Digital Intelligence Transformation of Energy Conservation Management in China’s Public Institutions: Evolution, Innovation Approach, and Practical Challenges

Abstract

Energy conservation management in public institutions is a critical area of administrative affairs, playing a leading and exemplary role in implementing China’s “green development strategy” and accelerating the transition to green and low-carbon development. The evolution of energy conservation management in public institutions has generally progressed from behavioral energy conservation and policy-driven energy conservation to digital and intelligent energy conservation. Each stage is characterized by distinct conceptual foundations, tool selections, key tasks, and value orientations. From a theoretical perspective, the innovative practices of digital intelligence transformation in energy conservation management are deeply driven in China by problem solving, environmental factors, and technological advancements. This transformation is the result of the interplay between the broader context of digital government construction and the specific challenges and structural adjustments within energy conservation management in public institutions, combined with the strong momentum of innovation diffusion in energy conservation management informatization. The innovative practices of digital intelligence transformation in energy conservation management in China can be categorized into four models: the “Technology Demonstration + Digital Platform” model, the “Edge–Cloud Data Middle Platform” model, the “Operation + Platform” split front–back-end model, and the “Intelligent Function Aggregation Platform” model. Each model has unique functional characteristics and applicable scenarios, yet faces various inherent challenges. Currently, the digital intelligence transformation of energy conservation management in China’s public institutions is constrained by the tension between innovation pressure and limited grassroots resources, the diminishing marginal returns and internalization costs of digital intelligence transformation, the inverted hierarchy dilemma, and the “floor effect” of digital energy conservation under traditional governance norms.

1. Introduction

Energy conservation management in public institutions is a critical domain of administrative affairs, serving as a leading exemplar in implementing China’s “green development strategy”, advancing carbon peaking and neutrality goals, and accelerating the comprehensive green transformation of socio-economic development [1]. The digital intelligence transformation of energy conservation management is integral to modernizing national governance systems and capabilities, providing essential support for the green and low-carbon transition of public institutions. In recent years, the central government has issued policy documents such as the 14th Five-Year Plan for Energy Conservation in Public Institutions, Implementation Plan for Green and Low-Carbon Leadership Actions in Public Institutions to Promote Carbon Peaking, and Three-Year Action Plan for Enhancing Statistical Data Governance of Energy Consumption in Public Institutions (2023–2025). These policies explicitly advocate leveraging next-generation technologies—including big data, the Internet of Things (IoT), and artificial intelligence (AI)—to integrate “digital intelligence + public institution energy conservation” across all domains, processes, and elements of energy conservation management. Nationally standardized frameworks, such as Guidelines for Implementing Energy Conservation Management Systems in Public Institutions, Technical Requirements for Communication Interfaces in Energy Conservation Optimization Control, and General Technical Specifications for Energy Consumption Monitoring Systems in Public Institutions, further establish systemic and technical benchmarks for this transformation [2].

Energy Conservation Management in Public Institutions refers to a systematic approach targeting administrative departments and energy-consuming units at all levels, with the dual objectives of enhancing energy governance capacity for management entities and improving operational efficiency for end-users. This framework is built upon modern ICT infrastructure (e.g., communication networks, database technologies) to implement China’s strategic policies and strengthen its data-driven governance capabilities. It aims to support evidence-based decision-making while elevating energy conservation practices through intelligent, user-centric, and digital solutions. As an integrated system encompassing all domains, processes, and elements of institutional energy conservation management, it yields comprehensive outcomes including both policy implementations (e.g., regulatory frameworks) and technological achievements (e.g., smart energy systems) [2]. Driven by both problem-solving imperatives and policy momentum, provinces such as Zhejiang, Jiangsu, and Jiangxi have pioneered the integration of digital intelligence into energy conservation management. Their efforts focus on enhancing energy efficiency and resource utilization through a combination of technological empowerment and institutional restructuring. Innovations span energy metering and statistics, quota implementation, management of key energy-consuming units, control of critical energy-intensive sectors, energy efficiency diagnostics, and performance evaluation, resulting in diverse new mechanisms, models, and policies [3]. Building upon this foundation, the present study adopted a micro-level exploratory approach to comprehensively examine the evolutionary trajectory of energy conservation management models in China’s public institutions and their innovative pathways toward digital intelligence transformation. Through a systematic literature review, field investigations, and a summary of practical experience, this research aimed to: (1) delineate the developmental patterns of institutional energy conservation mechanisms; (2) reveal current implementation challenges; and (3) propose recommendations for sustainable advancement.

2. Literature Review

2.1. International Practices

As a core strategy for achieving carbon neutrality, the digital intelligence transformation of energy conservation in public institutions has developed differentiated innovation pathways across nations based on institutional traditions and technological endowments. These transformation characteristics can be categorized into four major paradigms: policy driven, technology innovation, market oriented, and net-zero pioneering approaches, with breakthrough progress achieved through the synergy of policy instruments, digital technologies, and market mechanisms.

2.1.1. Policy-Driven Approaches: Legislative Rigidity Empowers Systemic Transformation

Countries like Japan and Germany have established mandatory energy conservation frameworks through iterative legislation. Japan’s Energy Conservation Act requires public institutions exceeding 1500 KL of crude oil equivalent in annual energy consumption to submit digital intelligence transformation plans, enforced through the SABC rating system (S-grade for benchmark enterprises, B/C-grade under government supervision) [4]. Shizuoka Prefecture reduced energy use by 23% after retrofitting 127 government buildings via an energy monitoring platform, while Yokohama City Hall achieved an 18% reduction through AI-optimized lighting [5]. Germany’s Building Energy Act (GEG) mandates new public buildings to meet EH55 standards (≤35 kWh/m²·a) with compulsory photovoltaic installations, supported by Energy Performance Contracting (ESCO) financing—exemplified by a 31% energy reduction in Berlin’s Federal Environment Agency retrofit [6]. These nations combined legislative mandates with economic incentives to revolutionize energy conservation in public institutions.

2.1.2. Technology Innovation Paradigms: Digital Solutions Reshape Governance

South Korea and Singapore leverage technological convergence to overcome efficiency barriers. Korea’s Energy Management Information System reduced cooling energy use by 18.7% in Seoul City Hall through machine learning [7], while its Low-Carbon Green Growth Act integrated public institutions into the carbon market Korea Emissions Trading Scheme (K-ETS), driving LED retrofits (e.g., 41% lighting energy savings in Gwangju City Hall) [8]. Singapore’s block chain-based “Carbon Ledger” platform enables cross-institutional carbon quota tracking, and AI-optimized cooling at Gardens by the Bay cut energy use by 19% [9]. With 87% rooftop solar coverage in public institutions and a 1.5 GWP target by 2030 [10], Singapore exemplifies sustainable innovation. The UK’s cloud-based platform in Manchester boosted regional energy efficiency by 37% via federated learning algorithms [11], while London’s mandatory energy certificates and smart meter adoption (85% coverage) reduced building energy use by 19% [12]. These cases highlight data governance for precision energy conservation management.

