are evenly distributed across the campus, providing a representative reflection of the overall temperature distribution.
图 7 显示了 8 个选定点所代表的表面温度与测量数据的模拟结果。这些点涵盖了园区的主要功能区域（ a a aa ： 屋顶; b b bb ： 水体;c： 公共广场;d： 草原;e： 土壤;f： 道路;g： 运动场;h：停车场），并且均匀分布在园区内，从而代表性地反映了整体温度分布。
Validation shows a deviation of ≤ 1 ∘ C ≤ 1 ∘ C <= 1^(@)C\leq 1{ }^{\circ} \mathrm{C}, indicating that the measured and simulated data can be well fitted, except for Point b. The lake surface represented by Point b b bb is affected by the flow of the water body as well as the depth of the water body, which leads to a larger discrepancy between the on-site measurements and the simulated values. Overall, the difference between measured and simulated data is acceptable.
验证显示偏差 ， ≤ 1 ∘ C ≤ 1 ∘ C <= 1^(@)C\leq 1{ }^{\circ} \mathrm{C} 表示测量数据和模拟数据可以很好地拟合，但点 b 除外。Point b b bb 表示的湖面受水体流量和水体深度的影响，导致现场测量值与模拟值之间存在较大的差异。总体而言，实测数据和模拟数据之间的差异是可以接受的。
Fig. 8 presents the simulation results of energy consumption, demonstrating that the annual energy use of most buildings exhibits a seasonal trend, which is closely related to the academic calendar. Due to the unavailability of actual energy data for February and August in the new campus, the remaining 10 months of recorded energy data were used for the calibration process. The results indicate that during the summer months, the actual energy consumption is relatively close to the simulated energy consumption, which is consistent with the error margin. Despite a very small error remains after calibration, the only variable for the calibrated model is the applied scenarios. Therefore, by comparing the simulation results under different scenarios, the impact of these scenarios on campus energy consumption can be analyzed.
图 8 显示了能源消耗的模拟结果，表明大多数建筑物的年能耗呈现季节性趋势，这与校历密切相关。由于新校区无法获得 2 月和 8 月的实际能源数据，因此剩余 10 个月的记录能源数据用于校准过程。结果表明，在夏季，实际能耗与模拟能耗相对接近，这与误差边际一致。尽管校准后仍然存在非常小的误差，但校准模型的唯一变量是应用的场景。因此，通过对比不同情景下的仿真结果，可以分析这些情景对校园能耗的影响。

