Digital Transformation of District Heating: A Scoping Review of Technological Innovation, Business Model Evolution, and Policy Integration

Abstract

District heating is critical for low-carbon urban energy systems, yet most networks remain centralized in both heat generation and data ownership, fossil-dependent, and poorly integrated with digital, customer-centric, and market-responsive solutions. While artificial intelligence (AI), the Internet of Things (IoT), and automation offer transformative opportunities, their adoption raises complex challenges related to business models, regulation, and consumer trust. This paper addresses the absence of a comprehensive synthesis linking technological innovation, business-model evolution, and institutional adaptation in the digital transformation of district heating. Using the PRISMA-ScR methodology, this review systematically analyzed 69 peer-reviewed studies published between 2006 and 2024 across four thematic domains: digital technologies and automation, business-model innovation, customer engagement and value creation, and challenges and implementation barriers. The results reveal that research overwhelmingly emphasizes technical optimization, such as AI-driven forecasting and IoT-based fault detection, whereas economic scalability, regulatory readiness, and user participation remain underexplored. Studies on business-model innovation highlight emerging approaches such as dynamic pricing, co-ownership, and sector coupling, yet few evaluate financial or policy feasibility. Evidence on customer engagement shows increasing attention to real-time data platforms and prosumer participation, but also persistent barriers related to privacy, digital literacy, and equity. The review develops a schematic conceptual framework illustrating the interactions among technology, business, and governance layers, demonstrating that successful digitalization depends on alignment between innovation capacity, market design, and institutional flexibility.

1. Introduction

District heating (DH) has long been recognized as a cornerstone of the global energy transition, providing efficient and low-carbon heating solutions for residential, commercial, and industrial end users. With accelerating urbanization and the urgency of climate change mitigation, national and local governments are increasingly turning to DH networks to improve energy security, reduce emissions, and foster sustainable urban development [1,2]. Traditionally, however, DH systems have relied on centralized structures in both heat generation and data ownership, lacking the flexibility and responsiveness required in a rapidly evolving energy landscape. This paradigm is now shifting as advances in digitalization, including artificial intelligence (AI), the Internet of Things (IoT), big-data analytics, and automation, begin to reshape how DH is produced, delivered, and consumed [3].

Digital technologies enable real-time monitoring, predictive maintenance, and demand-side management, collectively improving operational efficiency and grid flexibility. For instance, Machine Learning (ML) models have enhanced short- and long-term heat-demand forecasting, leading to more accurate load prediction and lower energy losses [4]. IoT-enabled smart meters facilitate real-time data acquisition and remote fault detection, while automation optimizes supply temperatures and reduces both energy waste and carbon emissions [5]. In leading markets such as Denmark, Sweden, and Germany, digital district-heating (DDH) applications are already embedded in municipal networks to enhance performance and customer interaction [6]. Meanwhile, emerging markets such as China and Eastern Europe are beginning to follow this trajectory as policy frameworks for high-efficiency heating gain momentum [6,7,8].

Despite these technological advances, digitalization inevitably transforms the economic and organizational logic of DH systems. As services evolve from utility-driven supply toward data-enabled, service-oriented, and customer-centric models, operators must redefine value creation, revenue mechanisms, and stakeholder collaboration [9]. New approaches, such as dynamic pricing, decentralized or waste-heat-integrated supply, and interactive consumer platforms, are changing how utilities generate revenue and differentiate services [10]. However, these opportunities also introduce challenges, including the cost of retrofitting legacy networks, regulatory uncertainties, and the need to build consumer trust in automated, data-driven systems [11]. Moreover, existing policy instruments designed to promote sustainable heating often lack the clarity, flexibility, or standardization required to support the digital transition [12]. Earlier generations of DH (4GDH and 5GDH) have been extensively discussed in prior reviews, focusing primarily on thermal integration, hydraulic optimization, and renewable coupling [13,14]. However, these studies provide limited attention to the digital layer that connects technical performance with business-model transformation and governance innovation. This review extends previous analyses by integrating technological, economic, and institutional perspectives to capture how digitalization redefines the structure and governance of district-heating ecosystems.

