BIM-Enabled Life-Cycle Energy Management in Commercial Complexes: A Case Study of Zhongjian Plaza Under the Dual-Carbon Strategy

Abstract

Commercial complexes, as major sources of urban energy consumption and carbon emissions, face urgent demands for efficiency improvement under the “dual-carbon” strategy. This paper develops a Building Information Modeling (BIM)-enabled life-cycle energy management framework to address fragmented monitoring, weak coordination, and data silos inherent in traditional approaches. Methodologically, a structured literature review was conducted to identify inefficiencies and draw lessons from global practices. An enhanced Delphi method was then applied to refine 12 key evaluation indicators spanning six dimensions—policy, economic, social, technological, environmental, and compliance—which were subsequently integrated into a BIM platform. This integration enables real-time energy monitoring, multi-system diagnostics, and cross-phase collaboration across the design, construction, and operation stages. An empirical case study of the Zhongjian Plaza project in Shanghai demonstrates that the proposed framework not only enhances energy efficiency and reduces life-cycle costs, but also improves user comfort while aligning with both domestic green building standards and international sustainability targets. Overall, the study provides a replicable methodology and practical reference for the smart and low-carbon operation of large-scale commercial complexes, thereby offering strategic insights for advancing sustainable urban development.

1. Introduction

Global climate change and rising energy consumption are exerting unprecedented pressures on urban sustainability [1,2,3]. The Paris Agreement of 2015 set ambitious temperature-control goals that have become a key driver of energy transition across multiple sectors, including the built environment. According to the 2024–2025 Global Status of Construction report, the construction industry remains a major driver of the climate crisis, consuming 32% of global energy and contributing 34% of global CO₂ emissions. The industry is dependent on materials like cement and steel that are responsible for 18% of global emissions and are a major source of construction waste.

In China, rapid urbanization amplifies both the scale and speed of energy demand [4,5,6]. Commercial complexes, as dominant units of urban energy consumption, face distinct challenges. Their three-dimensional spatial layouts produce uneven heating and cooling loads; overlapping operational schedules intensify peak demand; and heterogeneous tenant behaviors create regulatory “blind spots.” Moreover, structural disconnections persist between design simulations and operational data, between system operations and maintenance, and between monitoring and decision-making. These fragmented practices reinforce a cycle of high investment but low efficiency [7].

China’s dual-carbon objectives—aiming to peak carbon emissions by 2030 and achieve carbon neutrality by 2060—have established a guiding framework for energy management within the building sector. Megacities such as Shanghai illustrate the dual challenge of aligning with national carbon reduction commitments while managing the complex energy dynamics of commercial complexes. Addressing this issue requires localized analysis of energy characteristics, technological adaptability, and management bottlenecks, alongside the integration of digital tools for precision control. Such efforts not only contribute to the carbon-neutral transition of the building sector but also enrich the broader discourse on sustainable urban governance and climate-resilient city development.

This paper investigates the application of BIM technology for life-cycle energy management in commercial complexes, addressing the limitations of traditional single-dimensional energy analysis. The study establishes a multidimensional coordination mechanism that integrates technological innovation, managerial strategies, and operational efficiency optimization. Its objectives are threefold: (1) to analyze the macro context and existing challenges of energy management in commercial complexes, demonstrating how BIM can enhance full-chain energy efficiency; (2) to identify technical, managerial, and evaluative bottlenecks through a systematic literature review and Delphi method, and construct a validated, multidimensional indicator framework for comprehensive energy management; and (3) to propose and empirically validate a BIM-based life-cycle energy management solution via case analysis, showcasing a scalable approach that supports China’s dual-carbon strategy while advancing sustainable urban development.

This study aims to overcome the limitations of traditional energy management in terms of systematization and coordination, with its innovations reflected in three aspects. First, it establishes a life-cycle evaluation framework for energy management indicators. Unlike prior studies that typically focus on either the design or operational phase, this framework explicitly spans the entire building life cycle, bridging the gap between fragmented approaches. Second, through a Delphi-based validation process with an interdisciplinary expert panel, 27 preliminary indicators are refined into 12 rigorously validated key indicators, enhancing both academic rigor and practical relevance. Third, by applying the framework to a real-world case of Zhongjian Plaza, the study demonstrates how BIM, when integrated with Internet of Things (IoT) and intelligent management platforms, can improve operational efficiency, optimize equipment performance, and reduce carbon emissions. Collectively, these contributions highlight a multidimensional, practice-oriented pathway that positions BIM not only as a design support tool but also as a pivotal driver of digital and low-carbon building operations, addressing the gap between conceptual BIM applications in large commercial complexes and data-driven operational decision support.

This paper is structured as follows. Section 2 reviews literature on energy management in commercial complexes and BIM applications for energy optimization. Section 3 analyzes energy consumption characteristics and evolution trends, highlighting key influencing factors. Section 4 presents the research methodology, employing the Delphi method to identify critical evaluation indicators for full life-cycle energy management. Section 5 demonstrates BIM-enabled energy management in the Zhongjian Plaza project across design, construction, and operation phases, including lighting optimization and system-level control. Finally, Section 6 summarizes the main findings, discusses implications for sustainable urban development, and outlines future research directions for BIM-driven energy management in commercial complexes.

2. Literature Review

2.1. Research on Building Energy Consumption

Energy management is a critical component of sustainable urban development [8]. Its goal is to improve energy efficiency [9], reduce carbon emissions, and promote resource recycling through technological innovation, policy guidance, and systematic optimization [10]. As an essential sector of urban energy consumption, commercial complexes have significant impacts on enterprise operational costs, urban environment, and residents’ quality of life [11].

The evolution of building energy management can be conceptualized through distinct developmental stages, each characterized by core concepts and supporting technologies, as shown in

Table 1. Evolution of Theories and Practices in Energy Consumption.

Period	Core Concept	Representative Theories & Technologies	Representative Organization & Date
1970s–1990s	Energy efficiency priority	Building energy codes; HVAC system optimization	ASHRAE (1980)
Early 2000s–2010	Lifecycle management	BIM applications; Green building certification (LEED)	USGBC (2000)
Since the 1950s	Low-carbon & smart transition	Smart grids; Renewable energy integration; Big data monitoring	USGBC (2000)
2010–present	Net-zero & resilience-oriented	Net-zero energy buildings; Energy system resilience planning	IPCC (2021)

.

This evolution reflects a paradigm shift in which, driven by digital technologies, building energy and carbon management has moved from a passive, component-level focus to an active, systematic approach spanning the entire building life cycle.