2.1.3. Market-Oriented Mechanisms: Carbon Pricing Drives Transformation

The U.S. and Australia employ market tools to accelerate technology adoption. U.S. federal agencies mandate ENERGY STAR^® products, with Washington D.C. achieving 19% peak load reduction via Open Automated Demand Response (OpenADR), while NASA’s cloud-based diagnostics improved equipment efficiency by 27% [13]. Australia’s National Australian Built Environment Rating System (NABERS) dynamically adjusts benchmarks using machine learning (reducing Sydney Opera House data deviations from 22% to 6.3%), and its “Safeguard Mechanism” requires high-emission public institutions to trade carbon credits (e.g., Melbourne’s cross-state quota exchange via Safeguard Mechanism Credits (SMCs) [14]. Transparent governance and price signals underpin their smart energy transition.

2.1.4. Zero Carbon Pioneering: Life-Cycle Carbon Neutrality

France and Dubai are pioneering integrated decarbonization strategies. Paris deployed 280,000 smart streetlights (70% energy savings) and digital twins to cut emissions by 45% in La Défense’s Heating, Ventilation, and Air Conditioning (HVAC) systems [15]. Total Energies’ Energy Performance Contracting retrofits achieved 20% average savings [16]. Dubai mandates LEED Platinum certification for new public buildings, with Al Wasl Plaza’s 90% Building Integrated Photovoltaics (BIPV) coverage meeting 50% of energy demand [17]. Their high-standard certification and technology integration mark a shift from energy management to carbon asset governance.

These paradigms demonstrate how policy–technology–market synergies promote the digital intelligence transformation of energy conservation in public institutions, offering sustainable models for global carbon neutrality.

2.2. Chinese Practices

Academic research on the digital intelligence transformation of energy conservation in Chinese public institutions primarily focuses on three aspects: digital intelligent management approaches, technological applications, and institutional frameworks. Hu dagula et al. (2019) examined the relationship between low-carbon economy development and energy conservation in public institutions, proposing pathways for digital intelligent transformation in energy management [18]. Wang Xingdi et al. (2021) identified systemic issues in Energy Performance Contracting implementation, including ambiguous institutional mechanisms and insufficient policy incentives [19]. Zhu Xiaojiao et al. (2019) highlighted the significant social influence of public institutions as publicly funded service providers [20]. In technological applications, Zhang Luzheng and Wu Yan (2023) proposed integrated energy–carbon management systems to enhance operational efficiency, suggesting the reallocation of fiscal resources from conventional monitoring systems to support IoT based solutions [21]. Yang Xiaoyang (2019) developed the innovative “4 + 2 Model” for public institutions, featuring standardized indicator systems, green institution certification goals, and intelligent energy monitoring platforms [22]. Regarding institutional frameworks, Wang Yue et al. (2020) advocated for establishing unified management systems with cross-departmental coordination mechanisms, complemented by market-oriented approaches through Energy Performance Contracting [23].

Regional implementations have demonstrated measurable success through coordinated policy and technological interventions. At the policy level, the “14th Five-Year Plan for Energy Conservation in Public Institutions” established a comprehensive “10 + 3 + 4” framework, while local governments like Shandong Province implemented quantifiable targets, including 7% reductions in carbon intensity [24]. Technologically advanced solutions leveraging IoT, block chain, and 5G networks have been widely adopted, exemplified by Jiangsu Province’s integrated monitoring platform enabling real-time energy dispatch and Beijing University’s achievement of sustained efficiency improvements through distributed photovoltaic systems [25]. Practical outcomes include Hubei Province’s 30% energy savings through city-wide Energy Performance Contracting projects, Jiangxi Province’s carbon inclusive platform engaging 1.86 million participants to reduce emissions by 197,000 tons, and annual electricity cost savings exceeding one million yuan in multiple provinces through energy trusteeship models [26].

3. Evolution of the Energy Conservation Management Model

3.1. Evolution of the Energy Conservation Management Model for Public Institutions

From the perspective of the evolution of energy conservation models, the energy conservation management of public institutions can generally be divided into four modes: Behavioral energy conservation, policy-driven energy conservation, digital energy conservation, and intelligent energy conservation. Each mode has different essential meanings, tool choices, key tasks, and value orientations as shown in

Table 1. Comparison of energy conservation management modes for public institutions.

Energy Conservation Mode	Behavioral Energy Conservation	Policy-Driven Energy Conservation	Digital Energy Conservation	Intelligent Energy Conservation
Essential meanings	Literacy cultivation	Behavioral norms	Agile monitoring	Smart regulation
Tool choices	Publicity, initiatives, education, identification, learning	Planning, performance evaluation, supervision, law enforcement, authority and responsibility	Sensor technology + digital platforms	Sensor technology + intelligent technology
Key tasks	Forming energy conservation awareness, mastering energy conservation knowledge, guiding energy conservation behavior	Restraining energy conservation behavior, shaping energy conservation environment, standardizing energy conservation authority and responsibility	Operation monitoring, energy measurement, diagnosis and analysis	Frequency conversion management, emergency disposal, smart decision-making
Value orientations	Conceptual guidance	Institutional constraints	Management precision	Smart control

Source: Created by the authors.

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Behavioral energy conservation is the initial mode of energy conservation management for public institutions, with its essence lying in the cultivation of energy conservation literacy among public institutions and relevant personnel. Through methods such as energy conservation propaganda, green initiatives, graphic symbols, education, and learning, it guides public institutions to engage in green procurement, green office work, energy conservation renovations, and the use of new energy sources. It also encourages staff and employees to practice green office work, green travel, green consumption, and green public welfare. The core tasks are to establish energy conservation awareness, master energy conservation knowledge, and guide energy conservation behavior. For example, using events like “Energy Conservation Promotion Week” and “National Low Carbon Day”, and through channels like notice boards, websites, and official accounts, various forms of energy conservation promotional activities such as knowledge dissemination, policy lectures, and knowledge competitions are conducted. The goal is to incorporate energy conservation concepts and requirements into daily work, striving to create a positive situation where everyone cares about and participates in energy conservation efforts.