3.2. Microclimate simulation results under various scenarios
3.2. 各种情景下的微气候模拟结果

3.2.1. Air temperature  3.2.1. 空气温度

Fig. 9 illustrates the simulation results of average temperature changes from 8:00 a.m. to 21:00 pm. on a typical summer day in each scenario. Notably, in the baseline scenario (BL), the air temperature at the site increases significantly over the years. A comparison of the baseline scenario (BL) with the other three scenarios (A, B, and C) reveals that tree growth can lead to a substantial decrease in air temperature, with greater effectiveness observed over a longer time frame.
图 9 显示了每种情景中典型夏日上午 8：00 至晚上 21：00 平均温度变化的模拟结果。值得注意的是，在基线情景 （BL） 中，现场的空气温度多年来显着升高。将基线情景 （BL） 与其他三种情景（A、B 和 C）进行比较表明，树木生长会导致气温大幅下降，在较长的时间范围内观察到的效果更大。
In scenario B, where construction ceases, the maximum temperature at the site decreases by approximately 0.95 ∘ C 0.95 ∘ C 0.95^(@)C0.95^{\circ} \mathrm{C} after 30 years compared to the baseline scenario (BL), and by about 2.88 ∘ C 2.88 ∘ C 2.88^(@)C2.88^{\circ} \mathrm{C} after 60 years. When comparing the planned scenario (A) with the baseline (BL), the
在情景 B 中，施工停止，与基线情景 （BL） 相比，工地的最高温度在 0.95 ∘ C 0.95 ∘ C 0.95^(@)C0.95^{\circ} \mathrm{C} 30 年后降低约，在 60 年 2.88 ∘ C 2.88 ∘ C 2.88^(@)C2.88^{\circ} \mathrm{C} 后降低约。将计划场景 （A） 与基线 （BL） 进行比较时，
Fig. 6. Modeling schematic of two tree species in this research.
图 6.本研究中两种树种的建模示意图。
Table 8  表 8
Input meteorological file information for the model.
输入模型的气象文件信息。
Location  位置 Nanjing, China. 32.06 ∘ 32.06 ∘ 32.06^(@)32.06^{\circ} N, 118.78 ∘ 118.78 ∘ 118.78^(@)118.78^{\circ} E
中国南京。 32.06 ∘ 32.06 ∘ 32.06^(@)32.06^{\circ} N、 118.78 ∘ 118.78 ∘ 118.78^(@)118.78^{\circ} E
Start Simulation at Day
从白天开始模拟		 31.07.2024\31.07.2054\31.07.2074
Start Simulation at Time
在 Time 开始仿真		 07:00:00
Total Simulation Time in Hours
总仿真时间（小时）		 15
End Simulation at Time
在 Time 结束模拟		 22:00:00
Initial Temperature Atmosphere
初始温度气氛		 26 ∘ C 26 ∘ C 26^(@)C26^{\circ} \mathrm{C}
Wind Direction  风向 135
Wind Speed  风速 2.9 m / s 2.9 m / s 2.9m//s2.9 \mathrm{~m} / \mathrm{s}
Relatively Humidity in 2 m
2 m 内的相对湿度		 75 %
Specific Humidity in 2500 m
2500 m 的比湿度		 9 g / kg 9 g / kg 9g//kg9 \mathrm{~g} / \mathrm{kg}
Location Nanjing, China. 32.06^(@) N, 118.78^(@) E Start Simulation at Day 31.07.2024\31.07.2054\31.07.2074 Start Simulation at Time 07:00:00 Total Simulation Time in Hours 15 End Simulation at Time 22:00:00 Initial Temperature Atmosphere 26^(@)C Wind Direction 135 Wind Speed 2.9m//s Relatively Humidity in 2 m 75 % Specific Humidity in 2500 m 9g//kg| Location | Nanjing, China. $32.06^{\circ}$ N, $118.78^{\circ}$ E | | :--- | :--- | | Start Simulation at Day | 31.07.2024\31.07.2054\31.07.2074 | | Start Simulation at Time | 07:00:00 | | Total Simulation Time in Hours | 15 | | End Simulation at Time | 22:00:00 | | Initial Temperature Atmosphere | $26^{\circ} \mathrm{C}$ | | Wind Direction | 135 | | Wind Speed | $2.9 \mathrm{~m} / \mathrm{s}$ | | Relatively Humidity in 2 m | 75 % | | Specific Humidity in 2500 m | $9 \mathrm{~g} / \mathrm{kg}$ |
Table 9  表 9
Load settings for different types of buildings.
不同类型建筑物的负载设置。
Input Parameters  输入参数 Office  办公室 Education  教育 Laboratory  实验室 Residence  住宅 Public  公共
Equipment Load Per Area ( W / m 2 W / m 2 W//m^(2)\mathrm{W} / \mathrm{m}^{2} )
单位面积设备负载 （ W / m 2 W / m 2 W//m^(2)\mathrm{W} / \mathrm{m}^{2} ）		 7.5 15 22.5 3.8 7.5
Lighting Density Per Area ( W / m 2 W / m 2 W//m^(2)\mathrm{W} / \mathrm{m}^{2} )
每个区域的照明密度 （ W / m 2 W / m 2 W//m^(2)\mathrm{W} / \mathrm{m}^{2} ）		 9 9 10 5 7
Air Infiltration Rate (ACH)
空气渗透率 （ACH）		 0.5 0.5 0.5 0.5 0.5
Area Per Number of People ( m 2 / ppl m 2 / ppl m^(2)//ppl\mathrm{m}^{2} / \mathrm{ppl} )
实际人数面积 （ m 2 / ppl m 2 / ppl m^(2)//ppl\mathrm{m}^{2} / \mathrm{ppl} ）		 10 6 10 25 6
Ventilation Per  通风量 30/ 30/3600 30/3600 30/3600 30/
Person ( m 3 / s m 3 / s m^(3)//s\mathrm{m}^{3} / \mathrm{s} )
人员 （ m 3 / s m 3 / s m^(3)//s\mathrm{m}^{3} / \mathrm{s} ）		 3600 3600
Input Parameters Office Education Laboratory Residence Public Equipment Load Per Area ( W//m^(2) ) 7.5 15 22.5 3.8 7.5 Lighting Density Per Area ( W//m^(2) ) 9 9 10 5 7 Air Infiltration Rate (ACH) 0.5 0.5 0.5 0.5 0.5 Area Per Number of People ( m^(2)//ppl ) 10 6 10 25 6 Ventilation Per 30/ 30/3600 30/3600 30/3600 30/ Person ( m^(3)//s ) 3600 3600| Input Parameters | Office | Education | Laboratory | Residence | Public | | :--- | :--- | :--- | :--- | :--- | :--- | | Equipment Load Per Area ( $\mathrm{W} / \mathrm{m}^{2}$ ) | 7.5 | 15 | 22.5 | 3.8 | 7.5 | | Lighting Density Per Area ( $\mathrm{W} / \mathrm{m}^{2}$ ) | 9 | 9 | 10 | 5 | 7 | | Air Infiltration Rate (ACH) | 0.5 | 0.5 | 0.5 | 0.5 | 0.5 | | Area Per Number of People ( $\mathrm{m}^{2} / \mathrm{ppl}$ ) | 10 | 6 | 10 | 25 | 6 | | Ventilation Per | 30/ | 30/3600 | 30/3600 | 30/3600 | 30/ | | Person ( $\mathrm{m}^{3} / \mathrm{s}$ ) | 3600 | | | | 3600 |