In China, rapid urbanization has accelerated the total building stock, making energy consumption a prominent challenge in achieving the “dual carbon” goals [12]. Buildings constitute a key battlefield for energy transition [13], but challenges remain, including: high carbon emissions during material production and insufficient penetration of green materials; high costs and low coverage of retrofitting technologies for existing buildings; significant differences in local energy efficiency standards; and underdeveloped cross-departmental regulatory mechanisms. Currently, building energy management is recognized as a national strategic priority. Policy development has evolved from “Four Savings and One Environmental Protection (i.e., saving water, saving electricity, saving fuel, saving materials, and environmental protection)” standards toward the dual-carbon objectives, and practical implementation has displayed phased characteristics, as summarized in

Table 2. Development Stages of Building Energy Consumption Management in China and Representative Cases.

Period	Policy Orientation	Typical Case	Features & Outcomes
2006–2010	Mandatory energy-saving standards	«Public Building Energy Efficiency Design Standard»	Established building energy benchmarks; enforced mandatory energy-saving design
2010–2015	Green building promotion	Shanghai Tower	USGBC (LEED Platinum certification, integrated photovoltaic and smart temperature control systems)
2016–2020	Low-carbon city pilot	Xiong’an New Area Zero-Carbon Community	Ground-source heat pumps; distributed energy coverage
2021–present	Dual-carbon target-driven	Beijing Sub-center	Building-integrated photovoltaics (BIPV); full lifecycle carbon footprint monitoring

.

Globally, energy management has received considerable attention [14]. Developed countries have adopted diversified energy management approaches through policy regulation and technological innovation. For commercial buildings, energy efficiency improvements are vital for achieving national emission reduction targets and promoting sustainable urban development. In developed regions such as Europe and North America, commercial complex energy management emphasizes not only efficiency enhancement but also smart energy system retrofits and integration of renewable energy. Cutting-edge technologies are employed for real-time monitoring and precise control, combined with green building principles and advanced control systems, moving energy management toward an intelligent and refined stage.

2.2. Energy Management in Commercial Complexes

China’s 14th Five-Year Plan and the national targets of carbon peaking by 2030 and carbon neutrality by 2060 provide a strong policy framework for energy management in commercial complexes. Key initiatives, such as the Green and Efficient Cooling Action Plan, set specific efficiency improvement targets, including a 30% enhancement in cooling energy performance for large commercial buildings by 2030. These policies have catalyzed a shift in research and practice from isolated technological improvements to comprehensive, life-cycle oriented energy management strategies [15,16,17].

Technological innovation has been central to energy management in commercial complexes. BIM facilitates dynamic energy simulation and design optimization across the entire building lifecycle [18]. Big data analytics enables accurate load forecasting by analyzing historical consumption patterns, supporting predictive and data-driven energy management. IoT technologies allow real-time integration between energy supply and demand systems, yielding measurable reductions in HVAC and overall energy consumption. Smart management platforms have emerged as key enablers, integrating multi-source data for planning, design, construction, operation, and maintenance, resolving common challenges such as fragmented information, delayed feedback, and cross-stakeholder coordination difficulties [19]. Collectively, these technologies enable precise scheduling, operational efficiency, and progress toward low-carbon objectives [20].

Effective energy management extends beyond technology, requiring coordinated governance and operational strategies. Life-cycle collaboration is essential to translate design-phase green initiatives into operational energy savings [21]. Market-oriented mechanisms are increasingly recognized as necessary complements to policy-driven mandates, aligning incentives across stakeholders and ensuring sustained performance. Digital platforms facilitate multi-stakeholder engagement, enabling dynamic monitoring, real-time adjustment, and systematic performance evaluation, thereby supporting efficient and accountable energy management [22].

Despite these advances, several challenges persist. First, traditional energy systems and emerging digital technologies remain partially siloed, with heterogeneous data standardization still underdeveloped. Second, predictive models often lack robustness in responding to extreme events and climatic variability. Third, coordination mechanisms among multiple stakeholders are underdeveloped, resulting in unclear responsibilities between designers, operators, and end users. According to the IEA report Integrating Distributed Energy Resources in China, current technologies are capable of realizing only 60–70% of their theoretical energy-saving potential, highlighting the need for innovative management approaches.

International research on energy management of commercial complexes started early and has formed a relatively mature theoretical system and practical framework. At present, the world has realized the importance of new technologies in solving the shortcomings of traditional energy management and has combined them with project energy management [23]. The current research trend shows three major characteristics: (1) methodological innovation, with machine learning enhancing predictive modeling for energy consumption; (2) technological integration, exemplified by digital twin approaches linking physical and information spaces; and (3) management innovation, focusing on multi-stakeholder collaboration and market-based incentive structures. Future research should further explore interoperability across heterogeneous systems, quantify uncertainty factors, and design effective incentive mechanisms, thereby addressing the systemic challenge of aligning technology, governance, and policy for sustainable energy management in commercial complexes.

2.3. BIM Applications in Building Energy Management

With the intensification of the global energy crisis and the widespread adoption of sustainability principles, energy consumption management in the building sector has become a central focus of research and practice [24]. BIM, as a digital tool, integrates data across the entire lifecycle of buildings and provides accurate analysis and optimization strategies for energy management [25,26,27]. During the design phase, BIM enables dynamic energy simulation and optimization, allowing multi-scenario comparisons of building planning, system configuration, and equipment selection, thereby enhancing energy-saving potential. In the operation and maintenance phase, BIM combined with Facilities Management (FM) platforms supports real-time energy monitoring, data analysis, and performance evaluation, facilitating energy scheduling, fault prediction, and operational optimization to improve both energy efficiency and economic performance [28,29].

The application of BIM in energy management has evolved toward integration with other advanced technologies [30]. Coupled with the IoT, sensor networks, cloud computing, big data analytics, and Artificial Intelligence (AI), BIM enables real-time data acquisition, energy consumption forecasting, and intelligent control, advancing refined management of building energy systems [31,32,33]. The adoption of digital twin technologies further strengthens this process by mapping physical spaces to virtual models, allowing dynamic simulation and optimization of energy use, and promoting collaborative and intelligent lifecycle management [34,35,36]. At the same time, BIM enhances data integration and sharing across all project stages [37], providing technical support for multi-stakeholder collaboration and addressing persistent challenges such as data silos and information asymmetry in traditional energy management [38,39,40].