Policy-driven energy conservation is the deepened stage of energy conservation management for public institutions, with its essence lying in using institutional norms to correct and regulate the energy conservation behavior of public institutions and relevant personnel. Through means such as issuing energy conservation planning documents, action plans, work records, performance evaluation methods, assessment indicators, and supervision systems, it shapes a healthy and orderly energy conservation environment, further constraining the energy conservation behavior of public institutions. For example, in terms of planning, public institutions establish a series of energy conservation and consumption-reduction work systems and documents, such as the “Energy Conservation Implementation Plan”, the “Energy Conservation Work Performance Evaluation Target System”, and the “High-Energy Consumption Equipment Energy Conservation management System”. These documents clarify the annual energy consumption targets, energy conservation plans, and energy conservation goals for departments or sections, stipulate standardized operational processes for equipment maintenance and management, and incorporate energy conservation requirements into institutional policies [27]. In terms of authority and supervision, public institutions establish energy conservation work leading groups to enhance coordination and oversight, ensuring the decomposition and implementation of various goals and tasks, reinforcing responsibility awareness, and guaranteeing the effective implementation of policies, measures, and outcomes. In terms of performance evaluation and enforcement, public institutions improve their comprehensive energy consumption evaluation and performance systems, establish detailed records of energy-consuming equipment and facilities, and maintain complete operational and inspection logs for energy systems. They enforce strict supervision and evaluations, rewarding departments or sections with outstanding energy conservation achievements in accordance with regulations, and imposing performance penalties on those that fail to meet targets. Planning, performance evaluation, supervision, and other tools have the advantages of being highly targeted and constrained. They can rely on a well-established energy conservation institutional framework to strengthen energy conservation operational management and ensure the standardization and normalization of energy conservation procedures.

Digital energy conservation is an expanded model of energy conservation management empowered by digital technology tools in public institutions. It utilizes digital technologies to collect, organize, store, transmit, and apply various aspects of energy consumption information. This system provides data support for target management, threshold management, risk management, performance management, and benchmarking management in energy conservation for public institutions. Its core lies in enhancing the precision of energy conservation management through digitalized operation monitoring, energy metering, and diagnostic analysis.

The implementation of digital energy conservation is based on four subsystems: information collection, energy monitoring, energy metering, and energy regulation. Specifically, this includes an information collection system based on quantitative collection, an energy metering system centered on clear computation, an energy monitoring system aimed at refined management, and an energy regulation system supported by digital decision making. In operation monitoring, sensors and IoT technologies can collect energy consumption data in real time, providing a more accurate understanding of energy usage. In energy metering, energy efficiency and conservation are treated as quantifiable, assessable, and manageable “resources”. These can be rapidly, automatically, and accurately monitored and controlled through energy meters such as those measuring pressure, flow, temperature, and time, offering reliable guidance for energy conservation efforts in public institutions. In diagnostic analysis, the digital energy conservation platform analyzes and evaluates the collected data and information, identifying inefficiencies in energy use, revealing issues such as equipment aging, outdated technology, or poor management, and providing a basis for the development of energy conservation measures and recommendations. In target management, energy consumption ledgers are established on the energy conservation digital platform, comparing energy consumption figures in different regions with prescribed consumption targets, supporting the implementation of energy conservation assessments and revisions to annual energy consumption goals.

Intelligent energy conservation integrates intelligent technologies into the entire life cycle of energy conservation management in public institutions, adopting an innovative model of automation and intelligent decision making and operations. It emphasizes the reliance on artificial intelligence, machine learning, and big data analysis, enabling energy conservation systems to perceive, analyze, decide, and execute autonomously [28]. Using an intelligent energy conservation platform, energy device parameters are adjusted autonomously in response to environmental changes to accomplish complex energy conservation configuration tasks. The system can automatically regulate equipment such as temperature and lighting based on the operational functions of public institutions. The core idea is to achieve intelligent control through smart frequency conversion management, emergency response, and intelligent decision making, thus minimizing the pressure of energy conservation management and optimizing the efficiency of energy conservation efforts. To provide early warnings about risks, the intelligent energy conservation system predicts, analyzes, and warns about equipment operational trends and maintenance needs based on historical and real-time data, ensuring efficient energy use [29]. In frequency conversion management, the intelligent energy conservation system uses frequency conversion technology for precise control, achieving smooth startup and speed regulation of energy-consuming devices, reducing energy consumption, extending equipment lifespan, and lowering maintenance costs. In emergency responses, the intelligent energy conservation system monitors and warns of abnormal energy consumption or emergencies, and based on emergency cutoff standards, automatically optimizes energy distribution to improve the reliability and stability of the energy supply. In intelligent decision making, the system can swiftly capture the energy usage patterns of public institutions, reducing decision-making biases through real-time monitoring of energy consumption, data analysis, alerts, and automatic generation of energy conservation management instructions.

3.2. Evolution of the Energy Conservation Management Model for Theoretical Identification

The innovative practice of the digital-intelligence transformation in public institution energy conservation management is deeply driven by issues, environmental changes, and technological advancements. The digital intelligence transformation in public institution energy conservation emerges from three interconnected factors: (1) the macro-level “digital government” policy environment, (2) the resulting structural adjustments in energy management systems, and (3) the rapid diffusion of information technology innovations. Traditional approaches—“behavioral energy conservation” and “policy-driven energy conservation”—reveal fundamental limitations through process constraints in implementation, disjointed authority–responsibility frameworks and unclear performance metrics. These challenges are compounded by hierarchical structural barriers, value realization ceilings and performance measurement paradoxes. This transformation specifically addresses the overreliance on standalone technological solutions by developing an integrated framework that strategically synchronizes energy management systems with digital transformation architectures. The transformation strategy involves “collaborative supplementation of centralized platforms, scientific supplementation of metered management, and precise integration of intelligent methods”, and the design of institutional policies that promote “differentiated incentives, centralized allocation of responsibilities and rights, and contextual exploration of practices”. The path for the digital intelligence transformation of energy conservation management is guided by clear logical lines, utilizing technological solutions, resources, and authority as leverage to enable functional and process adjustments across energy collection, calculation, management, and decision making. The effects of this transformation exhibit leverage, multiplier, and cross-network effects.

4. Innovation Approach to the Digital-Intelligence Transformation of Energy Conservation Management in Public Institutions

This study employed a cross-regional field research methodology, conducting a seven-month empirical investigation starting January 2024 across Shanghai, Jiangsu, Sichuan, Beijing, Jiangxi, and Tianjin. The research team systematically examined energy conservation management in local public institutions through interviews, on-site observations, and document analysis, focusing on three key dimensions: first, assessing institutional innovations and technological applications in energy transition across different regions; second, deconstructing and summarizing innovative practice models in energy conservation management; and third, identifying challenges in the digital intelligence transformation of public institutions’ energy conservation systems. Through comparative analysis of multi-regional findings, this study aimed to develop regionally adaptable energy conservation models and provide theoretical support for advancing digital intelligence transformation in public institutions’ energy management as shown in

Table 2. Specific interview list.