Table 10  表 10
Parameter settings of the simulation model.
仿真模型的参数设置。
Input Parameters  输入参数 Values  值
Fixed Value  固定值 Range  范围
Wall Construction( W / ( m 2 ∗ K ) W / m 2 ∗ K W//(m^(2**)(K))\mathrm{W} /\left(\mathrm{m}^{2 *} \mathrm{~K}\right) )
墙体施工（ W / ( m 2 ∗ K ) W / m 2 ∗ K W//(m^(2**)(K))\mathrm{W} /\left(\mathrm{m}^{2 *} \mathrm{~K}\right) ）		 0.208 -
Roof Construction( W / ( m 2 ∗ K ) W / m 2 ∗ K W//(m^(2**)(K))\mathrm{W} /\left(\mathrm{m}^{2 *} \mathrm{~K}\right) )
屋面施工（ W / ( m 2 ∗ K ) W / m 2 ∗ K W//(m^(2**)(K))\mathrm{W} /\left(\mathrm{m}^{2 *} \mathrm{~K}\right) ）		 1.239 -
Floor Construction( W / ( m 2 ∗ K ) W / m 2 ∗ K W//(m^(2**)(K))\mathrm{W} /\left(\mathrm{m}^{2 *} \mathrm{~K}\right) )
地板施工（ W / ( m 2 ∗ K ) W / m 2 ∗ K W//(m^(2**)(K))\mathrm{W} /\left(\mathrm{m}^{2 *} \mathrm{~K}\right) ）		 2.433 -
Window U-Value(W/( m 2 ∗ K m 2 ∗ K m^(2**)K\mathrm{m}^{2 *} \mathrm{~K} ))
窗口 U 值 （W/（ m 2 ∗ K m 2 ∗ K m^(2**)K\mathrm{m}^{2 *} \mathrm{~K} ））		 2.5 -
Equipment Load Per Area ( W / m 2 W / m 2 W//m^(2)\mathrm{W} / \mathrm{m}^{2} )
单位面积设备负载 （ W / m 2 W / m 2 W//m^(2)\mathrm{W} / \mathrm{m}^{2} ）		 - 7.5-22.5
Lighting Density Per Area ( W / m 2 W / m 2 W//m^(2)\mathrm{W} / \mathrm{m}^{2} )
每个区域的照明密度 （ W / m 2 W / m 2 W//m^(2)\mathrm{W} / \mathrm{m}^{2} ）		 - 4.5-13.5
Area Per Number of People ( m 2 / ppl m 2 / ppl m^(2)//ppl\mathrm{m}^{2} / \mathrm{ppl} )
实际人数面积 （ m 2 / ppl m 2 / ppl m^(2)//ppl\mathrm{m}^{2} / \mathrm{ppl} ）		 - 4.0-12.0
Air Infiltration Rate (ACH)
空气渗透率 （ACH）		 0.5 -
Input Parameters Values Fixed Value Range Wall Construction( W//(m^(2**)(K)) ) 0.208 - Roof Construction( W//(m^(2**)(K)) ) 1.239 - Floor Construction( W//(m^(2**)(K)) ) 2.433 - Window U-Value(W/( m^(2**)K )) 2.5 - Equipment Load Per Area ( W//m^(2) ) - 7.5-22.5 Lighting Density Per Area ( W//m^(2) ) - 4.5-13.5 Area Per Number of People ( m^(2)//ppl ) - 4.0-12.0 Air Infiltration Rate (ACH) 0.5 -| Input Parameters | Values | | | :--- | :--- | :--- | | | Fixed Value | Range | | Wall Construction( $\mathrm{W} /\left(\mathrm{m}^{2 *} \mathrm{~K}\right)$ ) | 0.208 | - | | Roof Construction( $\mathrm{W} /\left(\mathrm{m}^{2 *} \mathrm{~K}\right)$ ) | 1.239 | - | | Floor Construction( $\mathrm{W} /\left(\mathrm{m}^{2 *} \mathrm{~K}\right)$ ) | 2.433 | - | | Window U-Value(W/( $\mathrm{m}^{2 *} \mathrm{~K}$ )) | 2.5 | - | | Equipment Load Per Area ( $\mathrm{W} / \mathrm{m}^{2}$ ) | - | 7.5-22.5 | | Lighting Density Per Area ( $\mathrm{W} / \mathrm{m}^{2}$ ) | - | 4.5-13.5 | | Area Per Number of People ( $\mathrm{m}^{2} / \mathrm{ppl}$ ) | - | 4.0-12.0 | | Air Infiltration Rate (ACH) | 0.5 | - |
maximum temperature decreases by approximately 2.36 ∘ C 2.36 ∘ C 2.36^(@)C2.36{ }^{\circ} \mathrm{C} after 60 years. Analysis of the simulation results indicates that, aside from the baseline scenario, scenario A experiences the highest daytime temperatures among the three scenarios. Conversely, scenario C demonstrates that increased tree planting based on planning can effectively mitigate temperature increases associated with development, aligning closely with the temperature conditions of the halted construction scenario (B) after 60 years.
最高温度大约 2.36 ∘ C 2.36 ∘ C 2.36^(@)C2.36{ }^{\circ} \mathrm{C} 在 60 年后下降。对模拟结果的分析表明，除了基线情景外，情景 A 在三个情景中白天温度最高。相反，情景 C 表明，基于规划的增加植树可以有效缓解与开发相关的温度升高，与 60 年后停止施工情景 （B） 的温度条件密切相关。
Fig. 10 shows the temperature simulation results for all grids from 12:00 a.m. to 16 : 00 pm 16 : 00 pm 16:00pm16: 00 \mathrm{pm}. for each scenario. The median status quo temperatures are around 32 − 33 ∘ C 32 − 33 ∘ C 32-33^(@)C32-33^{\circ} \mathrm{C} during these 5 h , while the median temperatures in the baseline scenario after 60 years reach 37 − 39 ∘ C 37 − 39 ∘ C 37-39^(@)C37-39^{\circ} \mathrm{C}, indicating a potential 5 ∘ C 5 ∘ C 5^(@)C5{ }^{\circ} \mathrm{C} increase in site temperatures over 60 years due to future climate changes. Additionally, the box plots for individual scenarios after 30 years are shorter than those for the 60-year period but longer than those in the status quo and baseline scenarios, suggesting that tree growth may increase the differences in temperature distribution within the site.
图 10 显示了从 12：00 a.m. 到 16 : 00 pm 16 : 00 pm 16:00pm16: 00 \mathrm{pm} 的所有网格的温度模拟结果。对于每个场景。在这 5 小时内，现状温度的中位数约为 32 − 33 ∘ C 32 − 33 ∘ C 32-33^(@)C32-33^{\circ} \mathrm{C} ，而 60 年后基线情景中的中位温度达到 37 − 39 ∘ C 37 − 39 ∘ C 37-39^(@)C37-39^{\circ} \mathrm{C} ，表明由于未来的气候变化，场地温度可能会 5 ∘ C 5 ∘ C 5^(@)C5{ }^{\circ} \mathrm{C} 在 60 年内升高。此外，30 年后单个情景的箱形图比 60 年期间的箱形图短，但比现状和基线情景中的箱形图长，这表明树木生长可能会增加场地内温度分布的差异。