Furthermore, energy-security and resource-efficiency arguments are revisited. While DH can enhance supply security through improved efficiency and local energy utilization, its centralized configurations may also create system vulnerabilities during disruptions—as observed in recent geopolitical events. Similarly, diesel- or CHP-based systems that supply waste heat are clarified here as integrated energy-recovery measures that increase overall plant efficiency by utilizing both thermal (kWth) and electrical (kWel) outputs rather than independent oil-fired heating sources.

Previous reviews have primarily examined the technical aspects of digital DH, such as algorithmic forecasting, IoT architecture, and control strategies [15], while comparatively few have assessed its economic, business, and social dimensions [16]. Yet digitalization also introduces new opportunities for customer engagement through AI-driven demand-response programs, prosumer participation, and personalized energy services—opportunities often constrained by privacy concerns, limited digital literacy, and insufficient user incentives [17]. These gaps underscore the need for an integrated synthesis connecting technological innovation, business-model evolution, consumer participation, and institutional frameworks across different regional contexts.

In accordance with PRISMA-ScR guidance, this review aims to provide a comprehensive mapping of existing evidence, clarify the conceptual boundaries of digital transformation in DH, and identify research gaps for future investigation. The central research question guiding this scoping review is: How do digital technologies such as AI, IoT, automation, and data analytics reshape the business models, customer-engagement mechanisms, and implementation strategies of district-heating systems, and what barriers and enablers influence this transformation?

To address this question, the review pursues four interrelated objectives: (1) to analyze how digital technologies affect the operational and strategic dimensions of district-heating business models; (2) to identify new value propositions and revenue mechanisms enabled by digital tools; (3) to explore how digital platforms foster customer engagement and prosumer participation; and (4) to assess the financial, regulatory, and organizational barriers that constrain large-scale implementation. By synthesizing these dimensions, the review provides both conceptual and empirical insights into how digitalization can accelerate the transition toward smart, flexible, and economically sustainable heating systems.

To achieve these objectives, the study applies the PRISMA-ScR methodology, performing a systematic search across four major databases (Web of Science, Scopus, IEEE Xplore, and ACM Digital Library), screening and selecting studies through transparent criteria, and conducting a qualitative thematic synthesis. This process ensures comprehensive coverage of relevant research across technical, economic, and social domains.

Figure 1 summarizes the overall scoping review framework used in this study, illustrating how the relevant literature was identified, screened, categorized, and synthesized. It also depicts the four thematic domains that form the analytical foundation of the paper (digital technologies and automation, business-model innovation, customer engagement and value creation, and challenges and implementation barriers) and highlights their interconnections within the broader digital-transformation process of DH.

Figure 1. Paper Structure Overview.

The remainder of this paper is organized as follows. Section 2 details the scoping-review methodology; Section 3 presents the results and thematic synthesis; Section 4 discusses these findings and introduces a conceptual framework integrating technological, organizational, and policy perspectives; and Section 5 concludes by summarizing the theoretical and practical contributions, acknowledging limitations, and outlining directions for future digital district-heating research.

2. Materials and Methods

This study adopts a scoping review methodology to provide a broad yet structured understanding of how digitalization is reshaping DH business models. Scoping reviews are well-suited to emerging interdisciplinary topics because they allow the identification of dominant concepts, thematic trends, and knowledge gaps without the restrictive criteria imposed by systematic reviews [18]. Following the five-stage framework proposed by Arksey and O’Malley [19] and refined by Levac et al. [20] in accordance with the PRISMA-ScR guidelines [21], this review proceeded sequentially through five transparent stages: (1) defining the research question; (2) identifying relevant studies; (3) selecting studies through predefined inclusion and exclusion criteria; (4) charting and coding the data; and (5) synthesizing and reporting the findings.