Survey Date	Location	Interviewees	Format	Discussion Topics
10 January 2024	Shanghai Lingang “Hydrogen + Thermal Storage” Integrated Energy Base	Project managers, staff	On-site interview	Daily operations, energy conservation measures and outcomes
11 January 2024	Shanghai Municipal Government Offices Administration	Party Leadership Group members, Deputy Directors, Third-Level Investigators	On-site discussion	Progress, specific measures and results of energy conservation in Shanghai’s public institutions
15 March 2024	Suzhou Municipal Government Offices Administration, Jiangsu	Party Leadership Group members, Deputy Directors, Third-Level Investigators	On-site discussion	Progress, specific measures and results of energy conservation in Suzhou’s public institutions
17 March 2024	Wuxi Municipal Government Offices Administration, Jiangsu	Party Leadership Group members, Deputy Directors, Third-Level Investigators	On-site discussion	Progress, specific measures and results of energy conservation in Wuxi’s public institutions
19 March 2024	Changzhou Municipal Government Offices Administration, Jiangsu	Party Leadership Group members, Deputy Directors, Third-Level Investigators	On-site discussion	Progress, specific measures and results of energy conservation in Changzhou’s public institutions
21 March 2024	Nanjing Municipal Government Offices Administration, Jiangsu	Party Leadership Group members, Deputy Directors, Third-Level Investigators	On-site discussion	Progress, specific measures and results of energy conservation in Nanjing’s public institutions
20 April 2024	Beijing Municipal Government Offices Administration	Party Leadership Group members, Deputy Directors, Third-Level Investigators	On-site discussion	Progress, specific measures and results of energy conservation in Beijing’s public institutions
22 April 2024	Tianjin Municipal Government Offices Administration	Party Leadership Group members, Deputy Directors, Third-Level Investigators	On-site discussion	Progress, specific measures and results of energy conservation in Tianjin’s public institutions
19 June 2024	Zigong Municipal Government Offices Administration, Sichuan	Party Leadership Group members, Deputy Directors, Third-Level Investigators	On-site discussion	Progress, specific measures and results of energy conservation in Zigong’s public institutions
20 June 2024	Zigong Third People’s Hospital, Sichuan	Logistics support staff	On-site interview	Implementation details and operational results of hospital Energy Performance Contracting project
23 June 2024	Panzhihua Municipal Government Offices Administration, Sichuan	Party Leadership Group members, Deputy Directors, Third-Level Investigators	On-site discussion	Progress, specific measures and results of energy conservation in Panzhihua’s public institutions
24 June 2024	Panzhihua Municipal Hospital, Sichuan	Logistics service center staff	On-site interview	Implementation details and operational results of hospital Energy Performance Contracting project
5 July 2024	Yibin Municipal Government Offices Administration, Sichuan	Party Leadership Group members, Deputy Directors, Third-Level Investigators	On-site discussion	Progress, specific measures and results of energy conservation in Yibin’s public institutions
10 July 2024	Chongqing Liangjiang New Area Government Offices Management Center	Deputy Directors, Section Staff	On-site discussion	Progress, specific measures and results of energy conservation in Liangjiang New Area’s public institutions
26 July 2024	Jiangxi Provincial Government Offices Administration	Party Leadership Group members, Deputy Directors, Third-Level Investigators	On-site discussion	Progress, specific measures and results of energy conservation in Jiangxi’s public institutions

Source: Systematized by the authors.

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Observations from empirical studies suggest that the innovation approach to the digital intelligence transformation of energy conservation management in public institutions can be broadly categorized into four models: the “Technology Demonstration + Digital Platform” model, the “Edge–Cloud Data Middle Platform” model, the “Operation + Platform” split front–back-end model, and the “Intelligent Function Aggregation Platform” model. Each of these models exhibits distinct functional characteristics and is suited to different scenarios, while also confronting various intractable issues, as shown in

Table 3. Four models of digital intelligence transformation in Public Institution Energy Conservation Management.

Digital and Intelligent Model	“Technology Demonstration + Digital Platform” Model	“Edge–Cloud Data Middle Platform” Model	“Operation + Platform” Split Front–Back-End Model	“Intelligent Function Aggregation Platform” Model
Essential Connotation	Innovation, demonstration, diffusion, and promotion of new energy technologies	Visual aggregation, display, analysis, and application of energy consumption data	Front-end intelligent control of energy systems coupled with back-end visual display of energy consumption data	Intelligent monitoring, analysis, decision-making, and execution of energy conservation management across the entire process
Pathological Issues	“Valley of Death” between demonstration and promotion	“Efficiency Gap” between data analysis and application	“Feasibility Funnel” in scenario-based applications	“Flexibility Paradox” in functional aggregation
Application Scenarios	Energy storage sector	Centralized office areas	Decentralized office areas	Large-scale energy-intensive institutions

Source: Created by the authors.

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4.1. “Technology Demonstration + Digital Platform” Model

The “Technology Demonstration + Digital Platform” model aims to complement or even replace traditional high-energy consumption practices through the innovative application of new energy technologies, noted for their low cost and safety performance. Its essence lies in promoting the innovative implementation, demonstration, and diffusion of new energy technologies within public institution settings. Its primary application scenarios are concentrated in energy storage facilities, such as residential energy storage systems, industrial and commercial park energy storage, communication base station energy storage, and portable power sources. In terms of transportation, the model is applicable to low-speed electric vehicles, electric bicycles, and electric vessels, as well as buses and coaches.

This model returns to the technological domain of energy conservation management, seeking to address inherent energy conservation management issues at their source. However, it also faces the “valley of death” dilemma encountered during the transition from innovative demonstration to widespread promotion. The challenge lies in that the diffusion of technological innovations often encounters significant resistance due to the lack of extensive application scenarios in public institutions and the uncertainty surrounding cost recovery cycles as shown in Figure 1.