3.2.2. Wind conditions  3.2.2. 风况

By comparing the simulation results of near-ground ( 1.5 m ) wind speed distribution for each scenario at the same time, (Fig. 11) it is evident that the proportion of the calm wind zone on the site significantly increases with the gradual rise in the number of buildings in the plan. Scenario BL and Scenario B demonstrate that climate change and tree growth do not have a significant impact on the near-ground wind environment as the number of years increases-the overall flow pattern of the site’s wind environment remains consistent. In 30 years, the overall near-ground wind environment across all scenarios is similar, characterized by dense isotaches on the northwestern side of the largescale slab buildings (e.g., residence area) and local high-rise towers (e.g., office buildings), where wind speed varies considerably. In 60 years, the near-ground wind environments of the two scenarios (A and C) exhibit greater variability, with dense isotaches on the northern and northwestern sides of each cluster. This variation may be attributed to a reduction in buffer zones for bottleneck winds, leading to a more complex wind environment.
通过同时比较每种情景下近地面 （ 1.5 m ） 风速分布的模拟结果（图 11），可以明显看出，随着规划中建筑物数量的逐渐增加，现场平静风区的比例显着增加。情景 BL 和情景 B 表明，随着年数的增加，气候变化和树木生长不会对近地面风环境产生重大影响——场地风环境的整体流动模式保持一致。30 年来，所有情景下的整体近地风环境都是相似的，其特征是大型板状建筑（如住宅区）和局部高层塔楼（如办公楼）的西北侧密集等速线，风速变化很大。在 60 年中，两种情景（A 和 C）的近地面风环境表现出更大的可变性，每个集群的北侧和西北侧都有密集的等速线。这种变化可能归因于瓶颈风缓冲区的减少，导致风环境更加复杂。
Five grids are selected for static wind speed analysis within the simulation range of 100 × 100 100 × 100 100 xx100100 \times 100, specifically the central grassland
在模拟范围内选取 5 个网格进行静态风速分析 100 × 100 100 × 100 100 xx100100 \times 100 ，具体为中央草原
Fig. 7. Mean radiant temperature of measurement and stimulation.
图 7.测量和刺激的平均辐射温度。
Fig. 8. Comparison of the simulated monthly energy consumption with historical data of the five buildings.
图 8.模拟的每月能源消耗量与五栋建筑的历史数据进行比较。
Fig. 9. Average temperature ( ∘ C ∘ C ^(@)C{ }^{\circ} \mathrm{C} ) in all scenarios.
图 9.所有情况下的平均温度 （ ∘ C ∘ C ^(@)C{ }^{\circ} \mathrm{C} ）。
( 45 , 45 ) ( 45 , 45 ) (45,45)(45,45), tower block ( 66 , 66 ) ( 66 , 66 ) (66,66)(66,66), public building ( 33 , 33 ) ( 33 , 33 ) (33,33)(33,33), main road ( 66 , 33 ) ( 66 , 33 ) (66,33)(66,33), and enclosed architectural complex ( 33 , 66 ) ( 33 , 66 ) (33,66)(33,66). (Fig. 12) In all scenarios, the central grassland and main road are situated in grids with higher wind speeds, whereas the tower block is found in grids with lower wind speeds, not exceeding approximately 1.1 m / s 1.1 m / s 1.1m//s1.1 \mathrm{~m} / \mathrm{s}. Both the baseline scenario (BL) and Scenario B indicate that future climate change and tree growth will have minimal effects on the near-ground wind speed of the site. A comparison between Scenario A, C and Scenario BL, B reveals that the construction of the public building and the enclosed architectural complex significantly impacts wind speeds, resulting in a
( 45 , 45 ) ( 45 , 45 ) (45,45)(45,45) 、塔楼 ( 66 , 66 ) ( 66 , 66 ) (66,66)(66,66) 、公共建筑 ( 33 , 33 ) ( 33 , 33 ) (33,33)(33,33) 、主干道 ( 66 , 33 ) ( 66 , 33 ) (66,33)(66,33) 和封闭式建筑群 ( 33 , 66 ) ( 33 , 66 ) (33,66)(33,66) 。（图 12）在所有情况下，中央草原和主干道都位于风速较高的网格中，而塔块位于风速较低的网格中，不超过大约 1.1 m / s 1.1 m / s 1.1m//s1.1 \mathrm{~m} / \mathrm{s} 。基线情景 （BL） 和情景 B 都表明，未来的气候变化和树木生长对场地的近地风速影响最小。情景 A、C 和情景 BL、B 之间的比较表明，公共建筑和封闭建筑群的施工对风速有很大影响，导致
maximum reduction of approximately 1.08 m / s 1.08 m / s 1.08m//s1.08 \mathrm{~m} / \mathrm{s}.
最大减少约 1.08 m / s 1.08 m / s 1.08m//s1.08 \mathrm{~m} / \mathrm{s} 。

3.2.3. Physiological Equivalent Temperature (PET)
3.2.3. 生理等效温度 （PET）

The violin plot in Fig. 13 illustrates the PET simulation results for all grids across the four scenarios. The results indicate that the PET values for each grid are predominantly centered around 53 ∘ C 53 ∘ C 53^(@)C53^{\circ} \mathrm{C}. A comparison of PET values throughout the day reveals that the median PET values from 12:00 a.m. to 16 : 00 pm 16 : 00 pm 16:00pm16: 00 \mathrm{pm}. exceeds 50 ∘ C 50 ∘ C 50^(@)C50^{\circ} \mathrm{C}, indicating extremely hot conditions. In 30 years, the distribution of PET values begins to show a trend of segmentation-the number of grids concentrated in the 50 − 55 ∘ C 50 − 55 ∘ C 50-55^(@)C50-55^{\circ} \mathrm{C}
图 13 中的小提琴图说明了四种场景中所有网格的 PET 仿真结果。结果表明，每个网格的 PET 值主要以 53 ∘ C 53 ∘ C 53^(@)C53^{\circ} \mathrm{C} 为中心。对全天 PET 值的比较表明，从上午 12：00 到 16 : 00 pm 16 : 00 pm 16:00pm16: 00 \mathrm{pm} 的 PET 值中位数。超过 50 ∘ C 50 ∘ C 50^(@)C50^{\circ} \mathrm{C} ，表示极热条件。30 年后，PET 值的分布开始呈现细分趋势——集中在 50 − 55 ∘ C 50 − 55 ∘ C 50-55^(@)C50-55^{\circ} \mathrm{C}
Fig. 10. Air temperature ( ∘ C ∘ C ^(@)C{ }^{\circ} \mathrm{C} ) from 12 − 16 h 12 − 16 h 12-16h12-16 \mathrm{~h} in all scenarios.
图 10.空气温度 （ ∘ C ∘ C ^(@)C{ }^{\circ} \mathrm{C} ） 在所有场景中。 12 − 16 h 12 − 16 h 12-16h12-16 \mathrm{~h}
Fig. 11. The distributions of wind velocities ( m / s m / s m//s\mathrm{m} / \mathrm{s} ) in all scenarios at 15:00.
图 11.15：00 所有场景中风速 （ m / s m / s m//s\mathrm{m} / \mathrm{s} ） 的分布。
Fig. 12. Static values of wind velocities ( m / s m / s m//s\mathrm{m} / \mathrm{s} ) at 15:00 for five selected areas.
图 12.5 个选定区域在 15：00 的风速 （ m / s m / s m//s\mathrm{m} / \mathrm{s} ） 的静态值。
Fig. 13. Simulation results of PET ( ∘ C ∘ C ^(@)C{ }^{\circ} \mathrm{C} ) in all scenarios.
图 13.PET （ ∘ C ∘ C ^(@)C{ }^{\circ} \mathrm{C} ） 在所有场景中的模拟结果。
decreases, while the number of grids in the 35 − 40 ∘ C 35 − 40 ∘ C 35-40^(@)C35-40{ }^{\circ} \mathrm{C} increases. This trend becomes more pronounced in 60 years, as more grids appear in the 30 − 35 ∘ C 30 − 35 ∘ C 30-35^(@)C30-35{ }^{\circ} \mathrm{C} range. This suggests that the growth of trees enhances the thermal comfort of the study area, with an increasing number of regions transitioning to a more comfortable state over time.
减少，而 中的 35 − 40 ∘ C 35 − 40 ∘ C 35-40^(@)C35-40{ }^{\circ} \mathrm{C} 网格数量增加。这种趋势在 60 年后变得更加明显，因为该 30 − 35 ∘ C 30 − 35 ∘ C 30-35^(@)C30-35{ }^{\circ} \mathrm{C} 范围内出现了更多的网格。这表明树木的生长增强了研究区域的热舒适性，随着时间的推移，越来越多的区域过渡到更舒适的状态。
When comparing the distribution of PET values across the three scenarios ( A , B A , B A,B\mathrm{A}, \mathrm{B}, and C ), it is evident that in 30 years ( A 1 , B 1 , C 1 A 1 , B 1 , C 1 A1,B1,C1\mathrm{A} 1, \mathrm{~B} 1, \mathrm{C} 1 ), there is minimal difference among the distributions. However, in 60 years (A2, B2, C2), the PET values for scenarios B and C are significantly lower than those for the other two scenarios. This observation suggests that increased construction has a partially negative impact on thermal comfort in the study area, although this effect can be mitigated by incorporating more trees into the original plan.
在比较三种情景（ A , B A , B A,B\mathrm{A}, \mathrm{B} 和 C）中 PET 值的分布时，很明显，在 30 年 （ A 1 , B 1 , C 1 A 1 , B 1 , C 1 A1,B1,C1\mathrm{A} 1, \mathrm{~B} 1, \mathrm{C} 1 ） 中，分布之间的差异很小。然而，在 60 年（A2、B2、C2）中，情景 B 和 C 的 PET 值明显低于其他两种情景。这一观察结果表明，增加施工对研究区域的热舒适度有部分负面影响，尽管这种影响可以通过在原始计划中纳入更多树木来减轻。

