Research on Time-Dynamic Operation Characterization of Energy Efficiency and Decarbonation of Green Building based on SD

Research on Time-Dynamic Operation Characterization of Energy Efficiency and Decarbonation of Green Building based on SD | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Research on Time-Dynamic Operation Characterization of Energy Efficiency and Decarbonation of Green Building based on SD Xuejun Lv, Keping Sun, Hui Zhang, Wei Chen, Yiquan Zou, Lei Yang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8748115/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 14 You are reading this latest preprint version Abstract This study employs a System Dynamics (SD) model to investigate energy efficiency and carbon mitigation strategies during the operational phase of university public buildings, with particular attention to long-term climate change and the evolving nature of policy, technology, and user behavior. Through multi-scenario simulation across extended time horizons, the model captures the dynamic feedback mechanisms among decarbonized energy structures, building envelope improvements, renewable energy adoption, and behavioral responses. Results show that reducing the carbon emission factor of the power grid can decrease operational carbon emissions by 19.4%, while improving energy intensity through technological optimization yields a 10% reduction in energy consumption. When these strategies are implemented in combination, a 36.4% reduction in emissions and a 19.3% decline in energy use can be achieved, highlighting the cumulative effect of integrated interventions over time. The study reveals how interactive and time-sensitive variables respond to long-term climate stressors, offering a system-level understanding of operational carbon dynamics in public buildings. Furthermore, the model provides insights into how adaptive strategies evolve under delayed policy impacts and resource limitations. This research not only contributes methodological depth to scenario-based modeling for climate-responsive building management but also delivers data-driven support and strategic guidance for integrating energy-saving practices into both the design and operational stages of green buildings, reinforcing the alignment between building performance optimization and sustainable development goals. Physical sciences/Energy science and technology Physical sciences/Engineering Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Environmental social sciences Public buildings Building energy consumption Building carbon emissions System Dynamics Simulation (SDS) Climate change adaptation Carbon emission reduction Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 1.Introduction Rapid industrialization has intensified global energy and environmental challenges, with the Copenhagen Climate Summit (2009) and Paris Agreement (2016) establishing a framework for international cooperation 1 . The IPCC’s 2023 Sixth Assessment Report highlights climate change’s impacts and underscores the urgency of a global low-carbon energy transition 2 . Greenhouse gases, especially CO 2 , drive global environmental change, with the construction sector responsible for over 70% of total CO 2 emissions 3 . To curb GHG emissions, China’s 14th Five-Year Plan promotes green industry restructuring, prioritizing the construction sector—one of the nation’s top three energy consumers 4 . Notably, the annual energy consumption of large public buildings in China is 10 to 22 times higher than that of residential buildings 5 . Recent studies show operational carbon emissions of public buildings total 830 Mt—38% of all operational building emissions—with energy use surpassing that of residential buildings 6 . Therefore, the carbon reduction potential of public buildings is greater than that of other building types 7 . Despite their smaller footprint, higher-education buildings require urgent energy and carbon reduction due to their high intensity of use 8 . Academic buildings in particular, as core facilities for daily operations, are densely populated and have high energy demand 9 . Prolonged lighting, HVAC, and equipment use drive high energy consumption 10 . Therefore, optimizing academic buildings’ energy and carbon strategies supports campus sustainability and offers a model for other public buildings. However, the complexity and dynamic behavior of building energy systems limit traditional models in capturing interactions among influencing factors 11 . This study adopts a system dynamics (SD) approach, integrating building energy and carbon theories, to model a Wuhan university building. The model quantifies climate change impacts, integrates photovoltaic energy, and simulates new building envelope technologies dynamically. Scenario simulations during the operational phase reveal interrelationships between energy and carbon factors in green buildings under climate change. Correspondingly, energy-saving strategies are proposed. In conclusion, optimizing university buildings is essential to achieving China’s “dual-carbon” goals and aligns with green-building sustainability principles.The study aims to provide a theoretical basis and decision-support for energy conservation and emission reduction in university public buildings. Findings offer guidance for future building design and management, and evidence for optimizing energy use in a changing climate. Additionally, achieving a 2030 carbon peak requires stronger policy guidance and adoption of innovative low-carbon technologies, underscoring integrated solutions. The study bridges static evaluation and dynamic simulation for carbon-mitigation strategies in university public buildings. 2 Literature Analysis Existing literature examines public building energy consumption and carbon emissions from multiple perspectives—primarily influencing factors, energy-saving potential, predictive analysis, and decarbonization strategies—providing a theoretical and practical basis for university building energy management. Accordingly, this section reviews related studies from the perspectives of influencing factors and predictive approaches. 2.1 Research on the influencing factors of energy consumption and carbon emission In terms of influencing factors, relevant studies have identified that the energy consumption and carbon emissions of university buildings are affected by both macro and micro-level factors, such as climate change, building characteristics, socio-economic conditions, population dynamics and policy measures. For instance, Wan et al. found that climate change significantly increases public building energy use, particularly in regions with high heating and cooling demands 12 . YANG and TANG demonstrated that optimizing insulation materials can substantially reduce heating and cooling energy demand in public buildings 13 . At a Thai university, Rewthong et al. observed a 10% annual rise in electricity consumption, largely from expanded use of air conditioning, lighting, and elevators 14 . Yildiz et al. reported that a mix of passive and active measures can cut university building energy use by over 60%, as confirmed by measured data and simulations 15 . AD Borghi et al. compared carbon-neutral design strategies at universities in Italy and the United States, highlighting differences in achieving neutrality through design and retrofits 16 . M Moradibistouni et al. examined how design goals, climate, and material performance interact to achieve near-zero energy standards 17 . Such multi-factor approaches offer valuable insights for guiding buildings toward energy-saving goals. Most prior research relies on static simulations to assess how design optimization, operational management, and material or technology choices influence building energy use and carbon emissions, offering useful theoretical and practical guidance. However, these static approaches often overlook the dynamic changes that occur during the actual operation of university public buildings. As a result, static simulations frequently miss the temporal–spatial, policy, and environmental interactions that shape real-world performance. Consequently, traditional static approaches have become inadequate for decision-relevant analysis. Accordingly, advancing dynamic-system simulations that capture the spatiotemporal interactions of building energy use is an urgent research priority, enabling more accurate and actionable strategies for energy conservation and carbon management. 2.2 SD simulation of building energy efficiency and carbon emission SD is a methodology for understanding, modeling and analyzing the behavior of complex systems over time, originally developed by Jay W. Forrester at MIT in the 1950s 18 , which is well-established for analyzing complex systems and is increasingly applied to energy and environmental problems. The scenario analysis method 19 , is commonly coupled with SD to formulate plausible futures and dynamically project their evolution. Yang et al. confirmed that dynamic lifecycle measurements of building carbon emissions are more accurate than traditional static methods 20 . Li G et al. used SD to predict carbon emissions at different stages of a building’s lifecycle, identifying the key driving factors behind these emissions 21 . Monna S et al. analyzed energy consumption simulations for selected residential buildings, establishing a benchmark for evaluating energy-saving retrofits and implemented three different retrofit scenarios 22 . Mohamed Marzouka et al. took a comprehensive approach by considering sustainable building indicators, including energy consumption, carbon emissions, construction costs and living conditions. Through the use of an SD model, they dynamically assessed the short- and long-term impacts of different design scenarios 23 . Lin CX et al. incorporated dynamic variables such as building energy consumption, residential electricity demand、energy structure and policy implementation into an SD model to simulate and predict the timing of carbon peaking under different scenarios. The model also demonstrated the emission reduction effects of various decarbonization pathways 24 . From a macro perspective, Huo TF et al. explored the dynamic impact of population growth, increased building demand, and different energy efficiency policies on carbon emissions as urbanization progresses 25 26 . Shi Q 27 and Huo TF 28 considered the extent to which different rates of technological advancement affect carbon emissions on different time scales, and the results suggest that technological advancement and energy transition will remain the key driving factors. While extensive research examines building energy and carbon performance, few studies address the combined effects of economic activity, energy transition, and climate variation on public buildings. Focusing on university facilities, this study develops a systematic SD-based framework for dynamic energy–carbon analysis and conservation guidance. Grounded in sustainable development theory, the framework integrates macro-level influences with single-building dynamics to quantify climate impacts, model feedbacks, and assess multiple carbon-reduction strategies by effectiveness. Scenario-based simulations project long-term trends and inform targeted policies. The results provide actionable recommendations for policymakers, practitioners, and academics, supporting China’s long-term energy and carbon goals and contributing scientific evidence to global climate-mitigation efforts and international cooperation. 3 Research methodology SD can systematically display the relationships between various factors within complex systems, which are often difficult to capture using traditional methods. It reveals hidden feedback mechanisms and represents the dynamic interactions within the system through feedback loops, stock and flow structures, offering a deeper understanding of the system's internal dynamics. This paper, based on System Dynamics theory and the indicator system of influencing factors for public building energy consumption and carbon emissions, constructs an SD model for public building energy consumption and carbon emissions. The causal loop diagram is established in this model, which specifies the relationship between variables such as socio-economics、urban energy、climate environment、relevant policy、building energy consumption and carbon emission, and the causal loop diagram is transformed into a stock flow diagram model using VENSIM software. Taking a university building from Wuhan as an example, the energy consumption and carbon emission of public buildings during the period of 2023-2035 are simulated based on the historical data from 2009-2022. By analyzing the factors driving the increase in energy consumption and carbon emissions, the study proposes corresponding countermeasures to address future energy demands and environmental pressures. These recommendations aim to provide targeted strategies for improving energy efficiency and reducing carbon emissions in public buildings, contributing to sustainable development goals. Figure 1 illustrates the research technology route of this study. 3 . 1 Research objects and system boundaries According to the Wuhan Statistical Yearbook, Wuhan City has a total of 1,254 schools of various types. Newly constructed buildings for regular primary and secondary schools accounted for 35% of the city's total building completions in 2022, indicating a significant proportion of educational buildings in urban construction. Therefore, this paper selects a teaching and research complex in Wuhan as its research subject. The building has a floor area of approximately 12,100 m², with a reinforced concrete main structure and an exterior wall self-insulating envelope system. The study's system boundary encompasses four interacting subsystems: socioeconomic, energy, building, and climate. It focuses on analyzing energy consumption and carbon emission characteristics during the building's operational phase, using these as core output metrics. 3 . 