A systematic literature search was conducted across four major databases (Web of Science, IEEE Xplore, ACM Digital Library, and Scopus), which together provide extensive coverage of technical, managerial, and interdisciplinary energy research. These databases were chosen for their inclusion of peer-reviewed journals, conference proceedings, and review papers spanning both engineering and business domains. Although other databases such as EBSCO and Google Scholar may contain additional records, they were deliberately excluded to ensure quality control, avoid duplication from non-peer-reviewed or gray literature, and maintain methodological feasibility [22]. The search was limited to English-language, full-text publications with no year restriction so that both foundational and recent studies could be captured. The final search was completed in September 2025, covering all records available up to that date, and was supplemented through forward and backward citation tracking of key publications. The mapping of the 69 reviewed publications reveals clear temporal, geographical, and thematic trends in how digitalization is being investigated within DH. As shown in Table A1 (Appendix A), the evidence base spans 2006–2025 (final search September 2025).

A Boolean search string was developed through pilot searches and expert consultation to ensure comprehensive retrieval across technical and economic dimensions of DH. Two concept groups were combined with the operator AND as follows: (“district heating” OR “heat network” OR “combined heat and power” OR “decentralized heating” OR “heat distribution” OR “heat demand”) AND (“business model” OR “value proposition” OR “customer engagement” OR “revenue model” OR “digital transformation” OR “Internet of Things” OR “artificial intelligence” OR “machine learning” OR “automation”). This formulation captured studies addressing digitalization within the context of DH business models and ensured interdisciplinary coverage.

The search yielded 140 records (47 from Web of Science, 12 from IEEE Xplore, 1 from ACM Digital Library, and 80 from Scopus). A structured three-step screening process was applied, as illustrated in the PRISMA-ScR flow diagram (Figure 2). Duplicate records were first removed using a reference manager, resulting in 89 unique articles. Titles and abstracts were then screened independently by two reviewers using predefined inclusion and exclusion criteria (Table 1). Articles that did not address both district heating and digitalization or that lacked business-model relevance were excluded, leaving 69 papers for full-text assessment. Each remaining study was examined in full to verify alignment with the review objectives. Any disagreements between reviewers were resolved through discussion until consensus was achieved, and inter-rater reliability was verified (Cohen’s κ = 0.91), ensuring methodological consistency and minimizing selection bias.

Figure 2. PRISMA-ScR Flow Diagram for Study Selection.

Table 1. Inclusion and Exclusion Criteria.

Inclusion Criteria	Exclusion Criteria
Studies addressing business models in district heating systems.	Studies focusing solely on technical aspects without business-model implications.
Research exploring digitalization (AI, IoT, automation, data analytics, machine learning) in district heating.	Articles without accessible full text.
Peer-reviewed journal articles, conference papers, and review studies.	Duplicate records retrieved across databases.

To synthesize the findings, a structured data-extraction framework was developed. For each included article, bibliographic details, methodological orientation, technological focus, and principal outcomes were recorded together with contextual information on value propositions, customer engagement mechanisms, revenue models, and reported barriers or enablers. All extracted data were compiled in a coded spreadsheet and analyzed using qualitative thematic synthesis. Recurring ideas were grouped into four overarching analytical domains corresponding to the focus of this review: (1) digital technologies and automation, (2) business-model innovation, (3) customer engagement and value creation, and (4) challenges and implementation barriers. Coding was iterative and continuously refined through repeated comparison between reviewers to maintain conceptual coherence, ensure interpretive reliability, and enhance transparency in analytical judgment.