In addition to its environmental benefits, BIM also generates substantial economic advantages. Comprehensive digital management reduces construction and operational costs, optimizes resource allocation, and enhances overall building performance [41,42]. Policy support has played a critical role in BIM adoption, as mandatory implementation in public projects across several countries has driven technological diffusion and practical application, thereby strengthening institutional frameworks for energy management [43,44,45].

In summary, existing studies on building energy management highlight significant progress in technological innovation and the adoption of intelligent systems [46,47]. Traditional energy-saving strategies have evolved toward data-driven, adaptive approaches through the integration of IoT, AI, and big data analytics, while BIM has emerged as a powerful tool for lifecycle-wide information integration and collaborative decision-making [48]. Nevertheless, several gaps remain in existing research. On the one hand, while different aspects of commercial complex energy management have been explored, systematic and holistic studies from a full life-cycle perspective are still insufficient [49], and collaborative mechanisms across various stages require further improvement. On the other hand, although BIM applications in energy management have made notable progress, their large-scale implementation continues to face both technical and non-technical challenges, such as data security, workforce training, and high upfront investment. Moreover, long-term evaluation and dynamic optimization of BIM-based energy management systems remain relatively underexplored.

3. Analysis of Energy Management in Commercial Complexes

3.1. Evolution of Energy Consumption Structure in Commercial Complexes

According to the Energy Efficiency Technology and Economic Assessment Report on Commercial Buildings 2023 by the China Energy Conservation Association and the Annual Report on the Application of Building Energy Efficiency Technologies by the Ministry of Housing and Urban-Rural Development, the energy consumption structure of commercial complexes underwent profound changes between 2015 and 2023, reflecting the combined impact of technological innovation and policy guidance (Figure 1). The data reveal a transition from heavy reliance on carbon-intensive energy to a cleaner and more diversified system. Prior to 2005, electricity and purchased heat dominated while renewables were almost absent. Between 2005 and 2015, natural gas and renewable energy gradually entered the mix, electricity use further increased, and reliance on purchased heat declined, marking a shift from a single to a diversified pattern. Since 2015, renewables have expanded significantly, purchased heat has sharply decreased, and distributed and smart energy systems have emerged alongside electricity’s dominant role, resulting in an increasingly low-carbon, diversified, and resilient energy system.

3.2. Determinants of Energy Consumption in Commercial Complexes

The energy consumption of commercial complexes is inherently diverse and dynamic, shaped by the interplay of building characteristics, equipment performance, user behavior, and operational management. Building attributes such as orientation, form, and window configuration determine daylighting and thermal performance, where effective design can leverage natural conditions but requires careful cost-benefit trade-offs. According to statistics published by the China Association of Building Energy Efficiency in 2025, air-conditioning systems dominate energy consumption in commercial complexes, accounting for 40–60% of total energy use. Variations in efficiency, operating patterns, and maintenance conditions can result in annual electricity intensity differences of up to twofold between different complexes. User behavior further drives fluctuations, as tenant types, business hours, and visitor density generate differentiated energy demands: restaurants and entertainment venues are more energy intensive than retail or office spaces, while peak hours see surges in elevator, lighting, and cooling loads compared with off-peak periods. Meanwhile, advanced technologies such as high-efficiency chillers, heat pumps, and intelligent lighting and temperature control systems are increasingly adopted to enhance proactive and fine-grained energy management.

3.3. Challenges in Commercial Complex Energy Management

Energy management in commercial complex projects faces significant challenges across the design, construction, and operation phases [50]. During the design stage, energy simulations often suffer from inaccuracies, BIM coordination frequently fails, and load predictions deviate from actual conditions, resulting in systemic errors. In the construction phase, management is weak: green materials are prone to performance degradation during storage and transport, equipment is often incorrectly sized, and substandard construction practices further increase energy consumption [51]. In the operational phase, low system integration, delayed response, and poor data quality hinder effective energy management, as erroneous sensor data can trigger improper equipment control, leading to frequent energy waste and accelerated equipment aging [52]. These operational realities reveal the systemic inadequacies of traditional energy management, which further manifest as weak life-cycle coordination, fragmented technical and managerial processes, structural gaps in professional staffing, low information integration in operations, and deficiencies in energy performance evaluation. Specifically, development phases often target LEED or Green Building star ratings as the endpoint, neglecting continuous life-cycle energy control [53]; construction rarely implements dynamic energy management, and oversight of materials, equipment, and processes is inconsistent. Operational management suffers from limited data sharing, delayed optimization, and skill or incentive imbalances among professional teams. Existing evaluation indicators are overly simplistic, failing to capture energy consumption relative to operational performance. Collectively, these stage-specific and systemic issues underscore the urgent need for integrated design-construction-operation management, underpinned by data-driven strategies and qualified personnel, to improve energy efficiency and support sustainable urban development.

4. Research Design and Methodology

Against the backdrop of digital transformation and sustainable development in the construction industry, this study employs the Delphi method, building on a comprehensive literature review, to select evaluation indicators for assessing the application of BIM in energy management of commercial complexes. As a structured expert consensus technique, the Delphi process involves multiple rounds of questionnaires with controlled feedback, enabling convergence toward stable results. Unlike approaches that rely solely on literature synthesis, this method integrates expert practice and policy considerations, ensuring that the selected indicators are not only scientifically rigorous but also practically feasible. Consequently, the resulting framework provides a reliable foundation for subsequent quantitative analysis and informed decision-making.

4.1. Identification of Preliminary Indicators

Building upon the systematic literature review and preliminary expert consultations, we developed an initial evaluation framework consisting of 27 indicators. This process ensured that the framework was grounded in both theoretical rigor and practical relevance. Specifically, the inclusion of indicators was guided by two principles: (i) their recurrence in prior scholarly and policy-oriented studies on building energy management, and (ii) their recognition by practitioners as essential for reflecting the operational, economic, and environmental dimensions of BIM-enabled management. To further validate their relevance, each indicator was cross-checked with feedback from domain experts. The column “Mentioned in Expert Interviews” (Yes) in

Table 3. Preliminary Summary of Energy Management Evaluation Indicators for Commercial Complexes.