2 System architecture modeling To analyze the systemic characteristics of public building energy use and carbon emissions, and clarify variable relationships, a causal loop diagram (CLD) is developed in Figure 2 showing interactions among subsystems. Building envelope measures can provide nearly half of potential energy savings, as changes in thermal transmittance affect heat exchange, indoor temperature, and energy use. Economic growth expands building areas and energy demand, raising emissions, while technologies such as PV generation and insulated walls mitigate these effects. Climate factors, notably summer cooling demand and the urban heat island (UHI) effect, also elevate consumption,UHI intensification forms a positive feedback with energy use. Solar radiation both influences temperature and supports PV generation, reducing reliance on conventional energy. However, temperature swings alter cooling or heating needs, sometimes increasing overall demand. This interplay underscores the need for strategies addressing both technological and environmental drivers. Building and climate CLDs are shown in Figures 3–4. Figure 5 provides the energy CLD, where an increase in Gross Domestic Product (GDP) leads to a rise in total energy consumption, due to the increased demand for more energy as a result of increased economic activity. Optimization of the energy structure can mitigate this trend and reduce overall carbon emissions by increasing the share of renewable energy sources. Energy consumption intensity and energy utilization efficiency are two key variables in this CLD. Improving energy utilization efficiency can significantly reduce energy consumption per unit of floor area, thereby reducing the building's carbon footprint. On the other hand, a reduction in energy intensity can also contribute to a reduction in overall energy consumption, which can be achieved by adopting energy-efficient technologies and optimizing the way energy is used. The economic CLD associated with it is shown in Figure 6. As CLDs only show causal paths, VENSIM is used to extend them into a stock–flow diagram (Figure 7) capturing cumulative effects. Variables are classified as stocks (system states), flows (rates of change), and auxiliaries (constants). The SD model comprises socio-economic, energy, building, and climate subsystems with interlinked feedbacks. Examples include education influencing conservation awareness, energy structure affecting consumption intensity, and climate factors impacting emissions—modifiable via policy. Equations integrate these links to capture dynamic changes, identify emission sources, and simulate reduction processes. This model is built upon the foundations of system dynamics, green building design standards, and real engineering cases. Compared with traditional static models, the model is able to simulate situational changes of the system under different conditions, revealing long-term trends and potential non-linear effects. Additionally, the model's structure is flexible and scalable, allowing for adjustments and optimizations to suit different types of public buildings. As a result, the model holds broad application potential not only in the research field but also in practical decision-making. It can assist policymakers in evaluating the potential impacts of various policies and technological solutions, providing a solid foundation for energy efficiency management and emission reduction strategies in public buildings. 3 . 3 Validity testing To further investigate the dynamic characteristics of energy consumption and carbon emissions in public buildings, this study conducted a quantitative analysis of the system's stock-and-flow diagram after completing the qualitative CLD analysis. The key lies in accurately determining the system's main equations and parameters to ensure that the model accurately reflects the actual process of building energy consumption. The parameter settings in this study are mainly obtained through the following ways: (1) Direct data, such as GDP, birth and death rates, are sourced from the Wuhan Statistical Yearbook , China Energy Statistical Yearbook , China Meteorological Data Service Center, and existing literature. (2) Indirect data are obtained through regression analysis using historical data and the table function in VENSIM software. Validity testing refers to conducting operational and historical tests to assess whether the information obtained from the model and its predictions can accurately reflect and simulate building energy consumption and carbon emissions. In this study, the historical period is set from 2009 to 2022, with the model starting in 2009 and the prediction period covering 2023 to 2035. (1) Operational Testing: Due to the numerous and interrelated factors influencing building energy consumption and carbon emissions, this study selected three different simulation time steps—0.25 years, 0.5 years, and 1 year—for operational testing to verify the overall stability of the model. The simulation results are shown in Figure 8. The simulation results show that different simulation steps have less impact on building energy consumption and carbon emissions and remain stable, which indicates that the model has passed the operational test, and thus the model is stable and feasible. (2) Historical Testing: Historical testing involves comparing simulation results with historical data. When the relative error falls within a reasonable range, it indicates that the SD model is accurate and effective. This study selected total population, regional GDP, and tertiary industry value as comparison indicators. The historical validation results are shown in Table 1, with all relative errors within an acceptable range. This demonstrates that the model results closely align with actual data, capable of reasonably and accurately reflecting objective reality. Table 1. Historical simulation test results Year Total population (millions) GDP (billions) Tertiary sector value(billions) Historic-al Simula-tion Error Historic-al Simula-tion Error Historic-al Simula-tion Error 2009 835.55 835.55 0.00 4620.86 4620.86 0.00 2581.52 2581.52 0.00 2010 836.73 838.70 -0.24 5565.93 5568.96 -0.05 2942.91 2970.6 -0.94 2011 827.24 840.03 -1.55 6762.2 6517.05 3.63 3418.65 3359.69 1.72 2012 821.71 841.77 -2.44 8003.82 7520.82 6.03 3987.99 3792.61 4.90 2013 822.05 846.13 -2.93 9051.27 8567.93 5.34 4523.32 4303.07 4.87 2014 827.31 851.46 -2.92 10069.48 9662.53 4.04 5501.04 4906.4 10.81 2015 829.27 857.64 -3.42 10905.6 10794.9 1.02 5881.58 5611.35 4.59 2016 833.85 863.60 -3.57 11531.42 11981.8 -3.91 6609.93 6421.77 2.85 2017 853.65 868.81 -1.78 13090.81 13215.9 -0.96 7677.86 7335.59 4.46 2018 883.73 872.25 1.30 14928.72 14483.3 2.98 8987.31 8347.4 7.12 2019 906.40 879.42 2.98 16223.21 15814.1 2.52 9855.34 9450.87 4.10 2020 916.19 885.65 3.33 15516.07 17226 -11.02 9786.71 10635.2 -8.67 2021 934.10 888.53 4.88 17688.03 18720.6 -5.84 11109.47 11892.5 -7.05 2022 944.42 891.26 5.63 18866.43 19673 -4.28 11673.99 13223.1 -13.27 3 . 4 Scenario design and simulation parameters Scenario simulations analyze the impact of different emission reduction measures on energy consumption and carbon emissions during the building operation phase by establishing two categories of scenarios: single-factor and multi-factor. The single-factor scenarios encompass climate, building performance, and policy regulation, comprising 10 sub-scenarios. Multi-factor scenarios comprise four integrated scenarios to evaluate synergistic emission reduction effects. The baseline scenario represents unchanged existing factors, with specific parameters detailed in Table 2. Table 3 illustrates the classification of the 10 single-factor sub-scenarios and their parameter adjustments. Table 2. Baseline scenario parameters Relevant factor Parameter value Heat transfer coefficient of roof 0.44 W/（m 2 ·K） Heat transfer coefficient of external wall 0.7 W/（m 2 ·K） Number of air changes 1 Solar photovoltaic area 100m 2 Photovoltaic system efficiency 0.4 Temperature difference of hot water 40℃ Construction area 12100m 2 Table 3. Single-factor classification Scenarios classification Description of adjustments Foundation Baseline scenario BS All factors remain unchanged Environmental scenarios S1 Heat island effect increased by 10% 29 S2 Temperature increase of 1°C 30 S3 5% increase in green space in urban areas 31 Architectural scenarios S4 Additional technical support for self-insulation of external walls S5 Reduction of roof heat transfer coefficient to 0.15W/（m 2 ·K） 32 S6 Reduction of external walls heat coefficient to 0.55W/（m 2 ·K） S7 Number of air changes increased to 1.2 S8 Increase in solar photovoltaic panel area by 15m 2 33 Policy scenarios S9 Grid carbon emission factor reduced to 0.4 S10 Reduce energy consumption intensity by 10% 34 Since the changes in energy consumption and carbon emissions of public buildings are the result of the joint action under the complex influence of many factors, the change of a single variable may result in a biased situation. Multi-factor scenario analysis builds upon single-factor simulations by combining two or more factors in order to achieve a dynamic system simulation. This approach lays the foundation for exploring energy-saving and carbon-reduction pathways in public buildings when responding to climate change under the influence of multiple factors. Four scenarios are proposed in this paper: Building Optimization Scenario (BOS), Policy Regulation Scenario (PRS), Technological Adaptation Scenario (TAS), and Integrated Application Scenario (IAS). Table 4 provides the specific parameter settings and descriptions. Table 4. Multi-factor classification Scenarios classification Description of adjustments Baseline scenario BS All factors remain unchanged Building Optimization Scenario BOS Reduction of roof heat transfer coefficient to 0.15W/(m 2 ·K),reduction of external walls heat coefficient to 0.55W/（m 2 ·K） Policy Regulation Scenario PRS Grid carbon emission factor reduced to 0.4, reduce energy consumption intensity by 10% Technological Adaptation Scenario TAS Additional technical support for self-insulation of external walls，increase in solar photovoltaic panel area by 15m 2 ，heat island effect increased by 10%, temperature increase of 1°C，5% increase in green space in urban areas Integrated Application Scenario IAS BOS+PRS+ Additional technical support for self-insulation of external walls, increase in solar photovoltaic panel area by 15m 2 ，5% increase in green space in urban areas 4. Discussion 4 . 1 Analysis of historical trends and baseline scenarios This paper establishes a baseline scenario (BS) based on real-world conditions and forecasts annually through 2035. In this scenario, it is assumed that all variables remain unchanged, with everything progressing according to historical trends and causal developments. Without setting energy-saving and carbon reduction targets or implementing effective energy-saving measures and policies, the model in this scenario reflects the future outcomes under a no-intervention scenario. The simulation results, as shown in Figure 9, indicate that the development of energy consumption and carbon emissions in university buildings in Wuhan under the BS scenario can be divided into three distinct phases. Phase 1 (2009–2016): Building carbon emissions and energy consumption rise slowly, mainly due to Wuhan’s modest economic growth, gradual urbanization, and limited expansion of university building areas, resulting in smaller increases in demand. Phase 2 (2017–2023): Growth accelerates as rapid economic development and urbanization drive higher energy demand. Although COVID-19 temporarily slows activity in 2020, post-pandemic recovery and resumed construction lead to a sharp rebound. Phase 3 (2024–2035): Emissions and energy use continue to grow rapidly, driven by Wuhan’s sustained economic expansion. Policies such as the 13th Five-Year Plan and the Peak Carbon Action Plan are introduced, but their effects take time to materialize due to delays in technology development and adoption. Without additional measures, total building energy consumption and carbon emissions are projected to reach 282.7 kWh and 146.3 tons by 2035, challenging the goals of carbon peaking by 2030 and neutrality by 2060. Identifying key influencing factors is essential for targeted policies, technological innovation, and sustainable development. 4 . 2 Results and analysis of the single-factor experiment Compared to simple forecasts, a deeper assessment of the potential impacts of future building energy consumption and carbon emissions is more practically significant. Using the methodology of SD, it is possible to conduct a single-factor analysis of 10 key factors (as detailed in Table 4) and adjust these factors under different scenarios to reveal their specific impacts on future building energy consumption and carbon emissions. These include environmental factors, envelope performance, behavioral influences, and policy interventions, as shown in Figure 10. In the environmental scenarios, the heat island effect significantly impacts building energy consumption and carbon emissions, as shown in Figures 10b and 10d. environmental scenarios demonstrate significant impacts. The intensified urban heat island effect (S1) leads to a 2.4% increase in carbon emissions by 2035, driven by elevated night-time temperatures and residual heat accumulation, especially in cities like Wuhan characterized by hot summers and cold winters. This increases the cooling load, making buildings more energy-intensive during extended warm periods. In contrast, the 1°C temperature rise scenario (S2) presents a slight 1.1% reduction in emissions compared to the baseline (BS). This result is attributed to a combination of decreased winter heating demand, shorter academic terms during hot months, and administrative energy-saving policies, such as restrictions on air-conditioning use. Scenario S3, which increases urban green space by 5%, reduces emissions and energy use by approximately 2.3% by mitigating ambient temperature through improved microclimate conditions. Regarding building envelope performance, scenario S4, which applies external wall self-insulation technology, yields the most significant results, reducing both energy consumption and carbon emissions by 6%. In contrast, reducing the external wall heat transfer coefficient alone (S6) achieves only a 2.4% decrease due to the persistence of thermal bridges around windows and junctions. Roof insulation enhancement (S5) delivers a 1.4% emissions reduction. Meanwhile, expanding solar PV panel coverage by 15m² (S8) has limited impact on energy consumption but contributes positively to emission reductions by displacing fossil-based electricity with renewable energy, aligning with China’s 14 th Five-Year Plan on renewable energy expansion. Scenario S7, which increases the number of air changes per hour