3.2.4. Wind environment and thermal comfort
3.2.4. 风环境和热舒适性

Wind environment optimization is a key consideration in urban
风环境优化是城市中的一个关键考虑因素
design (e.g., street canyon ventilation, building spacing) due to its direct impact on heat dissipation and thermal comfort. While humidity is integrated into PET calculations through ENVI-met’s energy balance algorithms, this study focuses on the explicit role of wind patterns in mitigating heat stress. To quantify this relationship, we analyzed PET distributions in areas with wind speeds below 1.5 m / s 1.5 m / s 1.5m//s1.5 \mathrm{~m} / \mathrm{s}-a threshold defined by China’s wind classification standard (GB/T 28,591-2012) as the lower limit for perceptible airflow.
设计（例如，街道峡谷通风、建筑间距），因为它对散热和热舒适度有直接影响。虽然湿度通过 ENVI-met 的能量平衡算法集成到 PET 计算中，但本研究侧重于风型在缓解热应力方面的明确作用。为了量化这种关系，我们分析了风速低于 1.5 m / s 1.5 m / s 1.5m//s1.5 \mathrm{~m} / \mathrm{s} 中国风分类标准 （GB/T 28,591-2012） 定义的阈值作为可感知气流下限的区域的 PET 分布。
The results reveal that in current conditions (Scenario N), 86.6 % of grids exhibited PET ≥ 41 ∘ C ≥ 41 ∘ C >= 41^(@)C\geq 41^{\circ} \mathrm{C}, classified as “high heat”(Binarti et al., 2020). Under climate change-only scenarios (BL1 and BL2), this proportion increased to 92.1 % (BL1) and 100 % (BL2), demonstrating that reduced wind speeds exacerbate PET in the absence of vegetation growth. The 60-year vegetation growth scenarios (A2, B2, C2) demonstrate a significant mitigation effect, confirming that the cooling effect of vegetation is more dominant than the enhancement of wind in mitigating heat
结果显示，在当前条件下（情景 N），86.6% 的网格表现出 PET ≥ 41 ∘ C ≥ 41 ∘ C >= 41^(@)C\geq 41^{\circ} \mathrm{C} ，被归类为“高温”（Binarti 等人，2020 年）。在仅气候变化情景（BL1 和 BL2）下，这一比例增加到 92.1 % （BL1） 和 100 % （BL2），这表明在没有植被生长的情况下，风速降低会加剧 PET。60 年植被生长情景（A2、B2、C2）显示出显著的缓解效果，证实了在缓解热量方面，植被的冷却效果比增强风更占主导地位
stress. In Scenario C2, only 39.9 % of low-wind grids exhibited PET ≥ 41 ≥ 41 >= 41\geq 41 ∘ C ∘ C ^(@)C{ }^{\circ} \mathrm{C}. This highlights the effectiveness of vegetation growth and building shading in offsetting the negative impacts caused by weakened airflow. The wind speed and PET at 15:00 for all scenarios are presented in Fig. 14
强调。在情景 C2 中，只有 39.9% 的低风电网出现 PET ≥ 41 ≥ 41 >= 41\geq 41 ∘ C ∘ C ^(@)C{ }^{\circ} \mathrm{C} 。这突出了植被生长和建筑物遮阳在抵消气流减弱造成的负面影响方面的有效性。图 14 显示了所有场景下 15：00 的风速和 PET