No.	Indicator Name	Mentioned in Expert Interviews	References
R01	Policy Compliance	YES	[54]
R02	Energy Policy Responsiveness	YES	[32]
R03	Government Regulation and Rating	YES	[55]
R04	BIM Policy Integration	YES	[56]
R05	Project Cost Control	YES	[57]
R06	Equipment Operational Efficiency	YES	[58]
R07	Energy-saving Investment Return	YES	[59]
R08	Market Competitiveness	YES	[60]
R09	BIM Cost-effectiveness	YES	[61]
R10	User Comfort and Satisfaction	YES	[62]
R11	Fulfillment of Social Responsibility	YES	[63]
R12	Employee Participation	YES	[64]
R13	BIM Public Participation	YES	[65]
R14	BIM Model Depth	YES	[66]
R15	BIM Collaboration Capability	YES	[67]
R16	BIM Operation and Maintenance Support	YES	[68]
R17	Application of Energy-saving Technologies	YES	[69]
R18	Data Analytics Capability	YES	[70]
R19	Technological Maturity	YES	[71]
R20	Ease of Operation and Maintenance	YES	[72]
R21	Carbon Emission Management	YES	[73]
R22	Resource Recycling and Utilization	YES	[74]
R23	Ecological Restoration Capacity	YES	[75]
R24	BIM-based Ecological Simulation	YES	[76]
R25	Compliance	YES	[77]
R26	Standard Implementation	YES	[78]
R27	Contract Energy Management	YES	[79]

indicates that all 27 indicators were explicitly confirmed as pertinent during the preliminary round of expert consultation. This dual validation approach strengthened the reliability of the framework and provided a robust foundation for subsequent Delphi iterations and quantitative analyses.

4.2. Questionnaire Survey and Data Collection

Based on the preliminary compilation of evaluation indicators, this study designed a questionnaire to assess the application of BIM in energy management for commercial complexes. The survey targeted practitioners and relevant personnel involved in energy management or similar practices, including representatives from local government, public institutions, project developers, consulting firms, universities and research institutes, on-site management and construction teams, as well as social groups and community organizations within the project scope. The questionnaire was distributed via the “Wenjuanxing” platform, with 48 copies sent and 36 valid responses collected after excluding incomplete or invalid submissions, yielding a response rate of 75%. The survey content was developed based on the evaluation indicators initially identified through literature review and employed a five-point Likert scale to quantify the importance and effectiveness of each indicator, ensuring the reliability and comparability of the collected data (see Appendix A for the questionnaire).

The survey respondents included personnel from government, enterprises, consulting firms, universities, and social organizations. Consulting firms (23.33%) and similar enterprises (20%) represented the largest groups, indicating that the sample was dominated by technically and consultatively experienced professionals. Both professional and work experience were mostly concentrated in the 6–10-year range, with mid-career participants predominating. Overall, respondents had moderate practical experience, providing sufficient expertise while not being overly biased toward highly senior experts, thus offering relatively objective and actionable insights (

Table 4. Analysis of the Characteristics of Questionnaire Respondents.

Profession	Local Government/Public Sector	This Project Developer	Similar Project Developer	Consulting Firm	University Experts	Social Organizations	Public
No. of Participants	3	3	6	7	5	5	2
Percentage	10.00%	6.67%	20.00%	23.33%	16.67%	16.67%	6.67%
Years of Professional Experience	1–5	6–10	11–15	>15
No. of Participants	6	14	6	4
Percentage	17.91%	47.76%	22.39%	1.49%
Years of Work Experience	1–5	6–10	11–15	>15	20–25	>25
Number of Participants	6	14	6	3	1	—
Percentage	20.00%	46.67%	20.00%	10.00%	3.33%	—

).

A total of 36 questionnaires were analyzed, and the statistical summary of the indicators is presented in

Table 5. Mean Values and Measures of Dispersion of Questionnaire Data.

Evaluation Indicator	Mean	Variance	Standard Deviation	Coefficient of Variation (CV)	Evaluation Indicator	Mean	Variance	Standard Deviation	Coefficient of Variation (CV)
R01	3.89	0.79	0.89	0.23	R15	3.53	0.88	0.94	0.27
R02	2.89	0.79	0.89	0.31	R16	3.72	0.83	0.91	0.25
R03	3.81	0.73	0.86	0.22	R17	3.17	0.71	0.85	0.27
R04	3.17	0.31	0.56	0.18	R18	3.78	0.75	0.87	0.23
R05	3.25	0.76	0.87	0.27	R19	3.67	0.86	0.93	0.25
R06	3.47	0.83	0.91	0.26	R20	3.39	0.53	0.73	0.21
R07	3.25	1.16	1.08	0.33	R21	3.94	0.97	0.98	0.25
R08	2.67	1.37	1.17	0.44	R22	2.94	0.91	0.95	0.32
R09	3.44	0.94	0.97	0.28	R23	3.00	0.69	0.83	0.28
R10	3.39	0.59	0.77	0.23	R24	3.53	0.48	0.70	0.20
R11	2.81	0.62	0.79	0.28	R25	3.06	0.97	0.98	0.32
R12	2.81	1.36	1.17	0.42	R26	3.14	0.69	0.83	0.27
R13	2.56	1.05	1.03	0.40	R27	3.28	0.89	0.94	0.29
R14	3.25	0.42	0.65	0.20

.

The data were further analyzed, and the top ten indicators ranked by mean, variance, and coefficient of variation are presented in

Table 6. Bottom 15 Indicators by Mean, Variance, and Coefficient of Variation.

Bottom 15 Indicators	Mean		Variance		Coefficient of Variation (CV)
No.	Evaluation Indicator	Value	Evaluation Indicator	Value	Evaluation Indicator	Value
13	R27	3.28	R01	0.79	R26	0.27
14	R14	3.25	R02	0.79	R15	0.27
15	R05	3.25	R06	0.83	R17	0.27
16	R07	3.25	R16	0.83	R05	0.27
17	R04	3.17	R19	0.86	R23	0.28
18	R17	3.17	R15	0.88	R11	0.28
19	R26	3.14	R27	0.89	R09	0.28
20	R25	3.06	R22	0.91	R27	0.29
21	R23	3.00	R09	0.94	R02	0.31
22	R22	2.94	R21	0.97	R25	0.32
23	R02	2.89	R25	0.97	R22	0.32
24	R11	2.81	R13	1.05	R07	0.33
25	R12	2.81	R07	1.16	R13	0.40
26	R08	2.67	R12	1.36	R12	0.42
27	R13	2.56	R08	1.37	R08	0.44

.