to improve indoor air quality, results in a 2.5% rise in energy use. This is especially problematic in regions with harsh seasonal climates, as thermal regulation of fresh air imposes a higher load on HVAC systems. The trade-off between health and energy efficiency emphasizes the importance of precision in ventilation system design. Among all scenarios, policy-driven interventions (S9 and S10) are the most effective. Scenario S9, which reduces the power grid emission factor to 0.4, achieves a 19.4% reduction in carbon emissions. Scenario S10, which lowers energy intensity by 10%, achieves a 10% reduction in energy use. Notably, Figure 10 reveals that S10’s impact is more immediate, while S9’s cumulative benefits become more prominent over time. This suggests that short-term behavior-based strategies and long-term structural energy reforms must be coordinated 35 . Importantly, policy measures can not only bring about significant emission reductions in the short term, but also achieve long-term sustainable development through systemic change and technology facilitation. Thus, relying on targeted government policies is considered the best option for reducing building energy consumption and carbon emissions. In summary, energy consumption intensity, grid emission coefficient, whether to apply external wall self-insulation technology, and heat transfer coefficient of external walls and roofs have the most significant impact on building energy consumption and carbon emissions. The effects of air temperature and solar photovoltaic are also more obvious, and the effects of green space rate in urban areas, heat island effect and the number of air changes on building energy consumption and carbon emissions are relatively limited. Figure 11 illustrates the combined impact of various single-factor scenarios on the direction and magnitude of changes in building operational energy consumption and carbon emissions. Overall, emission-reduction factors exert a significantly stronger influence on the system than emission-increase factors. Among these, policy regulation and operational efficiency improvement scenarios (e.g., S9, S10) dominate in reducing energy consumption and carbon emissions, representing the largest single-factor contributors to emission reductions. Building technology measures like self-insulating exterior walls (e.g., S4) yield stable but relatively limited energy savings and emission reductions. In contrast, climate disturbances and operational parameter changes (e.g., S1, S7) primarily result in modest increases in energy consumption and carbon emissions, exerting relatively limited overall driving force on the system. It should be noted that single-factor scenario analysis struggles to capture the synergistic effects of multiple factors in real-world operations. Therefore, subsequent multi-factor scenario simulations will be conducted to comprehensively evaluate the energy-saving and carbon-reduction outcomes under different combinations of measures. 4 . 3 Results and analysis of the multi-factor experiment Although certain policy measures and technological improvements have achieved remarkable results in reducing emissions and saving energy, it is difficult to achieve a comprehensive reduction in carbon emissions and maximizing energy consumption due to the one-sided nature of single-factor scenario simulation. Therefore, there is an urgent need for comprehensive scenario simulation analysis. Based on single-factor scenario simulation, the overall impacts on building energy consumption, annual energy consumption, carbon emissions and annual carbon emissions under different combinations of strategies are comprehensively assessed through the synergistic effect of multi-factor scenario simulation. These scenarios are categorized into four perspectives: building envelope performance optimization, macroeconomic policy regulation, new technology application to address climate change, and comprehensive carbon reduction measures. An integrated evaluation approach is used to assess these scenarios. As shown in Figure 12, the overall trend shows that the carbon emissions and energy consumption of buildings under different scenarios generally increase year by year, but the increase in each scenario is lower than that of the BS and there is a significant difference between them. In the BS, carbon emissions and energy consumption increase at the fastest rate, while in the IAS, these growth trends are significantly suppressed. This implies that energy efficiency programs in different combinations have different levels of impact on building energy consumption and carbon emissions. Therefore, targeted analysis and specific policies should be carried out in conjunction with specific objectives and realities. In BOS, the total building carbon emissions are reduced by about 3.8% and the total energy consumption is reduced by about 3.6% in 2035. The effect of energy saving and emission reduction is relatively inferior to that of TAS, but the curves of BOS are shown to be increasing and parallel to the BS curves from the perspective of annual energy consumption and carbon emission of buildings, as shown in Figure 10a and b. This trend indicates that the retrofitting and optimization of the performance of building envelopes have achieved a direct effect in energy saving and emission reduction without any additional impact. Wuhan has been actively promoting green building standards in recent years, and by optimizing the building maintenance structure, the building energy consumption and carbon emissions have been reduced, which is in line with the Green Building Action Plan advocated by China. This result is also consistent with the findings of Zhu J and others in the area of improving building envelope performance to reduce energy consumption and carbon emissions 36 . In the PRS context, building carbon emissions and energy consumption were successfully reduced by 28.2% and 10.3% as a result of the government’s intervention, indicating that the implementation of the policy measures was effective in reducing the carbon intensity and overall energy consumption in electricity use. However, in the case of PRS, Figures 10b and d depict trends in total building energy consumption and carbon emissions, where the difference between these values and the BS is initially small and then gradually increases over time. This phenomenon can be attributed to the lag in the popularization of policy promotion, as the gradual popularization of government-introduced policies to guide the development and application of new technologies, and the improvement and promotion of ultra-low-energy codes substantially reduced energy consumption and carbon emissions. Additionally, with government support, promoting clean energy and improving energy efficiency have yielded notable emission reduction results. However, it is important to note that the decrease in energy consumption is significantly lower than the reduction in carbon emissions. This discrepancy may be due to the government’s focus on macro-level building standards while neglecting efforts to raise energy-saving awareness among users. The lack of corresponding laws, regulations, and incentive or penalty measures means that even though buildings meet regulatory standards, energy misuse by occupants diminishes the overall energy-saving effectiveness. The TAS simulation demonstrates that advancements In technology Integrated with the building itself enable a certain level of response to climate change. The results show a reduction in carbon emissions by approximately 7.8% and energy consumption by about 7% compared to the BS, effectively curbing the annual increase in carbon emissions. However, the total energy consumption and carbon emissions reduced by buildings in this scenario go through a process from less to more. Specifically, at the initial stage of the application of a new technology, there may be a period of technological break-in and adaptation, which requires time for optimization and adjustment. So the initial energy saving and emission reduction effect may not be as significant as expected. However, with the growth of time, the application of new technologies gradually becomes popular and mature, the coverage expands, the optimization effect of the technology becomes apparent, energy consumption is significantly reduced, and the reduction of carbon emissions becomes more obvious. On the economic front, with the gradual reduction in the cost of energy-saving technologies and the increase in market demand, more enterprises are willing to invest in and adopt new technologies, further promoting the popularization of technology application and the manifestation of its effects thus gradually widening the gap with BS. Hence, the application of new technologies not only increases the resilience of the building itself and its ability to adapt to climate change, but also creates a win-win situation in terms of green low-carbon and economic practicality. It is also worth noting that the use of combined measures in the IAS results in a curve with the same trend as the TAS, but with a lower overall magnitude and a relatively longer time required than the TAS. This is due to the fact that it not only simply superimposes multiple mitigation measures, but also lies in the complex interactions and complementary effects between its factors, while the synergistic effects of more factors require more time. The report of Magrini et al 37 , also confirms that building carbon emission is a complex dynamic system, which is influenced by factors such as building envelope performance, climatic conditions and occupant behavior, and therefore its significant reduction effect is only visible in the middle and late stages, with a reduction of carbon emission by about 36.4% and energy consumption by about 19.3%. The IAS scenario outperforms other scenarios in terms of emission reduction primarily due to its systematic integration and multi-level feedback mechanisms. More importantly, it achieves comprehensive intervention from the macro to the micro level. Policy regulation not only drives the adoption of new technologies but also provides economic and policy support for building optimization measures. In turn, technological innovation strengthens the effectiveness of policy implementation, enhancing the enforcement of policies through the tangible results of energy conservation and emission reduction. The reasonable combination of various energy-saving measures creates cumulative emission reduction effects over time, increasingly highlighting their advantages. This trend reflects the power of systematic integration, where different measures enhance each other’s effectiveness, generating a synergistic effect that results in a “1+1>2” outcome 38 . Figure 13 illustrates the impacts and relative contributions of different integrated single factors on building operational energy consumption and carbon emissions across multiple dimensions. It further reveals the synergistic effects achieved through combining various measures. Compared to the single-scenario approach, the Building Optimization Scenario (BOS) and Technology Adaptation Scenario (TAS) demonstrate enhanced energy-saving and emission-reduction capabilities, though their overall contributions remain lower than those of the Policy Regulation Scenario (PRS). The Integrated Application Scenario (IAS) demonstrates the most pronounced effects on both metrics, contributing significantly more to carbon emission reductions than other scenarios while also dominating in energy consumption reduction. Overall, BOS, TAS, PRS, and IAS represent distinct emission reduction pathways: structural optimization, technological advancement, policy regulation, and integrated synergy, respectively. Policy regulation and multi-factor integrated application exert the most significant suppression on energy consumption and carbon emissions during the building operation phase. This indicates that in practical emission reduction efforts, it is essential to coordinate the synergistic implementation of multiple measures based on specific objectives and conditions. 5. Conclusions Based on the constructed system dynamics model, this study analyzed the spatiotemporal patterns of energy consumption and carbon emissions during the building operation phase under different scenarios. The main conclusions are as follows: (1) Long-term trend: Under the baseline scenario (BS), building energy consumption and carbon emissions continue to rise without any intervention measures, posing challenges to China's goals of peaking carbon emissions by 2030 and achieving carbon neutrality by 2060. Effective policy and technical measures are crucial to reversing this trend. (2) SD Model Validity: This model accurately simulates historical trends and reflects the dynamic interactions among policy, technology, climate, and socioeconomic factors. It quantitatively analyzes the combined effects of nonlinear factors under different scenario simulations. (3) Single-factor results: Reducing the carbon emission factor of the power grid achieved the greatest emission reduction (19.4%), while lowering energy consumption intensity yielded the highest energy savings (10.0%). In the short term, operational measures demonstrate more pronounced effects; over the long term, the cumulative benefits of structural decarbonization measures become more prominent, indicating that strategies must be tailored to the time horizon. (4) Integrated Action Strategy (IAS): By combining policy, technology, and building envelope optimization, IAS achieved a 36.4% reduction in carbon emissions and a 19.3% decrease in energy consumption, outperforming single-method approaches. Simulation data indicates that universities should implement comprehensive energy-saving and emission-reduction strategies centered on reducing energy intensity and optimizing grid carbon emission factors based on actual conditions. These efforts should be complemented by architectural design optimization and the promotion of energy-saving technologies to ensure steady carbon emission reductions and achieve energy conservation targets. SD modeling can dynamically predict changes in energy consumption and carbon emissions for university buildings while analyzing the underlying causes of these outcomes. Based on this, the research model provides quantitative evidence for energy consumption assessment and operational optimization under green building standards, further supporting the implementation and realization of green transformation in university buildings. Furthermore, this study concludes that achieving the 2030 carbon peak target for university public buildings hinges on proactively controlling the growth trend of operational-phase carbon emissions, rather than implementing concentrated energy-saving and emission-reduction measures only as the target year approaches. 