3.3. Building energy consumption results under various scenarios
3.3. 各种场景下的建筑能耗结果

Fig. 15 illustrates the cooling demand of each scenario, encompassing both the total cooling energy required in a day and the cooling energy required per unit area in the study area. In all scenarios, both the total cooling energy consumption and the cooling energy consumption per unit area increase with the passage of years, indicating that climate change will significantly elevate building energy consumption. A comparison of the simulation results for Scenario B with Scenario BL reveals a decrease of approximately 478.5 kWh in total cooling energy consumption in the study area in 30 years, with the cooling energy consumption per unit area declining by about 0.095 kWh / m 2 0.095 kWh / m 2 0.095kWh//m^(2)0.095 \mathrm{kWh} / \mathrm{m}^{2}. In 60 years, these values drop to 1095.9 kWh and 0.2 kWh / m 2 0.2 kWh / m 2 0.2kWh//m^(2)0.2 \mathrm{kWh} / \mathrm{m}^{2}, respectively, suggesting that the growth of trees can reduce the cooling demands of buildings in the area to some extent, with longer growth periods yielding greater benefits. Among the three scenarios-A, B, and C-after 60 years, Scenario A exhibits the highest total refrigeration energy consumption and energy consumption per unit area, recorded at 6249.3 kWh and 1.99 kWh / m 2 1.99 kWh / m 2 1.99kWh//m^(2)1.99 \mathrm{kWh} / \mathrm{m}^{2} respectively, followed by Scenario B. In contrast, Scenario C demonstrates the lowest total refrigeration energy consumption and energy consumption per unit area after 60 years, with total energy consumption reduced by approximately 4.9 % 4.9 % 4.9%4.9 \% compared to Scenario A, and cooling energy consumption per unit area decreased by 0.055 kWh / m 2 0.055 kWh / m 2 0.055kWh//m^(2)0.055 \mathrm{kWh} / \mathrm{m}^{2}. These findings indicate that integrating trees into the original plan can effectively diminish the cooling energy requirements of buildings.
图 15 说明了每种情景的制冷需求，包括一天所需的总制冷量和研究区内每单位面积所需的制冷量。在所有情景下，制冷能耗总量和单位面积制冷能耗均随着时间的推移而增加，表明气候变化将显著提高建筑能耗。情景 B 与情景 BL 的模拟结果比较显示，研究区域在 30 年内冷却能耗总量减少了约 478.5 kWh，单位面积制冷能耗下降了约 0.095 kWh / m 2 0.095 kWh / m 2 0.095kWh//m^(2)0.095 \mathrm{kWh} / \mathrm{m}^{2} 。在 60 年中，这些值分别下降到 1095.9 kWh 和 0.2 kWh / m 2 0.2 kWh / m 2 0.2kWh//m^(2)0.2 \mathrm{kWh} / \mathrm{m}^{2} ，这表明树木的生长可以在一定程度上减少该地区建筑物的制冷需求，更长的生长期会产生更大的好处。在 A、B 和 C 三个情景中，60 年后，情景 A 的制冷总能耗和单位面积能耗最高， 1.99 kWh / m 2 1.99 kWh / m 2 1.99kWh//m^(2)1.99 \mathrm{kWh} / \mathrm{m}^{2} 分别为 6249.3 kWh，其次是情景 B。相比之下，情景 C 在 60 年后表现出最低的总制冷能耗和单位面积能耗，总能耗比情景 A 降低约 4.9 % 4.9 % 4.9%4.9 \% ，单位面积制冷能耗降低约 0.055 kWh / m 2 0.055 kWh / m 2 0.055kWh//m^(2)0.055 \mathrm{kWh} / \mathrm{m}^{2} 。这些发现表明，将树木纳入原始规划可以有效减少建筑物的冷却能源需求。
Fig. 16 illustrates the relationship between cooling energy
图 16 说明了冷却能量之间的关系
consumption and PET across all scenarios. Scenarios with higher energy consumption (e.g., BL2) often correspond to elevated PET values, indicating a potential trade-off between energy use and outdoor thermal comfort. The three scenarios (A2, B2, C2) optimized in 60 years emerge as the most promising, as it achieves a balance between reduced energy consumption and improved PET values. For example, at 15:00, scenario A2 reduces cooling energy consumption by 5.13 % compared to the scenario BL2 (from 542.26 kWh to 514.43 kWh ) while simultaneously lowering PET values by 13.83 % 13.83 % 13.83%13.83 \% (from 54.50 ∘ C 54.50 ∘ C 54.50^(@)C54.50^{\circ} \mathrm{C} to 46.96 ∘ C 46.96 ∘ C 46.96^(@)C46.96^{\circ} \mathrm{C} ). Scenario C2 demonstrates further improvements, reducing cooling energy consumption by 6.85 % compared to BL2 and lowering PET values by 17.38 % % %\%. These results underscore that strategic design interventions can significantly enhance both energy efficiency and outdoor thermal comfort.
消耗和 PET。能耗较高的场景（例如 BL2）通常对应于 PET 值升高，这表明能源使用和户外热舒适度之间存在潜在的权衡。60 年来优化的三种方案（A2、B2、C2）是最有希望的，因为它在降低能耗和提高 PET 值之间实现了平衡。例如，在 15：00，与 BL2 情景相比，情景 A2 的制冷能耗降低了 5.13%（从 542.26 kWh 降低到 514.43 kWh），同时将 PET 值降低了 13.83 % 13.83 % 13.83%13.83 \% （从 54.50 ∘ C 54.50 ∘ C 54.50^(@)C54.50^{\circ} \mathrm{C} 到 46.96 ∘ C 46.96 ∘ C 46.96^(@)C46.96^{\circ} \mathrm{C} ）。情景 C2 展示了进一步的改进，与 BL2 相比，冷却能耗降低了 6.85%，PET 值降低了 17.38 % % %\% %。这些结果强调，战略设计干预可以显著提高能源效率和户外热舒适度。
Additionally, another notable observation emerges from the data. Despite the sharp decrease in PET in the evening ( 17 − 19 h 17 − 19 h 17-19h17-19 \mathrm{~h} ), the cooling energy consumption of the building fails to follow this downward trend proportionally. This discrepancy can be explained by thermal inertia within building components - the envelope systems (walls, roof, etc.) absorb and retain substantial heat energy during daytime exposure. When ambient temperatures decrease in the evening, these heatsaturated materials continue to transfer stored thermal energy indoors, resulting in continuous indoor cooling demand.
此外，数据中还出现了另一个值得注意的观察结果。尽管 PET 在晚上急剧下降 （ 17 − 19 h 17 − 19 h 17-19h17-19 \mathrm{~h} ），但建筑物的制冷能耗未能按比例跟随这种下降趋势。这种差异可以用建筑构件内的热惯性来解释——围护系统（墙壁、屋顶等）在白天暴露时吸收并保留大量热能。当环境温度在晚上降低时，这些热饱和材料继续将储存的热能转移到室内，从而产生持续的室内制冷需求。