Among the 27 evaluated indicators, the mean scores of R27, R14, R05, R07, R04, R17, R26, R25, R23, R22, R02, R11, R12, R08, and R13 were all below 3.30, indicating that these factors, while still relevant, were relatively less important. Further assessment of their dispersion showed that R07, R02, R22, R25, R13, R12, and R08 ranked among the lowest 15 in terms of variance, with correspondingly low coefficients of variation. Economic and social indicators are generally considered indirect or lagging measures of energy management performance; they are difficult to quantify during early BIM applications and have limited direct causal relationships with energy savings. Consequently, due to redundancy and low perceived impact, these seven indicators (R07, R02, R22, R13, R25, R12, and R08) were removed from subsequent analyses. This outcome was largely anticipated by the research team and aligns with the objectives of the Delphi method.

4.3. Expert Interviews and Final Indicator Selection

For the remaining eight indicators among the lowest fifteen in terms of variance, five experts with diverse backgrounds in BIM, sustainable building, energy management, and digital transformation were invited for follow-up interviews. Their expertise ensured that these indicators were both theoretically rigorous and practically relevant across the entire building life cycle. Detailed information on the experts is provided in

Table 7. Detailed Information about Experts.

No.	Position	Area of Expertise	Key Contribution
Expert 1	Senior Engineer/Committee Member	BIM and Intelligent Building	Proposed an “Intelligent Construction Maturity Model”, highlighting the integration of BIM with IoT, AI, and Blockchain
Expert 2	Professor/Chief Executive Officer (CEO)	BIM and Green Building Energy Management	Developed a BIM-based Energy Management Platform, emphasizing technological maturity and data integration
Expert 3	Chief Technology Officer (CTO)	BIM-Based Smart Operation & Maintenance (O&M)	Introduced the concept of “O&M Convenience”, emphasizing visualization, mobile interfaces, and automated reporting
Expert 4	Professor	Equipment Energy Optimization & System Stability	Proposed evaluation metrics for “Equipment Energy Balance” and “System Stability”
Expert 5	Vice President/Head of Digital Transformation	Full-Cycle Digitalization & Smart Property Management Platform	Emphasized management-technology synergy, proposing integration pathways of Digital Twin and AI

, and the indicator results are summarized in

Table 8. Expert Interview Records.

Evaluation Indicator	Expert 1	Expert 2	Expert 3	Expert 4	Expert 5	Conclusion
R04	Reflected in BIM Operation and Maintenance Support	Reflected in BIM Operation and Maintenance Support	Delete	Delete	Reflected in BIM Operation and Maintenance Support	Delete
R05	Reflected in Equipment Operational Efficiency	Reflected in Equipment Operational Efficiency	Delete	Delete	Reflected in Equipment Operational Efficiency	Delete
R11	Generally No Impact	Delete	Generally No Impact	Not Retained	Generally No Impact	Delete
R14	Reflected in BIM Cost-effective-ness	Delete	Reflected in BIM Cost-effectiveness	Not Retained	Reflected in BIM Cost-effectiveness	Delete
R17	Reflected in Technological Maturity	Reflected in Technological Maturity	Reflected in Technological Maturity	Not Retained	Delete	Delete
R23	Technology Relatively Mature	Delete	Generally No Impact on Project Implementation	Generally No Impact on Project Implementation	Technology Relatively Mature	Delete
R26	Reflected in Policy Compliance	Delete	Delete	Reflected in Policy Compliance	Delete	Delete
R27	Reflected in Equipment Operational Efficiency	Delete	Delete	Not Retained	Delete	Delete

.

In summary, several of the initially identified energy consumption management indicators—R02, R04, R05, R07, R08, R11, R12, R13, R14, R17, R22, R23, R25, R26, and R27—were determined not to serve as key indicators in commercial complex projects. Based on the aforementioned analysis, the selected energy consumption management indicators for commercial complexes are summarized in

Table 9. Screening Results of Energy Management Indicators for Commercial Complexes.

No.	Indicator Name	Indicator Description
R01	Policy Compliance	- Mandatory Requirements of Local Policies for BIM Application (e.g., BIM stipulations in land transfer contracts).
R02	Government Regulation and Rating	- Energy Audit Results (e.g., per-unit area energy consumption vs. industry benchmarks); - Green Building Certification Level (e.g., LEED Gold, China 3-Star); - Key Energy-Consuming Unit Supervision (listed or not).
R03	Equipment Operational Efficiency	- Key Equipment Load Rate (e.g., HVAC main units operating in 60–80% efficiency range); - System Stability (e.g., ≤2 unplanned shutdowns per year); - Equipment Efficiency Degradation Rate (≤3% annual performance decline).
R04	BIM Cost-effectiveness	- BIM Life-Cycle Cost Savings Rate (e.g., reduced design changes, lower O&M inspection costs); - BIM Model Reuse Rate (proportion of standardized components used across projects).
R05	User Comfort and Satisfaction	- Indoor Environmental Quality (compliance with temperature, humidity, air quality, and illuminance standards); - Tenant/Consumer Acceptance of Energy-saving Measures (e.g., smart temperature control, lighting adjustment); - Tenant Complaint Rate; - Illuminance Uniformity in Public Areas.
R06	BIM Collaboration Capability	- Multi-discipline Clash Detection Resolution Rate (≥90% conflict reduction); - Real-time Data Interaction with IoT Devices (≤5 s latency); - BIM-GIS Integration Level (spatial positioning accuracy ≤ 0.1 m)
R07	BIM Operation and Maintenance Support	- BIM-based Emergency Plan Simulation Coverage (e.g., optimized fire evacuation routes); - Historical Energy Data Traceability; - BIM Visualization Report Generation Efficiency.
R08	Data Analytics Capability	- Data Collection Frequency (e.g., minute-level real-time monitoring); - Predictive Algorithm Accuracy (e.g., load forecast error ≤ 10%); - Multi-source Data Integration Capability (e.g., BIM + GIS + IoT).
R09	Technological Maturity	- Domestic Equipment Rate (e.g., autonomy of core control systems); - Technical Failure Rate (e.g., ≤1 sensor failure per year); - Technical Scalability (supports upgrades over the next 5 years).
R10	Ease of Operation and Maintenance	- Remote Fault Diagnosis Response Time (≤2 h); - User-friendliness of Visualization Interface (e.g., 90% functions accessible in one click); - Maintenance Knowledge Base Update Frequency (≥1 update per quarter).
R11	Carbon Emission Management	- Carbon Emission per Unit Area (kgCO2/m2·year); - Carbon Reduction Measures (e.g., renewable energy substitution, carbon offsetting).
R12	BIM-based Ecological Simulation	- BIM Environmental Performance Simulation Capability (e.g., annual solar radiation analysis error ≤ 5%); - BIM-based Dynamic Calculation of Green Carbon Sequestration (calibrated with sensor data).