5.1Policy Recommendations Based on the above analysis and research findings, this paper proposes the following three policy recommendations: (1) To achieve the 2030 carbon peak target, university public buildings should transition from “retroactive energy conservation” to “process control” as soon as possible. Research indicates that continuing existing operational models will result in inertial growth of carbon emissions during the building operation phase, making it difficult to naturally peak before 2030. Therefore, it is recommended to use carbon emission intensity during the building operation phase as a management lever, setting clear phased control targets for major university public buildings and continuously tracking progress during operational management. By decelerating the growth rate of carbon emissions in advance, the emissions curve can gradually flatten before 2030, thereby establishing an achievable carbon peak pathway. This approach avoids the need for concentrated remedial measures as the target year approaches. (2) Research indicates that relying solely on optimizing building envelope measures or any single energy-saving technology is unlikely to significantly alter carbon emission trends in the short term. In contrast, the synergistic effects of operational management, energy structure adjustment, and building performance optimization prove more critical. Therefore, in implementing building energy retrofits, universities should avoid fragmented implementation of energy-saving measures. Instead, they should holistically consider adjustments to operational management, integration of renewable energy sources, and enhancement of existing building performance, advancing these efforts within the same timeframe to achieve stable and sustained emission reduction outcomes. (3) Dynamic calculations indicate that rising temperatures and the urban heat island effect may, under certain conditions, increase building cooling demands, partially offsetting energy-saving gains. Therefore, as universities advance energy conservation and carbon reduction efforts, they should incorporate climate change factors into building operation decisions. This includes rationally controlling air conditioning strategies and mitigating summer heat load growth through greening and shading measures 39 , 40 , thereby preventing a rebound in energy consumption and carbon emissions due to climatic factors. Declarations Competing Interests Statement The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding This work was supported by the the Hubei Provincial Science and Technology Program (Grant No.2025BCB033). Author Contribution 1.Xuejun Lv：Responsible for the main writing of this article, the conducting of experiments, and the processing and analysis of data.2.Keping Sun: Provided data and project support for this research.3.Hui Zhang: The corresponding author of this study provided support for the logical organization, writing guidance, and review of this research.4.Wei Chen: It also provided writing guidance and assistance with data analysis for this research.5.Yiquan Zou: Provided data and project support for this research.6.Lei Yang: Provided data and project support for this research.7.Zhengwei Wang: Assisted the first author in completing the data processing work. Data Availability The data in this article were all obtained through government open channels and can provide the original data. This link leads to the page where you can search for the keyword *Wuhan Statistical Yearbook* on the Wuhan Municipal Bureau of Statistics website. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8748115","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":589176270,"identity":"62e6e48a-775f-47b0-95bf-0c7b251bfc43","order_by":0,"name":"Xuejun Lv","email":"","orcid":"","institution":"Hubei University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Xuejun","middleName":"","lastName":"Lv","suffix":""},{"id":589176272,"identity":"35d275ac-4d7e-406d-993b-75a4db38f518","order_by":1,"name":"Keping Sun","email":"","orcid":"","institution":"China Construction Third Engineering Bureau Group Co., 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07:32:21","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":62197,"visible":true,"origin":"","legend":"\u003cp\u003eImpact of multiple factors on building energy consumption and carbon emissions\u003c/p\u003e","description":"","filename":"floatimage13.png","url":"https://assets-eu.researchsquare.com/files/rs-8748115/v1/6b5c5589ef237c65808fd3db.png"},{"id":102750811,"identity":"8e20a11e-7eef-44c5-a879-311e3a6588d2","added_by":"auto","created_at":"2026-02-16 09:22:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3359652,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8748115/v1/31ed00dd-19ad-4f27-ae90-d7d30adf0a7d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Research on Time-Dynamic Operation Characterization of Energy Efficiency and Decarbonation of Green Building based on SD","fulltext":[{"header":"1.Introduction","content":"\u003cp\u003eRapid industrialization has intensified global energy and environmental challenges, with the Copenhagen Climate Summit (2009) and Paris Agreement (2016) establishing a framework for international cooperation\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. The IPCC\u0026rsquo;s 2023 Sixth Assessment Report highlights climate change\u0026rsquo;s impacts and underscores the urgency of a global low-carbon energy transition\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Greenhouse gases, especially CO\u003csub\u003e2\u003c/sub\u003e, drive global environmental change, with the construction sector responsible for over 70% of total CO\u003csub\u003e2\u003c/sub\u003e emissions \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo curb GHG emissions, China\u0026rsquo;s \u003cem\u003e14th Five-Year\u003c/em\u003e Plan promotes green industry restructuring, prioritizing the construction sector\u0026mdash;one of the nation\u0026rsquo;s top three energy consumers\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Notably, the annual energy consumption of large public buildings in China is 10 to 22 times higher than that of residential buildings\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Recent studies show operational carbon emissions of public buildings total 830 Mt\u0026mdash;38% of all operational building emissions\u0026mdash;with energy use surpassing that of residential buildings\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Therefore, the carbon reduction potential of public buildings is greater than that of other building types\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDespite their smaller footprint, higher-education buildings require urgent energy and carbon reduction due to their high intensity of use\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Academic buildings in particular, as core facilities for daily operations, are densely populated and have high energy demand\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Prolonged lighting, HVAC, and equipment use drive high energy consumption\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Therefore, optimizing academic buildings\u0026rsquo; energy and carbon strategies supports campus sustainability and offers a model for other public buildings.\u003c/p\u003e \u003cp\u003eHowever, the complexity and dynamic behavior of building energy systems limit traditional models in capturing interactions among influencing factors\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. This study adopts a system dynamics (SD) approach, integrating building energy and carbon theories, to model a Wuhan university building. The model quantifies climate change impacts, integrates photovoltaic energy, and simulates new building envelope technologies dynamically. Scenario simulations during the operational phase reveal interrelationships between energy and carbon factors in green buildings under climate change. Correspondingly, energy-saving strategies are proposed.\u003c/p\u003e \u003cp\u003eIn conclusion, optimizing university buildings is essential to achieving China\u0026rsquo;s \u0026ldquo;dual-carbon\u0026rdquo; goals and aligns with green-building sustainability principles.The study aims to provide a theoretical basis and decision-support for energy conservation and emission reduction in university public buildings. Findings offer guidance for future building design and management, and evidence for optimizing energy use in a changing climate. Additionally, achieving a 2030 carbon peak requires stronger policy guidance and adoption of innovative low-carbon technologies, underscoring integrated solutions. The study bridges static evaluation and dynamic simulation for carbon-mitigation strategies in university public buildings.\u003c/p\u003e"},{"header":"2 Literature Analysis","content":"\u003cp\u003eExisting literature examines public building energy consumption and carbon emissions from multiple perspectives\u0026mdash;primarily influencing factors, energy-saving potential, predictive analysis, and decarbonization strategies\u0026mdash;providing a theoretical and practical basis for university building energy management. Accordingly, this section reviews related studies from the perspectives of influencing factors and predictive approaches.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Research on the influencing factors of energy consumption and carbon emission\u003c/h2\u003e \u003cp\u003eIn terms of influencing factors, relevant studies have identified that the energy consumption and carbon emissions of university buildings are affected by both macro and micro-level factors, such as climate change, building characteristics, socio-economic conditions, population dynamics and policy measures. For instance, Wan et al. found that climate change significantly increases public building energy use, particularly in regions with high heating and cooling demands\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. YANG and TANG demonstrated that optimizing insulation materials can substantially reduce heating and cooling energy demand in public buildings\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. At a Thai university, Rewthong et al. observed a 10% annual rise in electricity consumption, largely from expanded use of air conditioning, lighting, and elevators\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Yildiz et al. reported that a mix of passive and active measures can cut university building energy use by over 60%, as confirmed by measured data and simulations\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. AD Borghi et al. compared carbon-neutral design strategies at universities in Italy and the United States, highlighting differences in achieving neutrality through design and retrofits\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. M Moradibistouni et al. examined how design goals, climate, and material performance interact to achieve near-zero energy standards\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Such multi-factor approaches offer valuable insights for guiding buildings toward energy-saving goals.\u003c/p\u003e \u003cp\u003eMost prior research relies on static simulations to assess how design optimization, operational management, and material or technology choices influence building energy use and carbon emissions, offering useful theoretical and practical guidance. However, these static approaches often overlook the dynamic changes that occur during the actual operation of university public buildings. As a result, static simulations frequently miss the temporal\u0026ndash;spatial, policy, and environmental interactions that shape real-world performance. Consequently, traditional static approaches have become inadequate for decision-relevant analysis. Accordingly, advancing dynamic-system simulations that capture the spatiotemporal interactions of building energy use is an urgent research priority, enabling more accurate and actionable strategies for energy conservation and carbon management.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 SD simulation of building energy efficiency and carbon emission\u003c/h2\u003e \u003cp\u003eSD is a methodology for understanding, modeling and analyzing the behavior of complex systems over time, originally developed by Jay W. Forrester at MIT in the 1950s\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, which is well-established for analyzing complex systems and is increasingly applied to energy and environmental problems. The scenario analysis method\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, is commonly coupled with SD to formulate plausible futures and dynamically project their evolution. Yang et al. confirmed that dynamic lifecycle measurements of building carbon emissions are more accurate than traditional static methods\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Li G et al. used SD to predict carbon emissions at different stages of a building\u0026rsquo;s lifecycle, identifying the key driving factors behind these emissions \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Monna S et al. analyzed energy consumption simulations for selected residential buildings, establishing a benchmark for evaluating energy-saving retrofits and implemented three different retrofit scenarios\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Mohamed Marzouka et al. took a comprehensive approach by considering sustainable building indicators, including energy consumption, carbon emissions, construction costs and living conditions. Through the use of an SD model, they dynamically assessed the short- and long-term impacts of different design scenarios\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Lin CX et al. incorporated dynamic variables such as building energy consumption, residential electricity demand、energy structure and policy implementation into an SD model to simulate and predict the timing of carbon peaking under different scenarios. The model also demonstrated the emission reduction effects of various decarbonization pathways\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. From a macro perspective, Huo TF et al. explored the dynamic impact of population growth, increased building demand, and different energy efficiency policies on carbon emissions as urbanization progresses\u003csup\u003e25 26\u003c/sup\u003e. Shi Q \u003csup\u003e27\u003c/sup\u003e and Huo TF\u003csup\u003e28\u003c/sup\u003e considered the extent to which different rates of technological advancement affect carbon emissions on different time scales, and the results suggest that technological advancement and energy transition will remain the key driving factors.