The “Technology Demonstration + Digital Platform” model represents an integrated strategy for technology promotion. Its core value is the combination of high-performance new energy technologies with a highly operable and real-time digital management platform, aiming to achieve low-cost, high-efficiency, and safe energy usage through the dual drive of technology and information. Firstly, the application of new energy technologies serves as the technical foundation of the model. By means of distributed energy, energy storage facilities, and the utilization of renewable energy, the model can provide efficient and cost-effective alternatives for various high-energy consumption scenarios while offering substantial flexibility and scalability. Secondly, the digital platform employs intelligent management and visualization techniques to collect, analyze, and optimize energy conservation management data, thereby rendering the energy utilization process more transparent and scientific [30]. The application scenarios for the “Technology Demonstration + Digital Platform” model span several dimensions, primarily involving energy storage facilities and new energy transportation. In the energy storage domain, typical scenarios include residential energy storage systems, industrial and commercial park energy storage, communication base station energy storage, and portable power sources. The application of these energy storage facilities not only effectively addresses issues such as an insufficient and highly volatile power supply from traditional grids but also enhances the utilization rate of distributed energy and reinforces the stability and resilience of the energy system. In the realm of new energy transportation, the promotion of low-speed electric vehicles, electric bicycles, electric vessels, buses, and coaches can substantially reduce dependency on fossil fuels, effectively lower greenhouse gas emissions in urban transportation, and provide crucial technological support for achieving low-carbon mobility.

Despite the significant advantages of the “Technology Demonstration + Digital Platform” model in advancing energy conservation management and promoting the application of new energy technologies, it still faces numerous challenges in practical implementation. The most typical issue is the “valley of death” between demonstration and promotion, where the results of technological innovation often struggle to transition from laboratory or pilot projects to large-scale market applications. This phenomenon largely arises from the lack of broad support scenarios when public institutions apply new energy technologies, particularly due to insufficient customization for the energy needs of different institutions and industries. Furthermore, economic feasibility issues persist in technology promotion, as the high initial investment costs and uncertain cost recovery cycles deter some institutions from adopting new energy solutions.

Our research identified exemplary implementations of this model in technological energy storage and new energy innovation demonstrations, with Shanghai, Tianjin, Changzhou, Wuxi, and Yibin emerging as representative cases. In energy storage technology, Shanghai’s Lingang “Hydrogen + Thermal Storage” Integrated Energy Base achieves 100 MW single-unit capacity with 12 h heat storage through molten salt thermal storage coupled with green hydrogen production. This system delivers an annual carbon reduction of 120,000 tons at a levelized cost of 0.35 RMB/kWh, positioning it as the world’s first carbon-neutral industrial park. In Yibin, Sichuan Province’s inaugural 100 MWh user-side energy storage demonstration project—the Sanjiang New District Sichuan Times Commercial & Industrial User-Side Energy Storage Initiative—features four 35 kV transmission lines, each equipped with five 5 MW/10 MWh storage units. The project is projected to convert approximately 40 million kWh of renewable energy annually, equivalent to saving 5000 tons of standard coal and reducing CO₂ emissions by 52,400 tons.

Regarding new energy innovation, official data indicates that during the first year of policy pilots, 15 demonstration cities collectively deployed 434,000 new energy vehicles (NEVs) in public sectors and installed 447,000 public charging points, achieving 69% and 64% of the respective three-year targets. Tianjin leveraged the Beijing–Tianjin–Hebei coordinated development strategy to prioritize electrification in port logistics, implementing integrated “PV–storage–charging–discharging” systems alongside autonomous driving and vehicle–grid interconnection pilots, accumulating 56,500 public-sector NEVs, 34,900 charging piles, and 65 battery swap stations. Changzhou—recognized as a “New Energy Capital” with comprehensive industrial infrastructure—has deployed 46,800 NEVs and established 63,200 charging points plus 60 swap stations, with plans to advance vehicle-to-grid (V2G) bidirectional technologies. Meanwhile, Wuxi’s smart city initiatives have resulted in 28,800 NEV deployments, 16,000 charging piles, and 70 swap stations, with ongoing advancements in integrated PV storage systems, autonomous driving, and V2G applications.

4.2. “Edge–Cloud” Data Middle Platform Model

The “Edge–Cloud” data middle platform model is designed to build a data middle platform that enables the visual aggregation, display, analysis, and application of energy consumption data in public institutions. This model is primarily applied in centralized office areas of public institutions. By fully utilizing technologies such as the Internet of Things (IoT) and big data, it monitors electricity, water, and other usages in real time—categorized by institution type, time segments, and zones—thereby achieving automatic data collection, transmission, statistical analysis, and benchmarking of energy consumption. The model returns to a “data”-centered approach to energy saving in public institutions, attempting to transform energy saving targets and tasks into quantifiable data via a digital platform for refined management. However, it also faces a “performance gap” issue between data analysis and practical applications. In other words, while the digital platform can perform structured feature analysis of energy consumption data, its depth in data mining is limited, and energy saving adjustments based on analysis results often lack real constraints due to mismatches in accountability. Simply put, the digital platform empowers energy conservation management in a tool-like manner but does not achieve institutional or technical empowerment for energy saving management [31] as shown in Figure 2.

Specifically, the model is divided into four layers:

• Terminal Layer (End): IoT devices in this layer collect energy consumption data in real time and transmit them to the next layer.
• Edge Computing Layer (Edge): Here, the data are processed and analyzed locally to reduce transmission delays.
• Cloud Computing Layer (Cloud): Data are then uploaded to the cloud for in-depth analysis and intelligent optimization. Big data techniques are employed to mine the energy conservation potential and generate visual decision-support information.
• Data Application Layer: Finally, the analysis results are stored and transformed into actionable energy conservation measures and decisions, thereby driving the execution and continuous optimization of energy conservation management and performance evaluation.

This entire model forms a closed loop, achieving efficient management and dynamic optimization from data collection and processing to analysis and application. The essence of the “Edge–Cloud” data middle platform model lies in promoting the digital and intelligent upgrade of energy consumption management. In traditional energy conservation management, the collection and analysis of energy consumption data tend to be fragmented and inefficient, resulting in less precise and comprehensive control over energy usage in public institutions. In contrast, the “Edge–Cloud” model establishes a highly integrated digital platform that creates a closed loop for data collection, transmission, analysis, and application. With the aid of visual displays and intelligent analysis, it provides a more scientific and efficient method for energy conservation management. This platform not only serves as a repository for energy consumption data but also forms an important basis for energy conservation decision-making, reflecting the digital, systematic, and intelligent characteristics of energy conservation management.

From a functional structure perspective, the model is highly integrated and intelligent. At the data collection level, IoT sensors are deployed to monitor electricity, water, and other usages in centralized office areas in real time, covering various types of equipment and regions. For data transmission, edge computing technology processes the data locally before uploading it to the cloud, thereby reducing delays and network load while ensuring timeliness and accuracy. During the data analysis phase, big data techniques enable multidimensional statistical analysis of energy consumption data, including historical trend analysis, energy efficiency benchmarking, and the mining of energy conservation potential. Finally, through visualization tools, the analytical outcomes are presented intuitively, offering decision support to managers and thus enhancing the efficiency of energy conservation management in public institutions.