, with a total of 12 key indicators identified. Building on this foundation, BIM technology enables the systematic integration of these indicators into a comprehensive digital information model spanning the entire building lifecycle, thereby fundamentally transforming energy management practices. The model employs the PESTEL framework (Political, Economic, Social, Technological, Environmental, and Legal), a robust tool for macro-environmental analysis. This approach was adopted to move beyond purely technical BIM evaluations and to systematically account for multiple internal and external factors affecting energy performance in commercial complexes. Within this model, BIM serves as the central digital enabler, coordinating and supporting management across all six PESTEL dimensions, with detailed categorization provided in Appendix A.

The applicability and criticality of the twelve key indicators vary across different stages of the project life cycle. Indicators such as R01, R06, and R12 exert the greatest influence during the design phase, guiding preliminary planning, simulation, and interdisciplinary coordination. The construction phase primarily relies on indicators such as R03 and the traceability functions of R11 for critical assessments. During the operational phase, indicators including R05, R07, R08, and R10, along with performance tracking of R03 and R11, are essential for continuous performance optimization and cost control. Recognizing the stage-specific importance of each indicator enables project teams to prioritize tasks and BIM functionalities in alignment with the project phase.

The integration of BIM technology directly enhances the effectiveness of these indicators by providing a centralized data environment and advanced analytical capabilities. For instance, R01 is strengthened through automated compliance checks against building codes, while R6 is supported via real-time clash detection on a cloud-based platform. For R07, BIM models connected to IoT sensors enable real-time visualization of equipment status and energy flows, facilitating predictive maintenance. Historical and real-time operational data are consolidated within BIM to support machine learning analyses, thereby enhancing R08. R12 depends directly on BIM’s geometric and semantic data to perform accurate environmental performance assessments. Overall, BIM functions both as a comprehensive data integrator and a visualization and analysis engine, enabling the systematic application of these indicators throughout the entire building life cycle.

5. Case Study of BIM-Based Energy Management in Zhongjian Plaza

5.1. Case Study Context and Overview: Zhongjian Plaza

The case study selected for this research is the Zhongjian Plaza project, located at 899 Gaoke West Road in Zhoujiadu Community, Pudong New District, Shanghai. The project lies within the post-Expo radiation zone and represents the hot summer and cold winter climate region, which is characterized by high energy demand fluctuations. Functionally, it is positioned as a mixed-use development comprising Grade-A office space and a large-scale commercial complex. The project covers a land area of 16,573.7 m², with a total gross floor area of 75,968 m², including 50,413 m² above ground and 25,555 m² underground. The two underground floors accommodate parking, mechanical equipment, and ancillary facilities, while the above-ground portion consists of three towers: Tower A (17 floors, 24,860 m²), Tower B (10 floors, 11,600 m²), and Tower C (4 floors partially, 13,953 m²), which integrates retail and dining functions. Its scale and multifunctional configuration make it highly representative of the energy consumption and management challenges faced by contemporary commercial complexes in China. The effect diagram is shown in Figure 2.

In the planning and construction phases, the project strictly adhered to Shanghai’s Green Design Standard for Public Buildings (DGJ08-2143-2014) and the Green Building Evaluation Standard (DG/TJ08-2090-2012) [80], both at the two-star level. Additionally, it achieved the U.S. LEED Platinum certification, signifying its advanced position within the domestic green building sector and its alignment with international benchmarks. Independently developed and operated by a state-owned enterprise, Zhongjian Plaza emphasizes low-carbon construction and operation, leveraging BIM to establish an integrated energy management system for refined monitoring, real-time control, and performance optimization.

The project was selected as the case study for four reasons: (i) its climatic context enables analysis of energy performance under variable seasonal demands; (ii) its dual compliance with domestic and international green standards demonstrates technical advancement; (iii) systematic BIM deployment across design, construction, and operation provides empirical support for life-cycle-based energy management [81]; and (iv) as a state-owned enterprise-led benchmark, it offers replicable strategies for low-carbon transformation in similar commercial complexes.

5.2. Application of BIM in Whole-Life-Cycle Energy Management

In Zhongjian Plaza, BIM functioned as the core technological enabler for life-cycle energy management. An owner-led model, coordinated by a third-party consulting firm, defined stakeholder responsibilities from the bidding stage and required a “master model” mechanism to improve modeling efficiency and support operation and maintenance. Leveraging the C8BIM collaborative platform, independently developed by China State Construction Engineering Eighth Bureau, cross-organizational management integrated design, construction, consulting, and operation teams, with standardized data protocols and version control ensuring seamless information transfer across all project stages.

During design, BIM’s 3D visualization and parametric capabilities, combined with green building simulation tools, facilitated iterative multi-scheme optimization of ventilation, daylighting, and envelope performance, effectively reducing baseline energy consumption. In construction, IoT devices captured real-time energy data, while a traceable green material supply chain enabled refined monitoring, minimizing waste. In operation, a BIM-FM integrated platform established a closed-loop framework combining strategy, organization, spatial layout, and equipment management, enabling “data collection–intelligent analysis–optimized execution” and supporting intelligent, efficient facility management.

Two key aspects highlight BIM’s lifecycle role: (i) model refinement ensured efficient data flow and platform compatibility; phased modeling incorporated detailed parameters for high-energy-consuming systems (HVAC, lighting, elevators), with cross-checked and optimized data enabling rapid loading and smooth platform application (Figure 3); (ii) intelligent energy management was achieved through deep BIM–operations platform integration, enabling real-time data collection, big data analytics, and AI-driven strategies for optimized equipment scheduling, dynamic load adjustment, and staggered operations, significantly improving energy efficiency.

5.2.1. Application of BIM in Energy Management During the Design Phase

In the building life cycle, the design stage is critical for achieving energy management and sustainability objectives, as decisions made during this phase directly affect long-term energy consumption patterns and construction costs. In this project, BIM was integrated with national and international green building standards to guide design-phase optimization, focusing on site ecology, microclimatic performance, indoor natural ventilation, and daylighting. These measures aim to minimize operational energy demand, enhance occupant comfort, and align building performance with urban sustainability goals.

In the design phase, site ecological and environmental simulations played a critical role in shaping long-term energy performance. BIM, integrated with domestic and international green building standards, guided the optimization of site ecology, microclimatic performance, indoor natural ventilation, and daylighting (visualized model shown in Appendix B.1, Figure A1).