\u003c/p\u003e \u003cp\u003eWhile extensive research examines building energy and carbon performance, few studies address the combined effects of economic activity, energy transition, and climate variation on public buildings. Focusing on university facilities, this study develops a systematic SD-based framework for dynamic energy\u0026ndash;carbon analysis and conservation guidance. Grounded in sustainable development theory, the framework integrates macro-level influences with single-building dynamics to quantify climate impacts, model feedbacks, and assess multiple carbon-reduction strategies by effectiveness. Scenario-based simulations project long-term trends and inform targeted policies. The results provide actionable recommendations for policymakers, practitioners, and academics, supporting China\u0026rsquo;s long-term energy and carbon goals and contributing scientific evidence to global climate-mitigation efforts and international cooperation.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Research methodology","content":"\u003cp\u003eSD can systematically display the relationships between various factors within complex systems, which are often difficult to capture using traditional methods. It reveals hidden feedback mechanisms and represents the dynamic interactions within the system through feedback loops, stock and flow structures, offering a deeper understanding of the system\u0026apos;s internal dynamics.\u003c/p\u003e\n\u003cp\u003eThis paper, based on System Dynamics theory and the indicator system of influencing factors for public building energy consumption and carbon emissions, constructs an SD model for public building energy consumption and carbon emissions.\u0026nbsp;The causal loop diagram is established in this model, which specifies the relationship between variables such as socio-economics、urban energy、climate environment、relevant policy、building energy consumption and carbon emission, and the causal loop diagram is transformed into a stock flow diagram model using VENSIM software.\u0026nbsp;Taking a university building from Wuhan as an example, the energy consumption and carbon emission of public buildings during the period of 2023-2035 are simulated based on the historical data from 2009-2022.\u0026nbsp;By analyzing the factors driving the increase in energy consumption and carbon emissions, the study proposes corresponding countermeasures to address future energy demands and environmental pressures. These recommendations aim to provide targeted strategies for improving energy efficiency and reducing carbon emissions in public buildings, contributing to sustainable development goals. Figure 1 illustrates the research technology route of this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eResearch objects and system boundaries\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to the Wuhan Statistical Yearbook, Wuhan City has a total of 1,254 schools of various types. Newly constructed buildings for regular primary and secondary schools accounted for 35% of the city\u0026apos;s total building completions in 2022, indicating a significant proportion of educational buildings in urban construction. Therefore, this paper selects a teaching and research complex in Wuhan as its research subject. The building has a floor area of approximately 12,100 m\u0026sup2;, with a reinforced concrete main structure and an exterior wall self-insulating envelope system. The study\u0026apos;s system boundary encompasses four interacting subsystems: socioeconomic, energy, building, and climate. It focuses on analyzing energy consumption and carbon emission characteristics during the building\u0026apos;s operational phase, using these as core output metrics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eSystem architecture modeling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo analyze the systemic characteristics of public building energy use and carbon emissions, and clarify variable relationships, a causal loop diagram (CLD) is developed in Figure 2 showing interactions among subsystems.\u003c/p\u003e\n\u003cp\u003eBuilding envelope measures can provide nearly half of potential energy savings, as changes in thermal transmittance affect heat exchange, indoor temperature, and energy use. Economic growth expands building areas and energy demand, raising emissions, while technologies such as PV generation and insulated walls mitigate these effects. Climate factors, notably summer cooling demand and the urban heat island (UHI) effect, also elevate consumption,UHI intensification forms a positive feedback with energy use. Solar radiation both influences temperature and supports PV generation, reducing reliance on conventional energy. However, temperature swings alter cooling or heating needs, sometimes increasing overall demand. This interplay underscores the need for strategies addressing both technological and environmental drivers. Building and climate CLDs are shown in Figures 3\u0026ndash;4.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Figure 5 provides the energy CLD, where an increase in Gross Domestic Product (GDP) leads to a rise in total energy consumption, due to the increased demand for more energy as a result of increased economic activity. Optimization of the energy structure can mitigate this trend and reduce overall carbon emissions by increasing the share of renewable energy sources. Energy consumption intensity and energy utilization efficiency are two key variables in this CLD. Improving energy utilization efficiency can significantly reduce energy consumption per unit of floor area, thereby reducing the building\u0026apos;s carbon footprint. On the other hand, a reduction in energy intensity can also contribute to a reduction in overall energy consumption, which can be achieved by adopting energy-efficient technologies and optimizing the way energy is used. The economic CLD associated with it is shown in Figure 6.\u003c/p\u003e\n\u003cp\u003eAs CLDs only show causal paths, VENSIM is used to extend them into a stock\u0026ndash;flow diagram (Figure 7) capturing cumulative effects. Variables are classified as stocks (system states), flows (rates of change), and auxiliaries (constants).\u003c/p\u003e\n\u003cp\u003eThe SD model comprises socio-economic, energy, building, and climate subsystems with interlinked feedbacks. Examples include education influencing conservation awareness, energy structure affecting consumption intensity, and climate factors impacting emissions\u0026mdash;modifiable via policy. Equations integrate these links to capture dynamic changes, identify emission sources, and simulate reduction processes.\u003c/p\u003e\n\u003cp\u003eThis model is built upon the foundations of system dynamics, green building design standards, and real engineering cases.\u0026nbsp;Compared with traditional static models, the model is able to simulate situational changes of the system under different conditions, revealing long-term trends and potential non-linear effects.\u0026nbsp;Additionally, the model\u0026apos;s structure is flexible and scalable, allowing for adjustments and optimizations to suit different types of public buildings. As a result, the model holds broad application potential not only in the research field but also in practical decision-making. It can assist policymakers in evaluating the potential impacts of various policies and technological solutions, providing a solid foundation for energy efficiency management and emission reduction strategies in public buildings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eValidity testing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further investigate the dynamic characteristics of energy consumption and carbon emissions in public buildings, this study conducted a quantitative analysis of the system\u0026apos;s stock-and-flow diagram after completing the qualitative CLD analysis. The key lies in accurately determining the system\u0026apos;s main equations and parameters to ensure that the model accurately reflects the actual process of building energy consumption.\u0026nbsp;The parameter settings in this study are mainly obtained through the following ways:\u003c/p\u003e\n\u003cp\u003e(1) Direct data, such as GDP, birth and death rates, are sourced from the \u003cem\u003eWuhan Statistical Yearbook\u003c/em\u003e, \u003cem\u003eChina Energy Statistical Yearbook\u003c/em\u003e, China Meteorological Data Service Center, and existing literature.\u003c/p\u003e\n\u003cp\u003e(2) Indirect data are obtained through regression analysis using historical data and the table function in VENSIM software.\u003c/p\u003e\n\u003cp\u003eValidity testing refers to conducting operational and historical tests to assess whether the information obtained from the model and its predictions can accurately reflect and simulate building energy consumption and carbon emissions. In this study, the historical period is set from 2009 to 2022, with the model starting in 2009 and the prediction period covering 2023 to 2035.\u003c/p\u003e\n\u003cp\u003e(1) Operational Testing: Due to the numerous and interrelated factors influencing building energy consumption and carbon emissions, this study selected three different simulation time steps\u0026mdash;0.25 years, 0.5 years, and 1 year\u0026mdash;for operational testing to verify the overall stability of the model. The simulation results are shown in Figure 8. The simulation results show that different simulation steps have less impact on building energy consumption and carbon emissions and remain stable, which indicates that the model has passed the operational test, and thus the model is stable and feasible.\u003c/p\u003e\n\u003cp\u003e(2) Historical Testing: Historical testing involves comparing simulation results with historical data. When the relative error falls within a reasonable range, it indicates that the SD model is accurate and effective. This study selected total population, regional GDP, and tertiary industry value as comparison indicators. The historical validation results are shown in Table 1, with all relative errors within an acceptable range. This demonstrates that the model results closely align with actual data, capable of reasonably and accurately reflecting objective reality.\u003c/p\u003e\n\u003cp\u003eTable 1. Historical simulation test results\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 49px;\"\u003eYear\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 169px;\"\u003eTotal population (millions)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 168px;\"\u003eGDP (billions)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 183px;\"\u003eTertiary sector value(billions)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003eHistoric-al\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003eSimula-tion\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003eError\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003eHistoric-al\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003eSimula-tion\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003eError\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003eHistoric-al\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003eSimula-tion\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003eError\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e2009\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e835.55\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e835.55\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e0.00\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e4620.86\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e4620.86\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e0.00\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e2581.52\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e2581.52\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e0.00\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e2010\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e836.73\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e838.70\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e-0.24\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e5565.93\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e5568.96\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e-0.05\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e2942.91\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e2970.6\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e-0.94\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e2011\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e827.24\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e840.03\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e-1.55\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e6762.2\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e6517.05\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e3.63\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e3418.65\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e3359.69\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e1.72\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e2012\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e821.71\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e841.77\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e-2.44\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e8003.82\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e7520.82\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e6.03\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e3987.99\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e3792.61\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e4.90\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e2013\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