3. Results

The following sections present the key findings from the 69 reviewed studies, systematically mapped to illustrate how digitalization is reshaping DH business models. To achieve a nuanced understanding, the results are organized around four thematic areas identified during data extraction and coding: business model innovations, digital technologies & automation, customer engagement & value creation, and challenges & implementation barriers. This thematic structure captures both the technical advances, such as AI-based automation and IoT-enabled load forecasting, and the economic, regulatory, and user-centric dimensions critical to successful digital transformation in DH. The initial classification Section 3.1 first maps the evidence base—covering chronology, geography, and methods—and quantifies how research attention is allocated across domains. Section 3.2, Section 3.3, Section 3.4 and Section 3.5 then synthesize core findings, exemplary studies, and principal obstacles to realizing more efficient, flexible, and consumer-centered DH systems.

3.1. Overview and Classification of the Reviewed Studies

Following the corpus identification described in Section 2, the temporal analysis of publications reveals a distinct acceleration in research activity after 2019, when AI, IoT, and automation began to feature prominently in both technical and management studies. Earlier work (2006–2018) focused primarily on supervisory control and optimization frameworks aimed at improving operational efficiency [23,24], whereas the more recent literature demonstrates increasing maturity in machine-learning-based forecasting [25,26,27,28,29,30,31,32,33] and a growing emphasis on digital-driven business-model innovation [10,34,35,36]. This shift indicates a transition from technology-centred optimization toward a broader socio-technical understanding of how digitalization enables organizational and market transformation within district-heating systems.

Geographically, the field is dominated by Nordic and Central-European contexts, particularly Denmark, Sweden, Finland, Germany, and the Netherlands, which together account for more than two-thirds of the identified studies [6,34,35]. Research from Asia, led by China and South Korea, has grown since 2020 [37], whereas contributions from North America and other regions remain scarce. This regional skew suggests limits to transferability; therefore, some findings should be interpreted cautiously for non-European regulatory and market contexts (e.g., North America, parts of Asia). Methodologically, quantitative and model-based approaches dominate, with relatively few qualitative case studies or policy analyses and even fewer mixed-method investigations that integrate techno-economic and behavioral perspectives [10,36,38,39].

Each study was coded according to its primary thematic orientation and cross-referenced when it spanned multiple areas. Four categories emerged from this classification: business-model innovation, digital technologies and automation, customer engagement and value creation, and challenges and implementation barriers. As summarized in Table 2, the distribution is highly uneven: digital technologies and automation dominate with 55 studies, followed by business-model innovation (19), challenges and implementation barriers (21), and customer engagement and value creation (9). The total exceeds 69 because several papers address multiple domains.

Table 2. Distribution and Characteristics of Reviewed Studies (n = 69).

Thematic Domain	Number of Studies	Share of Corpus (%)
Digital Technologies & Automation	55	80%
Business-Model Innovation	19	28%
Customer Engagement & Value Creation	9	13%
Challenges & Implementation Barriers	21	30%

Overall, the mapping confirms that digital-technology research dominates the field (≈80% of studies), followed by business-model innovation (≈28%), challenges and implementation barriers (≈30%), and customer engagement and value creation (≈13%). This quantitative summary provides a clear picture of the research landscape and highlights the relative under-representation of socio-economic and behavioral studies compared with technical optimization.

This quantitative overview confirms that research on digitalized DH remains technologically oriented, emphasizing forecasting accuracy, fault detection, and system optimization [25,26,27,28,29,30,31,32,33,40,41]. In contrast, studies exploring economic and organizational transformation, such as service-based contracts, co-ownership, or multi-energy integration, are comparatively fewer and often conceptual [10,21,33,42]. Work on customer engagement and policy or financial barriers is emerging but remains fragmented [37,38,42,43,44,45,46]. Collectively, the pattern underscores the need for integrated, interdisciplinary approaches that connect digital capability with economic feasibility, behavioral acceptance, and regulatory readiness, ideally through mixed-method designs and comparative regional analyses.

The following sections build on this mapping to provide detailed syntheses of each thematic domain: Section 3.2 examines digital technologies and automation; Section 3.3 discusses business-model innovation; Section 3.4 addresses customer engagement and value creation; and Section 3.5 analyzes challenges and implementation barriers.