4. Discussion and implication
4. 讨论和影响

Although numerous studies have demonstrated that trees significantly mitigate urban heatwave and enhance thermal comfort level, there remains a paucity of research examining the impacts of tree growth dynamics, future climate change, and urban construction on climate and building energy consumption. By utilizing the scenario analysis method, this study quantitatively analyzes temperature, wind conditions, comfort levels, and building energy consumption under four
尽管大量研究表明树木可以显著缓解城市热浪并提高热舒适度，但仍然缺乏研究树木生长动态、未来气候变化和城市建设对气候和建筑能耗的影响。利用情景分析方法，定量分析了温度、风况、舒适度和建筑能耗
Fig. 14. Biaxial scatter plot of wind speed and PET at 15:00 in all scenarios.
图 14.在所有场景中 15：00 的风速和 PET 的双轴散点图。
Fig. 15. The cooling demand of each scenario.
图 15.每个方案的冷却需求。
Fig. 16. The cooling demand and average PET of each scenario.
图 16.每种方案的冷却需求和平均 PET。
scenarios. The results indicate that future climate change will lead to substantial increases in site temperature, thermal comfort, and building energy consumption (Scenario BL). In the Scenario B2 (stop construction), tree growth over a period of 60 years could result in a 2.88 ∘ C 2.88 ∘ C 2.88^(@)C2.88{ }^{\circ} \mathrm{C} reduction in the average air temperature of the site. This reduction is attributed to the increase in both height and canopy of the trees over time, which enables them to block and filter an increasing amount of solar radiation, thereby significantly reducing air temperatures. As temperatures decline, the thermal comfort of the site improves, leading to decreased reliance on air conditioning and a reduction in energy requirements for cooling, with a 15.5 % lesser increase in total building cooling energy after 60 years, and a decrease of 0.2 kWh / m 2 0.2 kWh / m 2 0.2kWh//m^(2)0.2 \mathrm{kWh} / \mathrm{m}^{2} in cooling energy per unit area of the building (Scenario B2).
场景。结果表明，未来的气候变化将导致场地温度、热舒适度和建筑能耗的大幅增加（情景 BL）。在情景 B2（停止施工）中，树木生长 60 年可能会导致场地的平均气温 2.88 ∘ C 2.88 ∘ C 2.88^(@)C2.88{ }^{\circ} \mathrm{C} 降低。这种降低归因于树木的高度和树冠随着时间的推移而增加，这使它们能够阻挡和过滤越来越多的太阳辐射，从而显着降低空气温度。随着温度的下降，场地的热舒适度提高，从而减少对空调的依赖和冷却能源需求的减少，60 年后建筑物的总冷却能量增加 15.5%，建筑物单位面积的冷却能量减少 0.2 kWh / m 2 0.2 kWh / m 2 0.2kWh//m^(2)0.2 \mathrm{kWh} / \mathrm{m}^{2} （情景 B2）。
Regarding the wind environment, results from various scenarios illustrate that tree growth does not significantly impact the near-ground wind conditions at the site. Conversely, increased construction, particularly of high-rise buildings, leads to complex changes in the nearground wind environment. This is due to the tall trees selected for the study already branching at a height of 1.5 m , rendering growth variations negligible at this elevation. In contrast, buildings, as modeled entities, alter the wind field conditions near the ground. The comparison of the scenarios reveals that Scenario C shows improvements in air temperature, comfort level, and building energy consumption relative to
关于风环境，各种情景的结果表明，树木生长不会对现场的近地面风条件产生重大影响。相反，建筑的增加，尤其是高层建筑的建设，会导致近地风环境发生复杂的变化。这是因为为研究选择的高大树木已经在 1.5 m 的高度分枝，使得在这个海拔高度的生长变化可以忽略不计。相比之下，建筑物作为建模实体，会改变地面附近的风场条件。情景比较表明，情景 C 显示空气温度、舒适度和建筑能耗相对于
Scenario A, achieving results similar to Scenario B after 60 years. Therefore, retaining some existing trees when developing new districts, or planting trees in advance according to the original plan at the early stages of development, can effectively address the summer overheating and discomfort that occur at the onset of new district development and substantially reduce the cooling energy consumption of buildings.
情景 A，在 60 年后取得与情景 B 相似的结果。因此，在开发新小区时保留部分现树，或在发展初期按原计划提前植树，可有效解决新小区发展初期出现的夏季过热和不适，大幅降低建筑物的制冷能耗。
By comparing the results of this study with the measurement data, it can be concluded that the climate and energy consumption model, as well as the simulation results, are generally accurate. However, the model does have some deficiencies, specifically in the following aspects:
通过将本研究的结果与测量数据进行比较，可以得出结论，气候和能源消耗模型以及模拟结果通常是准确的。但是，该模型确实存在一些不足，特别是在以下几个方面：
(1) Changes in tree growth rates resulting from future global climate changes were not considered in this study. Future research could develop more comprehensive dynamic models that integrate the relationships among regional climate change, tree growth, and building construction. Such models could further expand the scope of scenarios by incorporating additional variables, including diverse vegetation configurations (e.g., combinations of trees, shrubs, and grasslands) and comparative analyses across different climatic regions.
（1） 本研究未考虑未来全球气候变化导致的树木生长速率变化。未来的研究可以开发更全面的动态模型，以整合区域气候变化、树木生长和建筑施工之间的关系。此类模型可以通过纳入其他变量来进一步扩大情景的范围，包括不同的植被配置（例如，树木、灌木和草原的组合）和不同气候区域的比较分析。
(2) The simulations and analyses were based on idealized tree growth data, the effects of human interventions such as pruning were not involved in this study.
（2） 模拟和分析基于理想化的树木生长数据，本研究不涉及修剪等人工干预的影响。
(3) The study primarily focused on height and crown spread in modeling tree growth, given the extensive area of the study site. Consequently, smaller-scale variables, such as leaf shape and leaf area index, were excluded from consideration. The impacts of these parameters in urban environment and building energy consumption warrant further investigation.
（3） 鉴于研究地点的面积很大，该研究主要集中在树木生长建模中的高度和树冠蔓延。因此，较小尺度的变量，如叶形和叶面积指数，被排除在考虑范围之外。这些参数对城市环境和建筑能耗的影响值得进一步研究。
(4) There are notable differences in the growth data among various tree species-faster-growing species could achieve similar microclimate and energy-saving impacts within shorter timeframes. Therefore, future studies should incorporate a broader range of species with varying growth rates and climatic adaptability. This would provide a more comprehensive understanding of the relationship between tree growth, climate, and building energy consumption.
（4） 不同树种之间的生长数据存在显著差异——生长较快的树种可以在更短的时间内实现类似的小气候和节能效果。因此，未来的研究应纳入更广泛的具有不同生长速率和气候适应性的物种。这将提供更全面的了解树木生长、气候和建筑能源消耗之间的关系。
(5) The study selected a research area of approximately 2 km 2 2 km 2 2km^(2)2 \mathrm{~km}^{2}, the findings can offer valuable references for urban new district planning in regions with comparable climate conditions. Expanding the scope to the city level may reveal more relationships between tree growth, climate, and energy consumption. Future studies could be extended to more diverse urban areas (e. g., high-density commercial areas, low-density residential areas, etc.) to validate the generalizability of the findings of this study.
（5）本研究选取了一个研究领域， 2 km 2 2 km 2 2km^(2)2 \mathrm{~km}^{2} 研究结果可为气候条件相近地区的城市新区规划提供有价值的参考。将范围扩大到城市级别可能会揭示树木生长、气候和能源消耗之间的更多关系。未来的研究可以扩展到更多样化的城市地区（例如，高密度商业区、低密度住宅区等），以验证本研究结果的普遍性。
In the face of global climate change, it is essential to develop adaptive strategies that mitigate urban heatwaves, reduce energy consumption, and enhance the resilience of cities to future climate extremes. This study provides a methodology for improving environmental conditions and building energy consumption in various new urban districts. The findings can assist in predicting environmental parameters, such as air temperature and comfort levels, as well as the cooling demand for buildings in new districts at the early stages of development. This can serve as a basis for selecting an appropriate construction timeline and offers a strategic approach for planning new districts concerning construction and governance. Future work could employ other approaches such as Pareto optimization, to simultaneously address multiple objectives and enable policymakers to identify development patterns that achieve synergistic benefits across environmental sustainability and human-centric metrics.
面对全球气候变化，必须制定适应性策略来缓解城市热浪、减少能源消耗并增强城市对未来极端气候的抵御能力。本研究为改善各种新城区的环境条件和建筑能耗提供了一种方法。这些发现可以帮助预测环境参数，例如空气温度和舒适度，以及新区在开发早期阶段对建筑物的制冷需求。这可以作为选择合适的施工时间表的基础，并为规划有关建设和治理的新区提供战略方法。未来的工作可以采用其他方法，例如帕累托优化，以同时解决多个目标，并使政策制定者能够确定在环境可持续性和以人为本的指标之间实现协同效益的发展模式。