e822.05\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e846.13\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e-2.93\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e9051.27\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e8567.93\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e5.34\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e4523.32\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e4303.07\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e4.87\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e2014\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e827.31\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e851.46\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e-2.92\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e10069.48\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e9662.53\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e4.04\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e5501.04\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e4906.4\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e10.81\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e2015\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e829.27\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e857.64\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e-3.42\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e10905.6\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e10794.9\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e1.02\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e5881.58\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e5611.35\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e4.59\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e2016\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e833.85\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e863.60\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e-3.57\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e11531.42\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e11981.8\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e-3.91\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e6609.93\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e6421.77\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e2.85\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e2017\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e853.65\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e868.81\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e-1.78\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e13090.81\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e13215.9\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e-0.96\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e7677.86\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e7335.59\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e4.46\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e2018\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e883.73\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e872.25\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e1.30\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e14928.72\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e14483.3\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e2.98\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e8987.31\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e8347.4\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e7.12\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e2019\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e906.40\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e879.42\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e2.98\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e16223.21\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e15814.1\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e2.52\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e9855.34\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e9450.87\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e4.10\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e2020\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e916.19\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e885.65\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e3.33\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e15516.07\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e17226\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e-11.02\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e9786.71\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e10635.2\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e-8.67\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e2021\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e934.10\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e888.53\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e4.88\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e17688.03\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e18720.6\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e-5.84\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e11109.47\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e11892.5\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e-7.05\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e2022\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e944.42\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e891.26\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e5.63\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e18866.43\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e19673\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e-4.28\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e11673.99\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e13223.1\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e-13.27\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Scenario design and simulation parameters\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eScenario simulations analyze the impact of different emission reduction measures on energy consumption and carbon emissions during the building operation phase by establishing two categories of scenarios: single-factor and multi-factor. The single-factor scenarios encompass climate, building performance, and policy regulation, comprising 10 sub-scenarios. Multi-factor scenarios comprise four integrated scenarios to evaluate synergistic emission reduction effects. The baseline scenario represents unchanged existing factors, with specific parameters detailed in Table 2. Table 3 illustrates the classification of the 10 single-factor sub-scenarios and their parameter adjustments.\u003c/p\u003e\n\u003cp\u003eTable 2. Baseline scenario parameters\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003eRelevant factor\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003eParameter value\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003eHeat transfer coefficient of roof\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e0.44 W/（m\u003csup\u003e2\u003c/sup\u003e\u0026middot;K）\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003eHeat transfer coefficient of external wall\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e0.7 W/（m\u003csup\u003e2\u003c/sup\u003e\u0026middot;K）\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003eNumber of air changes\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e1\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003eSolar photovoltaic area\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e100m\u003csup\u003e2\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003ePhotovoltaic system efficiency\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e0.4\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003eTemperature difference of hot water\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e40℃\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003eConstruction area\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e12100m\u003csup\u003e2\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3. Single-factor classification\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003eScenarios\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003eclassification\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003eDescription of adjustments\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003eFoundation\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003eBaseline scenario\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003eBS\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003eAll factors remain unchanged\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 140px;\"\u003eEnvironmental scenarios\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003eS1\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 204px;\"\u003eHeat island effect increased by 10%\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\u003csup\u003e29\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003eS2\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 204px;\"\u003eTemperature increase of 1\u0026deg;C\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\u003csup\u003e30\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003eS3\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 204px;\"\u003e5% increase in green space in urban areas\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\u003csup\u003e31\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 140px;\"\u003eArchitectural scenarios\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003eS4\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 204px;\"\u003eAdditional technical support for self-insulation of external walls\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003eS5\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 204px;\"\u003eReduction of roof heat transfer coefficient to 0.15W/（m\u003csup\u003e2\u003c/sup\u003e\u0026middot;K）\u003cbr\u003e\u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 89px;\"\u003e\u003csup\u003e32\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003eS6\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 204px;\"\u003eReduction of external walls heat coefficient to 0.55W/（m\u003csup\u003e2\u003c/sup\u003e\u0026middot;K）\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003eS7\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 204px;\"\u003eNumber of air changes increased to 1.2\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003eS8\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 204px;\"\u003eIncrease in solar photovoltaic panel area by 15m\u003csup\u003e2\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\u003csup\u003e33\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 140px;\"\u003ePolicy scenarios\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003eS9\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 204px;\"\u003eGrid carbon emission factor reduced to 0.4\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003eS10\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 204px;\"\u003eReduce energy consumption intensity by 10%\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\u003csup\u003e34\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSince the changes in energy consumption and carbon emissions of public buildings are the result of the joint action under the complex influence of many factors, the change of a single variable may result in a biased situation. Multi-factor scenario analysis builds upon single-factor simulations by combining two or more factors in order to achieve a dynamic system simulation. This approach lays the foundation for exploring energy-saving and carbon-reduction pathways in public buildings when responding to climate change under the influence of multiple factors.\u0026nbsp;Four scenarios are proposed in this paper: Building Optimization Scenario (BOS), Policy Regulation Scenario (PRS), Technological Adaptation Scenario (TAS), and Integrated Application Scenario (IAS). Table 4 provides the specific parameter settings and descriptions.\u003c/p\u003e\n\u003cp\u003eTable 4. Multi-factor classification\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"570\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003eScenarios\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003eclassification\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003eDescription of adjustments\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003eBaseline scenario\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003eBS\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003eAll factors remain unchanged\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003eBuilding Optimization Scenario\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003eBOS\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 222px;\"\u003eReduction of roof heat transfer coefficient to 0.15W/(m\u003csup\u003e2\u003c/sup\u003e\u0026middot;K),reduction of external walls heat coefficient to 0.55W/（m\u003csup\u003e2\u003c/sup\u003e\u0026middot;K）\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003ePolicy Regulation Scenario\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003ePRS\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003eGrid carbon emission factor reduced to 0.4, reduce energy consumption intensity by 10%\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003eTechnological Adaptation Scenario\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003eTAS\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003eAdditional technical support for self-insulation of external walls，increase in solar photovoltaic panel area by 15m\u003csup\u003e2\u003c/sup\u003e，heat island effect increased by 10%, temperature increase of 1\u0026deg;C，5% increase in green space in urban areas\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003eIntegrated Application Scenario\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003eIAS\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003eBOS+PRS+ Additional technical support for self-insulation of external walls, increase in solar photovoltaic panel area by 15m\u003csup\u003e2\u003c/sup\u003e，5% increase in green space in urban areas\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"4. Discussion","content":"\u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAnalysis of historical trends and baseline scenarios\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis paper establishes a baseline scenario (BS) based on real-world conditions and forecasts annually through 2035. In this scenario, it is assumed that all variables remain unchanged, with everything progressing according to historical trends and causal developments. Without setting energy-saving and carbon reduction targets or implementing effective energy-saving measures and policies, the model in this scenario reflects the future outcomes under a no-intervention scenario.