• Ecological and wind simulations informed building layout and form adjustments to enhance airflow and occupant comfort while minimizing energy demand. BIM models were developed for key functional spaces, with parametric optimization applied to building orientation, window placement, and internal layouts. Natural ventilation analyses, combining BIM with CFD, simulated air change rates across floors, ensuring effective cross-ventilation and identifying areas for design improvement (detailed CFD parameters and wind speed data are provided in Appendix B.2).
• Daylighting simulations were conducted using BIM-derived geometry and material properties, imported into Radiance-based tools to evaluate illuminance distribution. Optimization measures included window adjustments and interior layout refinements to achieve the required daylight factors while reducing artificial lighting demand (models and daylighting statistics in Appendix B.3). Collectively, these design-phase strategies ensured a scientifically grounded, energy-efficient, and low-carbon interior environment.

5.2.2. Application of BIM in Energy Management During the Construction Phase

In the full life cycle of construction projects, the construction phase is a critical period for energy consumption and carbon emissions, especially in complex commercial complexes. Advanced BIM applications can substantially enhance energy management by establishing a BIM-based construction energy management system, enabling real-time collection of static and dynamic site data via smart sensors and IoT technologies. The system comprises two subsystems: (i) smart-site energy and environmental monitoring, and (ii) full-chain traceability of green building materials. The monitoring subsystem collects multi-source production and environmental data, including air quality, noise, temperature, humidity, and equipment energy usage, facilitating visualized, automated, and fine-grained energy management (monitoring tool schematics are shown in Appendix C.1). The traceability subsystem links component-level carbon IDs with IoT sensors and blockchain technology, ensuring authentic, end-to-end carbon tracking and dynamic emissions quantification (monitoring devices are illustrated in Appendix C.2). Real-time integration of BIM with these subsystems enables precise energy and carbon management during construction, minimizing waste and ensuring compliance with low-carbon objectives.

5.2.3. Application of BIM in Energy Management During the Operation and Maintenance Phase

In the operation and maintenance (O&M) phase of commercial complexes, energy management faces three interrelated challenges: heterogeneous business formats, conflicting stakeholder demands, and systemic complexity. These manifest as spatial and temporal heterogeneity of energy consumption across offices, retail, and catering spaces, creating tension between energy supply and demand, and requiring trade-offs among owners’ cost control, tenants’ comfort, and governmental carbon reduction requirements. Conventional management approaches often fail due to high coupling among subsystems such as HVAC, lighting, and vertical transportation.

The CSCEC Plaza project addresses these challenges through a systemic approach guided by the principle of “technology empowering management, organizational mechanisms ensuring implementation”. At the technological level, a BIM-enabled digital twin platform integrates IoT sensing, three-dimensional simulation, and cloud-based applications to map physical and informational spaces. At the managerial level, a PDCA (Plan-Do-Check-Act) closed-loop mechanism supports real-time monitoring, intelligent analytics, strategy optimization, and continuous improvement. At the organizational level, a matrix governance framework promotes horizontal cross-departmental collaboration and vertical talent development, supported by standardized institutional processes.

A BIM-driven smart O&M platform was established to overcome traditional facility management fragmentation, implementing an integrated workflow of “data collection → analysis and decision-making → execution and control”. Spatially, correlation analyses among regions, equipment, and energy use enabled optimized start-stop strategies for high-demand systems. Temporally, minute-level monitoring combined with long-term trend analysis facilitated pattern recognition and short-term forecasting. Operationally, a standardized work order system enabled end-to-end digital tracking from early warning to task closure, significantly improving cross-departmental coordination. Key features of management process reengineering included: (i) a three-tier early warning mechanism automatically triggering responses from system self-recovery to expert consultation (

Table 10. Three-level Early Warning System Design.

Warning Level	Trigger Condition	Response Mechanism
Level 1	Data deviation from baseline 10%	System Automatically Adjusts Equipment Operating Parameters
Level 2	Deviation 20% sustained for 2 h	On-site Inspection and Reporting by Maintenance Personnel
Level 3	Deviation 30% or critical equipment failure	Initiate Expert Consultation and Emergency Plan

); (ii) a unified coding standard for seamless multi-source data integration; and (iii) a closed-loop PDCA framework supported by a visualized dashboard for informed decision-making.

5.3. BIM-Enabled Intelligent Lighting and Operational Energy Optimization

To achieve real-time and precise energy management, the project implemented a refined sensor-based monitoring system across key energy-consuming subsystems, forming a dynamic, life-cycle-spanning monitoring network. Using the lighting system of Office Building B as a case study, the original manual control mode led to concentrated daytime electricity use and inefficiencies such as lights remaining on after occupancy. In response, the project employed BIM technology to establish an intelligent, scenario-based, and adaptive management framework, integrating spatial information with real-time energy data to optimize lighting according to differentiated demand.

• Diagnostic assessment. The lack of automated control led to unnecessary energy waste, exacerbated by deep building layouts limiting natural light. Office areas experienced over-bright conditions during lunch (12% user complaints), commercial zones consumed excessively due to absent zoning, and public areas such as underground parking suffered from “always-on” lighting (30% inefficient hours).
• Optimization strategies. The smart O&M platform, based on a BIM LOD400 model, integrated lighting system parameters with real-time energy consumption data, forming a three-dimensional framework covering temporal, spatial, and behavioral aspects. Temporally, lighting was divided into four control periods per the official work calendar; spatially, illuminance requirements were mapped to zone functions following GB50034-2013 standards [80]; behaviorally, the system architecture combined perception, network, platform, and terminal layers, with sensors capturing occupancy and lighting data and transmitting it to the cloud. Occupancy-based algorithms linked BIM space IDs with IoT sensors, implementing “lights on when people enter, lights off when they leave”, enabling precise, adaptive, and real-time lighting control (detailed data are provided in Appendix D,

  Table A3. Time Period Division of Lighting Control.

  Time Period	Illuminance Standard	Control Method	Description
  Business Hours	100%	Time-based + Illuminance Sensor	BIM system synchronizes office access control data via API
  Lunch Break	60%	Scene Switching	BIM model automatically switches to “Energy-saving Mode” based on space usage, turning off non-essential lighting
  Non-business Hours	30%	Motion Sensor	Integrated with security system, only emergency lighting (30% brightness) is maintained, with inspection alerts sent to maintenance personnel via BIM mobile interface
  Emergency Period	100%	Forced On	Integrated with security system, relevant alerts are sent to maintenance personnel via BIM mobile interface

  ,

  Table A4. Lighting Control Time Periods.