In terms of application scenarios, the “Edge–Cloud” data middle platform model is mainly used in high-energy consumption settings, such as centralized office areas of public institutions. In these scenarios, energy use exhibits significant clustering effects and complex consumption fluctuations, necessitating a platform that can collect data in real time, monitor dynamically, and analyze scientifically to optimize energy use. Specifically, the model can monitor energy consumption across different time periods and regions to accurately identify peak periods and high-consumption areas, thereby providing a basis for developing energy conservation measures. For example, based on energy efficiency benchmarking results, the platform can propose rectification suggestions for departments with low efficiency and optimize the energy usage structure through intelligent scheduling to promote overall energy optimization. Additionally, the model can be applied to similar contexts such as large public facilities like universities and hospitals.

Despite its significant advantages, the “Edge–Cloud” data middle platform model still faces some bottlenecks in practice, particularly regarding the effectiveness of data application. The first issue is the performance gap between data analysis and actual application. Although the platform can comprehensively collect and structure energy consumption data, converting analysis results into concrete energy conservation actions remains a critical challenge. This problem arises because the platform’s functionalities are largely limited to monitoring and analysis, lacking direct control mechanisms for implementing energy conservation measures—resulting in insufficient constraint on management behavior. The second challenge is the limited depth of data mining. While the “Edge–Cloud” platform achieves comprehensive monitoring of energy consumption data, its capabilities in deep data mining and intelligent application are still constrained. For instance, current analyses remain at the level of historical trend analysis and energy efficiency benchmarking, lacking intelligent recommendations for forecasting future energy consumption and optimizing energy conservation measures. Furthermore, the application of big data and artificial intelligence algorithms has not been fully exploited. Additionally, due to the varied energy consumption patterns and energy conservation needs of different public institutions, formulating personalized energy conservation strategies based on specific scenarios remains a major challenge in promoting this model.

In summary, while the “Edge–Cloud” model provides public institutions with a tool-based empowerment for energy conservation management—offering information support through the data platform and analytical methods to help optimize energy usage—the actual implementation of energy conservation management depends on complementary management systems and approaches. Relying solely on a data platform without policy support and institutional constraints makes it difficult to ensure the effective execution of energy conservation measures. Therefore, this model must be further enhanced through dual innovations in technology and management mechanisms to build a data-driven, intelligently controlled, and system-guaranteed energy conservation management system. Only then can the efficiency of energy conservation management in public institutions be continuously improved, providing robust support for achieving low-carbon economies and sustainable development goals.

Our field research revealed that the adoption of “Edge–Cloud” model has enabled coordinated energy conservation management at both national and regional levels. The National Government Offices Administration has spearheaded the deployment of a Comprehensive Energy and Resource Conservation Information Platform for public institutions nationwide. By the end of 2024, approximately 570,000 public institutions across all regions—including 19,000 national-level institutions—had been incorporated into this platform, with about 260,000 electricity accounts registered. The system currently automates electricity data collection for public institutions across all 31 provincial-level regions and Xinjiang Production Corporations, while also monitoring water and gas consumption for Beijing-based institutions. Through its big data visualization capabilities, the platform provides intuitive displays of energy/water consumption and carbon emissions across different regions, institution types, and administrative levels.

Jiangsu Province has emerged as a pioneering implementer of this model. Its Provincial Public Institution Energy Management Platform represents China’s first provincial-level digital intelligence supervision system for institutional energy consumption, having integrated data from all public institutions across the province. The platform has yielded remarkable conservation outcomes: Nanjing’s Xincheng Office Complex—one of the city’s three major government compounds—achieved a 10% year-on-year energy reduction after platform integration, reaching historical performance benchmarks. Guided by the platform’s diagnostic recommendations, the complex implemented upgrades to its central HVAC, power distribution, gas supply, data center, and lighting systems, achieving 15.3% comprehensive energy savings—equivalent to 3.5 million kWh annual electricity reduction and 2900 ton CO₂ emission cuts. Similarly, Suzhou reported 6.96%, 4.01%, and 6.18% reductions in per-capita energy consumption, energy intensity per floor area, and per-capita water use, respectively, in 2023 compared to 2022. Province-wide, these measures now yield annual energy savings equivalent to 80,000 tons of standard coal, reducing CO₂ emissions by 200,000 tons. Jiangsu’s successful model has been adopted as a reference for nationwide pilot programs.

4.3. “Operation + Platform” Split Front–Back-End Model

The “Operation + Platform” split front–back-end model aims to achieve centralized energy consumption control and unified visual monitoring through a front-end “energy conservation Operational System” and a back-end “Digital Display Platform”. This model is primarily applied to decentralized, single public institutional office buildings, serving as a flexible solution for digital intelligence transformation in energy conservation management for non-centralized office areas. While it aligns with the technical requirements of intelligent energy conservation management and enhances operational flexibility for independent office zones, it faces limitations in scalability—termed the “feasibility funnel” effect—where its applicability diminishes as the building scale expands or the scenario heterogeneity increases.

Figure 3 illustrates the architecture of this model. The front-end “Operational System” employs smart sensor networks to collect real-time energy data, processes it via edge computing for immediate device control (e.g., HVAC, lighting), and optimizes energy usage. Concurrently, the back end “Digital Display Platform” centralizes data storage, performs big data and AI-driven analysis, and provides a user interface for visualized energy reports and decision-making support. Modular decoupling of data collection and analysis enhances management flexibility, enabling centralized monitoring and optimization across decentralized public buildings.

The model’s innovation lies in its functional decoupling of control and display interfaces, which introduces modularity and scalability to energy conservation management systems [32]. Its architecture supports real-time data acquisition through front-end sensors, dynamic equipment adjustments via intelligent feedback mechanisms, AI-driven energy pattern analysis on the back end, and seamless integration with third-party systems like EMS or BMS. These features enable tailored energy conservation management strategies, overcoming the rigidity of traditional approaches.

Typical application scenarios include decentralized public office buildings characterized by a moderate scale, structural homogeneity, and a lack of centralized energy conservation management infrastructure. Such buildings often exhibit consistent spatial layouts and fixed energy consumption patterns, making them ideal for standardized control strategies. The model addresses common challenges in non-centralized office areas, such as opaque energy data and inefficient management, by providing transparent monitoring and adaptive control.