5. Conclusion  5. 总结

Trees can significantly mitigate urban heatwave and enhance residents’ comfort level. While previous studies have primarily focused on the relationship between tree arrangement, species diversity, less attention has been given to how tree growth dynamically affects the microclimate and subsequently influences indoor energy consumption. Supported by simulation and experiments in microclimate and building energy consumption, this study investigates the long-term effects of vegetation growth, building construction and climate change, using Southeast University’s new campus as a case study. To achieve this, four scenarios were established: only climate change, construct as planned, stop construction, and add trees in the original plan. These scenarios simulated and quantitatively evaluated air temperature, wind speed, PET and building energy consumption at present, 30 years and 60 years in the future.
树木可以显着缓解城市热浪并提高居民的舒适度。虽然以前的研究主要集中在树木排列、物种多样性之间的关系上，但很少关注树木生长如何动态影响小气候并随后影响室内能源消耗。在微气候和建筑能耗模拟和实验的支持下，本研究以东南大学新校区为案例研究，调查了植被生长、建筑施工和气候变化的长期影响。为了实现这一目标，建立了四种情景：仅气候变化、按计划施工、停止施工和在原始计划中增加树木。这些情景模拟并定量评估了当前、30 年和未来 60 年的气温、风速、PET 和建筑能耗。
The results indicate that after 60 years, tree growth reduces the average site temperature by nearly 3 ∘ C 3 ∘ C 3^(@)C3{ }^{\circ} \mathrm{C}, and this reduction will increase over time. Additionally, tree growth enhances thermal comfort and expands comfort zones in the site. The resultant climate changes from tree growth can decrease total cooling demand of buildings by 15.5 % 15.5 % 15.5%15.5 \%, with a maximum reduction of about 0.2 kWh / m 2 0.2 kWh / m 2 0.2kWh//m^(2)0.2 \mathrm{kWh} / \mathrm{m}^{2} per unit area. This study provides quantitative evidence of the overheating and thermal discomfort associated with the development of new urban districts and addresses the gap in research regarding the dynamic relationship among climate, trees, and urban construction. It emphasizes the long-term impacts of climate change, tree growth, and urban development in
结果表明，60 年后，树木生长使平均场地温度降低了近 3 ∘ C 3 ∘ C 3^(@)C3{ }^{\circ} \mathrm{C} ，并且这种降低会随着时间的推移而增加。此外，树木的生长增强了热舒适度并扩大了场地的舒适区。树木生长引起的气候变化可以将建筑物的总制冷需求降低 15.5 % 15.5 % 15.5%15.5 \% ，最大每单位面积减少约 0.2 kWh / m 2 0.2 kWh / m 2 0.2kWh//m^(2)0.2 \mathrm{kWh} / \mathrm{m}^{2} 。本研究提供了与新城区开发相关的过热和热不适的定量证据，并解决了关于气候、树木和城市建设之间动态关系的研究空白。它强调了气候变化、树木生长和城市发展的长期影响
urban environment, offering a critical foundation for future urban greening strategies. These findings provide actionable insights for urban regeneration initiatives and climate-resilient planning, assisting planners and city managers in making informed decisions.
城市环境，为未来的城市绿化战略奠定了重要基础。这些发现为城市更新计划和气候适应型规划提供了可作的见解，帮助规划者和城市管理者做出明智的决策。

CRediT authorship contribution statement
CRediT 作者贡献声明

Hanxun Fu: Writing - review & editing, Writing - original draft, Visualization, Software, Resources, Methodology, Investigation, Data curation, Conceptualization. Yuanhao Jiao: Writing - original draft, Visualization, Software, Methodology, Data curation. Lirui Deng: Writing - review & editing, Software. Wei Wang: Supervision, Project administration, Funding acquisition.
汉勋傅：写作 - 审查与编辑，写作 - 原稿，可视化，软件，资源，方法论，调查，数据管理，概念化。Yuanhao Jiao： 写作 - 原稿、可视化、软件、方法论、数据管理。邓丽瑞： 写作 - 审查和编辑， 软件.Wei Wang：监督、项目管理、资金获取。

Declaration of competing interest
利益争夺声明

The authors claim no conflicts of interests.
作者声称没有利益冲突。

Acknowledgements  确认

The work described in this paper was sponsored by Natural Science and Foundation of China (#52394224). Any opinions, findings, conclusions, or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of those organizations.
本文中描述的工作由中国自然科学基金 （#52394224） 赞助。本文中表达的任何意见、发现、结论或建议均为作者的观点，并不一定反映这些组织的观点。

Data availability  数据可用性

The authors do not have permission to share data.
作者没有共享数据的权限。

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    E-mail address: 220220045@seu.edu.cn (H. Fu).
    电子邮件地址：220220045@seu.edu.cn （H. Fu）。