  Space Type	Functional Requirement	Illuminance Standard (lx)	Description
  Office Area	Administrative Work	300–500	BIM system automatically calculates lighting demand based on workstation distribution and dims artificial lighting when natural illuminance exceeds 300 lx
  Commercial Area	Merchandise Display	500–750	Lighting layout is optimized by setting reflectance parameters for different usage types in the BIM material library
  Corridor	Pedestrian Safety	100–150	-
  Parking Lot	Vehicle Traffic	75–100	-

  and

  Table A5. Lighting Modes.

  Scenario Mode	Lighting Requirement
  Business Hours	Primary and auxiliary lighting on, illuminance at 100% of preset value
  Lunch Break	Primary lighting on, auxiliary lighting off, illuminance at 60% of preset value
  Non-business Hours	Primary and auxiliary lighting on, illuminance at 30% of preset value
  Emergency Period	All lighting off; infrared sensors automatically activate lighting in occupied

  ).
• Implementation path. Scenario-based and time-adaptive control schemes were applied: office zones used real-time illuminance sensors to complement daylight, ensuring standard compliance while minimizing waste, and underground parking employed a timer plus infrared occupancy detection strategy, brightening lights when users were present and dimming after departure (detailed data are provided in Appendix D,

  Table A6. Lighting Optimization Strategies for Underground Parking.

  Mode		Location	Strategy
  Daytime	Monday–Friday 07:30–18:00	Drive Lane	When occupants/vehicles are detected, lighting increases to 100% brightness, dimmed to 50% after 10 s, then to 20% after 5 s; sensing range 20 m
  		Parking Space	When occupants/vehicles are detected, lighting increases to 100% brightness, dimmed to 50% after 30 s, then to 10% after 5 s; sensing range 5 m
  Off-hours	Monday–Friday 18:00–07:30 (next day) Public Holidays (All Day)	Drive Lane	When occupants/vehicles are detected, lighting increases to 70% brightness, dimmed to 50% after 5 s, then to 5% after 5 s; sensing range 20 m
  		Parking Space	When occupants/vehicles are detected, lighting increases to 70% brightness, dimmed to 50% after 15 s, then to 0% after 5 s; sensing range 5 m

  ).
• Evaluation of benefits. Post-implementation, energy consumption matched occupancy patterns: office peaks occurred in the morning, dropped at lunch, and tapered after 18:00; underground parking energy use aligned with traffic peaks and fell near zero after 21:00. Compared with manual control, nighttime lighting was avoided, achieving measurable energy savings (detailed data are provided in Appendix D,

  Table A7. Energy Consumption Optimization Comparison.

  Area	Energy Consumption Under Original Strategy (kWh)	Energy Consumption Under Optimized Strategy (kWh)	Energy Saving Rate (%)
  Office Area, 10th Floor	77.45	62.16	19.74
  Parking Lot, B1	71.42	1113.37	81.28

  ).

From a technical standpoint, BIM-based mapping of spatial IDs to device addresses enabled precise “room-luminaire-sensor” associations, reducing fault location time from 30 min to 5 min and improving work order efficiency by 80%. Moreover, the system supported cross-platform functionality, whereby users could access lighting status and controls via computer, smartphone, or table. Consequently, the project not only demonstrated significant energy-saving and operational benefits but also exemplified how BIM-driven integration of digital twins and IoT sensing can institutionalize fine-grained, adaptive, and intelligent lighting management in large-scale commercial complexes.

Beyond energy reduction, BIM dismantled interdisciplinary information barriers, established a multi-stakeholder digital twin environment for collaborative decision-making, and supported a shift from experience-based to data-driven management through dynamic resource allocation and integrated cost-schedule control. Additionally, the project generated a reusable construction management database, offering a reference for similar projects. Through deep integration of management, technology, and data, the Zhongjian Tower project has become a benchmark for green and intelligent construction, earning multiple national and municipal BIM pilot awards, and establishing a scalable, low-carbon digital construction methodology that demonstrates the pioneering role of state-owned enterprises in advancing smart construction and sustainable urban development.

6. Discussion and Conclusions

This study develops and empirically validates a BIM-based life-cycle energy management framework for commercial complexes. The results indicate that, under the proposed indicator framework, BIM integration effectively addresses the challenges of data fragmentation and poor cross-phase coordination inherent in traditional approaches. By enabling digital collaboration, the framework bridges data silos across the entire building life cycle, enhancing traceability and visual control of energy flows, thereby substantially improving operational efficiency. Multi-system dynamic simulations provide quantitative support for the coordinated optimization of key energy-consuming systems, while integrated life-cycle data streams enhance equipment maintenance responsiveness and minimize energy waste. The case study of Zhongjian Plaza demonstrates that BIM-driven life-cycle energy management, leveraging digital twins, energy simulation, and intelligent decision-making, systematically improves energy efficiency, reducing energy consumption by 17–21%—exceeding the national 15% target—and enhancing environmental comfort, thereby offering a replicable methodology for intelligent, low-carbon operation of large commercial complexes.

The core contribution of this study lies in systematically advancing the application of BIM in life-cycle energy management. First, the validated indicator framework demonstrates that energy management cannot be effectively assessed using a single standard approach, as often assumed in previous literature. Instead, a combination of technical factors (e.g., equipment performance, data analytics capabilities), policy alignment (e.g., compliance, standard implementation), and managerial considerations (e.g., operational and maintenance ease, contract energy management) provides a more comprehensive and operationally relevant tool. Moreover, the case study highlights that BIM’s role extends beyond design-phase energy simulation to serve as a central data integration platform, enabling real-time monitoring, predictive maintenance, and performance benchmarking. This represents a transition from BIM as a static modeling tool to BIM as an active energy management platform, consistent with recent advancements in AI- and IoT-enabled building operations. Finally, the study offers new empirical insights into the implementation of China’s dual-carbon policy at the building level, providing evidence that BIM-supported platforms can translate abstract policy targets into measurable performance outcomes. The linkage between macro-level policy objectives and micro-level building operations is rarely addressed in existing research, representing a unique contribution to both scholarship and practice.

Despite these advances, challenges such as incomplete data integration and limited technical coordination remain. Future efforts should focus on deeper integration of BIM with analytical and optimization tools to enhance predictive capabilities, support automated optimization, and improve decision-making, enabling more precise and responsive energy management. Additionally, multi-scenario applications should be expanded to address diverse climates and building regulations, including optimized building envelopes and HVAC systems, BIM-based models for commercial complexes, and reverse-engineered approaches for retrofit evaluation.