Despite its advantages, the model faces inherent limitations. In large-scale or multifunctional buildings with heterogeneous energy demands, the technical complexity escalates due to interconnected subsystems like HVAC, lighting, and elevators. Heterogeneous office layouts further reduce adaptability, as uniform control strategies may fail to address localized energy requirements, potentially leading to inefficiencies or unintended consumption spikes. Additionally, expanding the system increases maintenance costs, particularly in managing diverse sensor networks and ensuring platform stability. These constraints highlight the model’s transitional role in small-scale, homogeneous environments, necessitating iterative optimization to overcome scalability barriers in complex scenarios.

Our research identified Jiangxi and Sichuan provinces as exemplary cases implementing this model. Jiangxi Province has focused on key energy-consuming sectors, units, and facilities, promoting comprehensive energy conservation upgrades in decentralized office areas—including building envelopes, HVAC systems, lighting/water fixtures, and office equipment—while phasing out high-energy-consumption, high-emission facilities. The province has pioneered adopting innovative energy-saving and carbon-reduction technologies, achieving 1.24 million square meters of existing building retrofits in public institutions by 2023. Notably, 6083 energy-efficient government offices were established (91.4% of county-level and above administrations), along with 10 national-level green low-carbon public institutions, 8 national energy efficiency leaders, 12 water efficiency leaders, and 8 green data centers—ranking among the nation’s highest concentrations. In 2023, the province reported per capita energy consumption of 64.97 kgce, energy intensity of 3.71 kgce/m², and per capita water use of 21.02 tons in public institutions—representing reductions of 3.93%, 5.60%, and 15.99%, respectively, from 2020 levels.

Sichuan Province has implemented comprehensive energy retrofits for existing buildings and HVAC systems in public institutions, consolidating non-centralized areas while encouraging Energy Performance Contracting and other market-based mechanisms for technological upgrades. Zigong City exemplifies these efforts, having decommissioned 284 high-energy-consumption devices and completed 11,000 m² of building retrofits and 14,000 m² of HVAC upgrades in public institutions. The city established 9 national-level energy-efficient public institution models, 82 “zero-waste government offices”, and 357 energy-efficient government agencies (87.3% conversion rate). These systematic interventions demonstrate effective pathways for public institution digital intelligence transformation through integrated policy, technological, and management innovations.

4.4. “Intelligent Function Aggregation Platform” Model

The “Intelligent Function Aggregation Platform” model integrates functional scenarios of energy conservation management into a unified platform to achieve intelligent, end-to-end monitoring, analysis, decision making, and execution. Primarily applied to large-scale, high-energy-consuming public service institutions such as hospitals, schools, and factories, this model represents a refined approach to the digital intelligence transformation of energy conservation management in public sectors. It consolidates facility management scenarios, smart sensing technologies, security maintenance modules, energy monitoring systems, frequency conversion regulation modules, and intelligent decision-making modules. On the front end, a large-screen interface displays real-time energy consumption, carbon emissions, quota management performance, early warnings, and automated alerts. The PC back-end supports managerial efficiency by providing energy and emissions monitoring, analysis, reporting systems (base/comprehensive/evaluation tables), quota progress tracking, and integrated management modules for electromechanical equipment—spanning monitoring, analysis, alerts, decision-making, regulation, execution, and optimization. A mobile interface enables real-time supervisory “inspections” by management and operational oversight by maintenance staff. This model addresses the ultimate goal of digital intelligence transformation in public institutions: the fusion of digitization and intelligence, combining deep data mining with automated decision-making and execution capabilities.

However, the model faces a “flexibility paradox” inherent to functional aggregation. As the platform aggregates more modules and refines data dimensions, system flexibility diminishes. High integration increases the interdependencies among modules, elevating system coupling and creating unforeseen conflicts in complex scenarios. Maintenance and scalability also become challenging; updating one module often necessitates adjustments across interconnected components, complicating system upgrades. Furthermore, expanding data dimensions strains processing capabilities, particularly with high-frequency updates and multi-source data integration, risking delays or inaccuracies. While the platform supports intelligent decision making, highly dynamic or complex scenarios still require human expertise for nuanced judgments, limiting full automation. These constraints underscore the trade-offs between functional richness and operational agility, necessitating balanced design to mitigate scalability and adaptability risks [33].

As demonstrated above, this model proves particularly suitable for large energy-consuming institutions such as hospitals, schools, and factories. Our research reveals its progressive adoption across major public service institutions nationwide, with Sichuan Province emerging as a mature implementation case. A prime example is the Comprehensive Energy Service Center at Zigong Third People’s Hospital in Sichuan, which has established an intelligent energy conservation system through IoT, big data analytics, and AI technologies. This system integrates power supply, heating, ventilation, and air conditioning systems while combining environmental regulation, equipment management, performance evaluation, and intelligent optimization into an intelligent function aggregation platform. The 49,000 m² hospital facility serving 3386 occupants previously consumed 6.2288 million kWh of electricity, 149,900 m³ of natural gas, and 235,700 tons of water annually, with its conventional energy system’s electricity intensity per floor area exceeding prescribed limits. Through comprehensive Energy Performance Contracting and targeted retrofits of key energy-consuming areas, the hospital achieved estimated annual energy savings equivalent to 291.87 tons of standard coal, reducing carbon emissions by 198.42 tons.

Another exemplary case is Panzhihua Central Hospital in Sichuan, a 128 acre medical complex with 2000 beds across 134,800 m² of floor area, including two major inpatient buildings (25,043.49 m² and 57,213 m², respectively) that consumed 16.7388 million kWh in 2021. Since implementing its “Intelligent Function Aggregation Platform”, the hospital has realized daily electricity savings of 1101 kWh and daily CO₂ emission reductions of 613 kg, with the computer room cluster control system achieving a 19.7% energy conservation rate. These outcomes demonstrate significant cost–efficiency improvements alongside substantial carbon footprint reduction, validating the model’s effectiveness in large-scale health care facilities.

5. Practical Challenges in Digital Intelligence Transformation of Energy Conservation Management for Public Institutions

Through comprehensive field investigations across multiple regions, coupled with analysis of government documentation and interviews with energy conservation officers from relevant public institutions, we have identified four major challenges in the digital intelligence transformation of energy conservation management in public institutions.

5.1. Tension Between Innovation Pressure and Limited Grassroots Resource

The energy conservation management efforts of public institutions are constrained by a dual challenge: the pressure for top-down innovation and the limitations of grassroots resources. While national policies, regulatory frameworks, and technological advancements provide clear directives and momentum for energy conservation initiatives, their implementation at the grassroots level is hindered by insufficient funding, technological gaps, personnel shortages, and misaligned institutional coordination. Unlike hospitals and factories with substantial construction budgets, government offices lack sufficient ongoing energy conservation funding. As one official noted, “The allocated energy conservation budget is inadequate for sustained implementation, making it truly challenging…”. These constraints delay or undermine the adoption of energy conservation retrofits and policies, creating a disconnect between strategic objectives and practical execution.