\u0026nbsp;The simulation results, as shown in Figure 9, indicate that the development of energy consumption and carbon emissions in university buildings in Wuhan under the BS scenario can be divided into three distinct phases.\u003c/p\u003e\n\u003cp\u003ePhase 1 (2009\u0026ndash;2016): Building carbon emissions and energy consumption rise slowly, mainly due to Wuhan\u0026rsquo;s modest economic growth, gradual urbanization, and limited expansion of university building areas, resulting in smaller increases in demand. Phase 2 (2017\u0026ndash;2023): Growth accelerates as rapid economic development and urbanization drive higher energy demand. Although COVID-19 temporarily slows activity in 2020, post-pandemic recovery and resumed construction lead to a sharp rebound. Phase 3 (2024\u0026ndash;2035): Emissions and energy use continue to grow rapidly, driven by Wuhan\u0026rsquo;s sustained economic expansion. Policies such as the \u003cem\u003e13th Five-Year Plan\u003c/em\u003e and the \u003cem\u003ePeak Carbon Action Plan\u003c/em\u003e are introduced, but their effects take time to materialize due to delays in technology development and adoption.\u003c/p\u003e\n\u003cp\u003eWithout additional measures, total building energy consumption and carbon emissions are projected to reach 282.7 kWh and 146.3 tons by 2035, challenging the goals of carbon peaking by 2030 and neutrality by 2060. Identifying key influencing factors is essential for targeted policies, technological innovation, and sustainable development.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eResults and analysis of the single-factor experiment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCompared to simple forecasts, a deeper assessment of the potential impacts of future building energy consumption and carbon emissions is more practically significant. Using the methodology of SD, it is possible to conduct a single-factor analysis of 10 key factors (as detailed in Table 4) and adjust these factors under different scenarios to reveal their specific impacts on future building energy consumption and carbon emissions. These include environmental factors, envelope performance, behavioral influences, and policy interventions, as shown in Figure 10.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the environmental scenarios, the heat island effect significantly impacts building energy consumption and carbon emissions, as shown in Figures 10b and 10d. environmental scenarios demonstrate significant impacts. The intensified urban heat island effect (S1) leads to a 2.4% increase in carbon emissions by 2035, driven by elevated night-time temperatures and residual heat accumulation, especially in cities like Wuhan characterized by hot summers and cold winters. This increases the cooling load, making buildings more energy-intensive during extended warm periods.\u003c/p\u003e\n\u003cp\u003eIn contrast, the 1\u0026deg;C temperature rise scenario (S2) presents a slight 1.1% reduction in emissions compared to the baseline (BS). This result is attributed to a combination of decreased winter heating demand, shorter academic terms during hot months, and administrative energy-saving policies, such as restrictions on air-conditioning use. Scenario S3, which increases urban green space by 5%, reduces emissions and energy use by approximately 2.3% by mitigating ambient temperature through improved microclimate conditions.\u003c/p\u003e\n\u003cp\u003eRegarding building envelope performance, scenario S4, which applies external wall self-insulation technology, yields the most significant results, reducing both energy consumption and carbon emissions by 6%. In contrast, reducing the external wall heat transfer coefficient alone (S6) achieves only a 2.4% decrease due to the persistence of thermal bridges around windows and junctions. Roof insulation enhancement (S5) delivers a 1.4% emissions reduction. Meanwhile, expanding solar PV panel coverage by 15m\u0026sup2; (S8) has limited impact on energy consumption but contributes positively to emission reductions by displacing fossil-based electricity with renewable energy, aligning with China\u0026rsquo;s 14\u003csup\u003eth\u003c/sup\u003e Five-Year Plan on renewable energy expansion.\u003c/p\u003e\n\u003cp\u003eScenario S7, which increases the number of air changes per hour to improve indoor air quality, results in a 2.5% rise in energy use. This is especially problematic in regions with harsh seasonal climates, as thermal regulation of fresh air imposes a higher load on HVAC systems. The trade-off between health and energy efficiency emphasizes the importance of precision in ventilation system design.\u003c/p\u003e\n\u003cp\u003eAmong all scenarios, policy-driven interventions (S9 and S10) are the most effective. Scenario S9, which reduces the power grid emission factor to 0.4, achieves a 19.4% reduction in carbon emissions. Scenario S10, which lowers energy intensity by 10%, achieves a 10% reduction in energy use. Notably, Figure 10 reveals that S10\u0026rsquo;s impact is more immediate, while S9\u0026rsquo;s cumulative benefits become more prominent over time. This suggests that short-term behavior-based strategies and long-term structural energy reforms must be coordinated\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003csup\u003e35\u003c/sup\u003e. Importantly, policy measures can not only bring about significant emission reductions in the short term, but also achieve long-term sustainable development through systemic change and technology facilitation. Thus, relying on targeted government policies is considered the best option for reducing building energy consumption and carbon emissions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn summary, energy consumption intensity, grid emission coefficient, whether to apply external wall self-insulation technology, and heat transfer coefficient of external walls and roofs have the most significant impact on building energy consumption and carbon emissions.\u0026nbsp;The effects of air temperature and solar photovoltaic are also more obvious, and the effects of green space rate in urban areas, heat island effect and the number of air changes on building energy consumption and carbon emissions are relatively limited.\u003c/p\u003e\n\u003cp\u003eFigure 11 illustrates the combined impact of various single-factor scenarios on the direction and magnitude of changes in building operational energy consumption and carbon emissions. Overall, emission-reduction factors exert a significantly stronger influence on the system than emission-increase factors. Among these, policy regulation and operational efficiency improvement scenarios (e.g., S9, S10) dominate in reducing energy consumption and carbon emissions, representing the largest single-factor contributors to emission reductions. Building technology measures like self-insulating exterior walls (e.g., S4) yield stable but relatively limited energy savings and emission reductions. In contrast, climate disturbances and operational parameter changes (e.g., S1, S7) primarily result in modest increases in energy consumption and carbon emissions, exerting relatively limited overall driving force on the system.\u003c/p\u003e\n\u003cp\u003eIt should be noted that single-factor scenario analysis struggles to capture the synergistic effects of multiple factors in real-world operations. Therefore, subsequent multi-factor scenario simulations will be conducted to comprehensively evaluate the energy-saving and carbon-reduction outcomes under different combinations of measures.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eResults and analysis of the\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003emulti-factor\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;experiment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAlthough certain policy measures and technological improvements have achieved remarkable results in reducing emissions and saving energy, it is difficult to achieve a comprehensive reduction in carbon emissions and maximizing energy consumption due to the one-sided nature of single-factor scenario simulation. Therefore, there is an urgent need for comprehensive scenario simulation analysis.\u0026nbsp;Based on single-factor scenario simulation, the overall impacts on building energy consumption, annual energy consumption, carbon emissions and annual carbon emissions under different combinations of strategies are comprehensively assessed through the synergistic effect of multi-factor scenario simulation. These scenarios are categorized into four perspectives: building envelope performance optimization, macroeconomic policy regulation, new technology application to address climate change, and comprehensive carbon reduction measures. An integrated evaluation approach is used to assess these scenarios.\u003c/p\u003e\n\u003cp\u003eAs shown in Figure 12, the overall trend shows that the carbon emissions and energy consumption of buildings under different scenarios generally increase year by year, but the increase in each scenario is lower than that of the BS and there is a significant difference between them. In the BS, carbon emissions and energy consumption increase at the fastest rate, while in the IAS, these growth trends are significantly suppressed. This implies that energy efficiency programs in different combinations have different levels of impact on building energy consumption and carbon emissions. Therefore, targeted analysis and specific policies should be carried out in conjunction with specific objectives and realities.\u003c/p\u003e\n\u003cp\u003eIn BOS, the total building carbon emissions are reduced by about 3.8% and the total energy consumption is reduced by about 3.6% in 2035. The effect of energy saving and emission reduction is relatively inferior to that of TAS, but the curves of BOS are shown to be increasing and parallel to the BS curves from the perspective of annual energy consumption and carbon emission of buildings, as shown in Figure 10a and b. This trend indicates that the retrofitting and optimization of the performance of building envelopes have achieved a direct effect in energy saving and emission reduction without any additional impact. Wuhan has been actively promoting green building standards in recent years, and by optimizing the building maintenance structure, the building energy consumption and carbon emissions have been reduced, which is in line with the \u003cem\u003eGreen Building Action Plan\u003c/em\u003e advocated by China. This result is also consistent with the findings of Zhu J and others in the area of improving building envelope performance to reduce energy consumption and carbon emissions \u003csup\u003e36\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIn the PRS context, building carbon emissions and energy consumption were successfully reduced by 28.2% and 10.3% as a result of the government\u0026rsquo;s intervention, indicating that the implementation of the policy measures was effective in reducing the carbon intensity and overall energy consumption in electricity use.\u0026nbsp;However, in the case of PRS, Figures 10b and d depict trends in total building energy consumption and carbon emissions, where the difference between these values and the BS is initially small and then gradually increases over time.\u0026nbsp;This phenomenon can be attributed to the lag in the popularization of policy promotion, as the gradual popularization of government-introduced policies to guide the development and application of new technologies, and the improvement and promotion of ultra-low-energy codes substantially reduced energy consumption and carbon emissions.\u0026nbsp;Additionally, with government support, promoting clean energy and improving energy efficiency have yielded notable emission reduction results. However, it is important to note that the decrease in energy consumption is significantly lower than the reduction in carbon emissions. This discrepancy may be due to the government\u0026rsquo;s focus on macro-level building standards while neglecting efforts to raise energy-saving awareness among users. The lack of corresponding laws, regulations, and incentive or penalty measures means that even though buildings meet regulatory standards, energy misuse by occupants diminishes the overall energy-saving effectiveness.\u003c/p\u003e\n\u003cp\u003eThe TAS simulation demonstrates that advancements In technology Integrated with the building itself enable a certain level of response to climate change. The results show a reduction in carbon emissions by approximately 7.8% and energy consumption by about 7% compared to the BS, effectively curbing the annual increase in carbon emissions. However, the total energy consumption and carbon emissions reduced by buildings in this scenario go through a process from less to more. Specifically, at the initial stage of the application of a new technology, there may be a period of technological break-in and adaptation, which requires time for optimization and adjustment. So the initial energy saving and emission reduction effect may not be as significant as expected. However, with the growth of time, the application of new technologies gradually becomes popular and mature, the coverage expands, the optimization effect of the technology becomes apparent, energy consumption is significantly reduced, and the reduction of carbon emissions becomes more obvious.\u0026nbsp;On the economic front, with the gradual reduction in the cost of energy-saving technologies and the increase in market demand, more enterprises are willing to invest in and adopt new technologies, further promoting the popularization of technology application and the manifestation of its effects thus gradually widening the gap with BS. Hence, the application of new technologies not only increases the resilience of the building itself and its ability to adapt to climate change, but also creates a win-win situation in terms of green low-carbon and economic practicality.\u0026nbsp;It is also worth noting that the use of combined measures in the IAS results in a curve with the same trend as the TAS, but with a lower overall magnitude and a relatively longer time required than the TAS. This is due to the fact that it not only simply superimposes multiple mitigation measures, but also lies in the complex interactions and complementary effects between its factors, while the synergistic effects of more factors require more time. The report of Magrini et al\u003csup\u003e37\u003c/sup\u003e, \u0026nbsp;also confirms that building carbon emission is a complex dynamic system, which is influenced by factors such as building envelope performance, climatic conditions and occupant behavior, and therefore its significant reduction effect is only visible in the middle and late stages, with a reduction of carbon emission by about 36.4% and energy consumption by about 19.3%. The IAS scenario outperforms other scenarios in terms of emission reduction primarily due to its systematic integration and multi-level feedback mechanisms. More importantly, it achieves comprehensive intervention from the macro to the micro level. Policy regulation not only drives the adoption of new technologies but also provides economic and policy support for building optimization measures. In turn, technological innovation strengthens the effectiveness of policy implementation, enhancing the enforcement of policies through the tangible results of energy conservation and emission reduction. The reasonable combination of various energy-saving measures creates cumulative emission reduction effects over time, increasingly highlighting their advantages. This trend reflects the power of systematic integration, where different measures enhance each other\u0026rsquo;s effectiveness, generating a synergistic effect that results in a \u0026ldquo;1+1\u0026gt;2\u0026rdquo; outcome \u003csup\u003e38\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eFigure 13 illustrates the impacts and relative contributions of different integrated single factors on building operational energy consumption and carbon emissions across multiple dimensions. It further reveals the synergistic effects achieved through combining various measures. Compared to the single-scenario approach, the Building Optimization Scenario (BOS) and Technology Adaptation Scenario (TAS) demonstrate enhanced energy-saving and emission-reduction capabilities, though their overall contributions remain lower than those of the Policy Regulation Scenario (PRS). The Integrated Application Scenario (IAS) demonstrates the most pronounced effects on both metrics, contributing significantly more to carbon emission reductions than other scenarios while also dominating in energy consumption reduction.\u003c/p\u003e\n\u003cp\u003eOverall, BOS, TAS, PRS, and IAS represent distinct emission reduction pathways: structural optimization, technological advancement, policy regulation, and integrated synergy, respectively. Policy regulation and multi-factor integrated application exert the most significant suppression on energy consumption and carbon emissions during the building operation phase. This indicates that in practical emission reduction efforts, it is essential to coordinate the synergistic implementation of multiple measures based on specific objectives and conditions.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eBased on the constructed system dynamics model, this study analyzed the spatiotemporal patterns of energy consumption and carbon emissions during the building operation phase under different scenarios. The main conclusions are as follows:\u003c/p\u003e \u003cp\u003e(1) Long-term trend: Under the baseline scenario (BS), building energy consumption and carbon emissions continue to rise without any intervention measures, posing challenges to China's goals of peaking carbon emissions by 2030 and achieving carbon neutrality by 2060. Effective policy and technical measures are crucial to reversing this trend.\u003c/p\u003e \u003cp\u003e(2) SD Model Validity: This model accurately simulates historical trends and reflects the dynamic interactions among policy, technology, climate, and socioeconomic factors. It quantitatively analyzes the combined effects of nonlinear factors under different scenario simulations.\u003c/p\u003e \u003cp\u003e(3) Single-factor results: Reducing the carbon emission factor of the power grid achieved the greatest emission reduction (19.4%), while lowering energy consumption intensity yielded the highest energy savings (10.0%). In the short term, operational measures demonstrate more pronounced effects; over the long term, the cumulative benefits of structural decarbonization measures become more prominent, indicating that strategies must be tailored to the time horizon. (4) Integrated Action Strategy (IAS): By combining policy, technology, and building envelope optimization, IAS achieved a 36.4% reduction in carbon emissions and a 19.3% decrease in energy consumption, outperforming single-method approaches. Simulation data indicates that universities should implement comprehensive energy-saving and emission-reduction strategies centered on reducing energy intensity and optimizing grid carbon emission factors based on actual conditions. These efforts should be complemented by architectural design optimization and the promotion of energy-saving technologies to ensure steady carbon emission reductions and achieve energy conservation targets.\u003c/p\u003e \u003cp\u003eSD modeling can dynamically predict changes in energy consumption and carbon emissions for university buildings while analyzing the underlying causes of these outcomes. Based on this, the research model provides quantitative evidence for energy consumption assessment and operational optimization under green building standards, further supporting the implementation and realization of green transformation in university buildings. Furthermore, this study concludes that achieving the 2030 carbon peak target for university public buildings hinges on proactively controlling the growth trend of operational-phase carbon emissions, rather than implementing concentrated energy-saving and emission-reduction measures only as the target year approaches.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e5.1Policy Recommendations\u003c/h2\u003e \u003cp\u003eBased on the above analysis and research findings, this paper proposes the following three policy recommendations:\u003c/p\u003e \u003cp\u003e(1) To achieve the 2030 carbon peak target, university public buildings should transition from \u0026ldquo;retroactive energy conservation\u0026rdquo; to \u0026ldquo;process control\u0026rdquo; as soon as possible. Research indicates that continuing existing operational models will result in inertial growth of carbon emissions during the building operation phase, making it difficult to naturally peak before 2030. Therefore, it is recommended to use carbon emission intensity during the building operation phase as a management lever, setting clear phased control targets for major university public buildings and continuously tracking progress during operational management. By decelerating the growth rate of carbon emissions in advance, the emissions curve can gradually flatten before 2030, thereby establishing an achievable carbon peak pathway. This approach avoids the need for concentrated remedial measures as the target year approaches.\u003c/p\u003e \u003cp\u003e(2) Research indicates that relying solely on optimizing building envelope measures or any single energy-saving technology is unlikely to significantly alter carbon emission trends in the short term. In contrast, the synergistic effects of operational management, energy structure adjustment, and building performance optimization prove more critical. Therefore, in implementing building energy retrofits, universities should avoid fragmented implementation of energy-saving measures. Instead, they should holistically consider adjustments to operational management, integration of renewable energy sources, and enhancement of existing building performance, advancing these efforts within the same timeframe to achieve stable and sustained emission reduction outcomes.\u003c/p\u003e \u003cp\u003e(3) Dynamic calculations indicate that rising temperatures and the urban heat island effect may, under certain conditions, increase building cooling demands, partially offsetting energy-saving gains. Therefore, as universities advance energy conservation and carbon reduction efforts, they should incorporate climate change factors into building operation decisions. This includes rationally controlling air conditioning strategies and mitigating summer heat load growth through greening and shading measures\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e, thereby preventing a rebound in energy consumption and carbon emissions due to climatic factors.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting Interests Statement\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the the Hubei Provincial Science and Technology Program (Grant No.2025BCB033).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003e1.Xuejun Lv：Responsible for the main writing of this article, the conducting of experiments, and the processing and analysis of data.2.Keping Sun: Provided data and project support for this research.3.Hui Zhang: The corresponding author of this study provided support for the logical organization, writing guidance, and review of this research.4.Wei Chen: It also provided writing guidance and assistance with data analysis for this research.5.Yiquan Zou: Provided data and project support for this research.6.Lei Yang: Provided data and project support for this research.7.Zhengwei Wang: Assisted the first author in completing the data processing work.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data in this article were all obtained through government open channels and can provide the original data. This link leads to the page where you can search for the keyword *Wuhan Statistical Yearbook* on the Wuhan Municipal Bureau of Statistics website. Through this link, you can obtain the relevant data for the period from 2009 to 2022 in this study. The link is as follows:[https://tjj.wuhan.gov.cn/SITE/whs\\_70/search.html?searchWord=%E6%AD%A6%E6%B1%89%E7%BB%9F%E8%AE%A1%E5%B9%B4%E9%89%B4\u0026amp;amp;siteId=58\u0026amp;amp;pageSize=10\u0026amp;amp;orderBy=all](https:/tjj.wuhan.gov.cn/SITE/whs_70/search.html?searchWord=%E6%AD%A6%E6%B1%89%E7%BB%9F%E8%AE%A1%E5%B9%B4%E9%89%B4\u0026amp;siteId=58\u0026amp;pageSize=10\u0026amp;orderBy=all)\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eJiang, P., Sonne, C. \u0026amp; You, S. 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Through multi-scenario simulation across extended time horizons, the model captures the dynamic feedback mechanisms among decarbonized energy structures, building envelope improvements, renewable energy adoption, and behavioral responses. Results show that reducing the carbon emission factor of the power grid can decrease operational carbon emissions by 19.4%, while improving energy intensity through technological optimization yields a 10% reduction in energy consumption. When these strategies are implemented in combination, a 36.4% reduction in emissions and a 19.3% decline in energy use can be achieved, highlighting the cumulative effect of integrated interventions over time. The study reveals how interactive and time-sensitive variables respond to long-term climate stressors, offering a system-level understanding of operational carbon dynamics in public buildings. Furthermore, the model provides insights into how adaptive strategies evolve under delayed policy impacts and resource limitations. This research not only contributes methodological depth to scenario-based modeling for climate-responsive building management but also delivers data-driven support and strategic guidance for integrating energy-saving practices into both the design and operational stages of green buildings, reinforcing the alignment between building performance optimization and sustainable development goals.\u003c/p\u003e","manuscriptTitle":"Research on Time-Dynamic Operation Characterization of Energy Efficiency and Decarbonation of Green Building based on SD","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-13 07:32:16","doi":"10.21203/rs.3.rs-8748115/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-17T07:02:35+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"234655557000543917608897812722058886152","date":"2026-02-15T21:24:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-15T04:40:53+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-10T13:35:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"111900765392291308489381076518610179630","date":"2026-02-10T10:45:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"285656859562432737415754107110474115086","date":"2026-02-09T16:09:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"53671215514548347044999591856770209344","date":"2026-02-08T16:15:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-07T18:31:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"339854541511536521356251164855652181129","date":"2026-02-07T15:55:18+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-07T15:52:53+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-07T15:50:42+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-05T17:39:18+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-04T06:13:48+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-02-04T06:02:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"991f5b36-f62f-441b-8fae-0846e9212271","owner":[],"postedDate":"February 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":62689960,"name":"Physical sciences/Energy science and technology"},{"id":62689962,"name":"Physical sciences/Engineering"},{"id":62689964,"name":"Earth and environmental sciences/Environmental sciences"},{"id":62689966,"name":"Earth and environmental sciences/Environmental social sciences"}],"tags":[],"updatedAt":"2026-04-23T09:08:26+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-13 07:32:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8748115","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8748115","identity":"rs-8748115","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}