5.2. Diminishing Marginal Returns and Internalization Costs of Digital-Intelligence Transformation

Public institutions face diminishing marginal returns in energy conservation efforts as initial measures yield significant reductions but lose effectiveness over time due to exhausted optimization potential. This trend is exacerbated by the inherent complexity of public institutions’ energy consumption patterns, including diverse energy endpoints, extended operational timelines, and multi-departmental usage. Several managers expressed practical constraints: “Beyond essential daily consumption and basic practices like turning off lights, there’s limited room for further energy control…”. Another added, “While initial data showed significant improvements, maintaining those levels has become the new baseline…”. Concurrently, the internalization of digital intelligence transformation costs presents a critical barrier. High upfront investments in technology, infrastructure, and skilled personnel, coupled with long payback periods and ongoing operational expenses, strain institutional budgets. Many institutions, particularly those with limited technical expertise, struggle to sustain these investments, further impeding progress.

5.3. Inverted Hierarchy Dilemma: Platform Aggregation vs. Functional Flexibility

A paradoxical “inverted hierarchy” challenge emerges in energy conservation management platforms: higher functional aggregation correlates with reduced flexibility. While integrated platforms consolidate multiple energy conservation modules—enabling comprehensive solutions—their rigid architectures and data silos hinder adaptability to dynamic or heterogeneous scenarios. Officials provided nuanced feedback on digital tools: “The platform’s core functions for managing key energy-consuming areas were well-received initially, but some features lack regional adaptability…”. Others noted evolving needs: “As energy management deepens, we now require expanded functions for waste and food management alongside energy…”. As customization demands grow with refined management needs, overly centralized platforms struggle to accommodate localized requirements, stifling innovation and operational efficiency. This tension between aggregation and adaptability underscores the need for balanced platform design to maintain responsiveness without sacrificing scalability.

5.4. “Floor Effect” in Digital Intelligent Energy Conservation Management

The efficacy of digital intelligent energy conservation management in public institutions exhibits a “floor effect”, where progress plateaus due to technical, managerial, and policy constraints. After initial gains, further improvements in energy efficiency stagnate as technologies reach functional limits, optimization opportunities diminish, and resource investments yield declining returns. Practical operational difficulties emerged: “Certain features were abandoned due to high maintenance costs…”. Staffing issues were also cited: “With existing workloads, creating dedicated positions is problematic. Rotating management duties among staff creates operational inefficiencies…”. Contributing factors include regulatory boundaries, technical bottlenecks (e.g., sensor accuracy, algorithm adaptability), fragmented management practices, and escalating costs. This stagnation highlights the need for systemic innovation to overcome inherent limitations and reignite momentum in energy conservation initiatives [34].

6. Conclusions and Discussion

6.1. Conclusions

Through systematic field investigations of public institutions in Shanghai, Jiangsu, Sichuan, Beijing, Jiangxi, and Tianjin, this study revealed that energy conservation management in public institutions has evolved through distinct phases while progressing from behavioral energy conservation and policy-driven energy conservation to digital and intelligent energy conservation. This progression reflects a fundamental paradigm shift from extensive control to algorithm-driven technological transformation. Now, the innovative practices of digital intelligence transformation in energy conservation management can be categorized into four models in China: the “Technology Demonstration + Digital Platform” model, the “Edge–Cloud Data Middle Platform” model, the “Operation + Platform” split front–back-end model, and the “Intelligent Function Aggregation Platform” model. This study demonstrated that the digital intelligence transformation of energy conservation management in China’s public institutions is constrained by the tension between innovation pressure and limited grassroots resources, the diminishing marginal returns and internalization costs of digital intelligence transformation, an inverted hierarchy dilemma, and the “floor effect” of digital energy conservation under traditional governance norms.

6.2. Discussion

Based on field investigations and the aforementioned findings, the following efforts are essential to advance the digital intelligence transformation of energy conservation management in public institutions.

6.2.1. Region-Specific, Categorized Promotion of Innovative Practices

To address the tension between innovation pressure and limited grassroots resources, digital intelligence transformation must account for regional disparities in resource endowments, economic development levels, and technological foundations. Developed regions should prioritize smart energy management systems (SEMS) and distributed energy integration to enhance efficiency, while energy-rich areas should focus on multi-energy coordination and cross-institutional data platforms for optimized resource allocation. The research indicates that a “technology adaptability–institutional flexibility” classification strategy can improve transformation efficiency by 18–32% (based on pilot data from seven provinces). This avoids the inefficiencies of one-size-fits-all policies and ensures alignment with local needs.

6.2.2. Enhancing Application and Diffusion of New Energy Technologies

To mitigate diminishing marginal returns on energy conservation and rising digital-intelligence costs, large-scale adoption of building-integrated photovoltaics (BIPV) and hydrogen fuel cells should be promoted, alongside compatibility standards for integrating new and existing energy systems. Policy incentives and infrastructure upgrades can accelerate diffusion, as seen in Jiangsu’s “PV–storage–direct flexibility” pilot, where standardized interfaces reduced integration costs by 34%.

6.2.3. Mobilizing Idle Capital via Social and Market Channels

To resolve the inverted hierarchy dilemma between platform integration and functional flexibility, innovative financing mechanisms—such as green bonds and carbon-neutral structured deposits—should attract private investment. Improved Energy Performance Contracting (EPC) models with third-party guarantees can reduce risks and boost participation. For instance, Zhejiang’s energy-saving revenue pledge financing mobilized 63% private capital, while Chongqing’s third-party guarantee pilot increased project implementation rates by 41%.

6.2.4. Establishing a Scientific Performance Evaluation Mechanism

To resolve the inverted hierarchy dilemma between platform integration and functional flexibility, a multi-dimensional assessment model—encompassing energy intensity benchmarks, carbon efficiency indices, smart governance levels, and economic resilience—should replace singular metrics. Beijing’s pilot demonstrated that this approach reduced evaluation bias from 22.7% to 6.3%.

6.2.5. Cross-Sector Collaboration for Technological Breakthroughs

To overcome the “floor effect” of traditional energy management norms, government–industry–academia alliances should tackle key technical bottlenecks via “challenge-based R&D” models. Regional tech-transfer platforms can accelerate commercialization, as seen in Shanghai’s “Learning by Doing” tax incentives, which tripled AI algorithm iteration speeds.