This regional distribution broadly mirrors the installed capacity and maturity of DH markets, with Northern and Central Europe representing the most established infrastructures. However, research intensity does not fully align with regions showing the fastest growth potential—such as China, South Korea, and parts of North America—indicating a spatial research gap between deployment expansion and academic attention.

3.2. Business Model Innovations in Digitalized District Heating

Business-model innovation represents the economic and organizational core of digital transformation in district heating (DH). Whereas Section 3.1 established that relatively few studies have analyzed this dimension compared with the extensive technical literature, the available evidence reveals how digitalization is beginning to redefine the mechanisms of value creation, delivery, and capture across the DH ecosystem. Rather than merely improving operational control, digital tools, particularly AI-driven analytics, IoT-based monitoring, and real-time automation, are enabling service-oriented and collaborative business logic that connect technical optimization with financial performance. The reviewed studies cluster around four focal areas (Table 3): (1) integration of renewable energy and waste-heat recovery, (2) smart-grid and decentralized market models, (3) customer-centric and co-creative strategies, and (4) policy and financial mechanisms, reflecting a transition from centralized infrastructure service to a digitally networked socio-technical system.

Table 3. Focus Areas and Representative Findings on Business Model Innovation in Digitalized District Heating.

Focus Area	Representative Findings and Implications
Integration of Renewable Energy & Waste Heat Recovery [29,35,43]	Integration of solar, biomass, and industrial waste heat requires adapted financial agreements and risk-sharing contracts. Machine-learning-optimized solar-assisted DH achieves more than 80% solar fraction and a payback of around 14 years; supermarket heat-recovery models yield up to 93% cost savings. However, contract transparency and pricing fairness are prerequisites for scalability.
Smart Grid & Decentralized Market Models [34,39,44,45,46].	Digitalization enables dynamic pricing, real-time control, and cross-sector trading. Coordinated wind-power and DH operation cuts heating costs by around 55% and energy waste by around 32%. Game-theoretical studies reveal that co-ownership and multi-stakeholder participation enhance competitiveness, yet robust regulatory design remains essential.
Customer-Centric Business Strategies [10,38,47,48]	Digital platforms foster prosperity and co-creation. A “smart and transparent” IIoT-based DH model achieves around 26% energy-use reduction through real-time interaction. Crowdfunding, demand-side contracting, and participatory planning reduce return temperatures and operating costs while increasing user trust.
Policy & Financial Mechanisms [36,37,49]	Regulatory rigidity and limited financial access hinder the diffusion of digital models. Split incentives and non-standardized heat pricing deter investment. Green bonds, public–private partnerships, and risk-sharing instruments are proposed to bridge the financing gap and enable experimentation with dynamic tariffs and service-based contracts.

3.2.1. Integration of Renewable Energy and Waste Heat Recovery

Digitalization has unlocked new technical possibilities for coupling DH networks with renewable and residual-heat sources, yet the success of these technologies ultimately hinges on the accompanying business logic. About one-third of the business-model studies address renewable and waste-heat integration, reflecting its status as a cornerstone of decarbonized heating strategies. The majority of these papers (e.g., [29,35,43]) originate from Nordic and Central European contexts, where large-scale solar and industrial waste-heat projects are already operational. Only a small number (notably from China) focus on policy instruments for incentivizing such integration [37]. Methodologically, most combine techno-economic modeling with case-study evidence.

Waste heat, typically a by-product of electricity generation or industrial processes, still involves CO₂ emissions; its recovery, however, increases total energy efficiency by utilizing both thermal (kWth) and electrical (kWel) outputs. This dual-use potential is central to many business-model innovations that aim to valorize residual energy streams within local markets.

Collectively, these works reveal that technological maturity is rarely the barrier; bankability and governance are. Solar-assisted DH can exceed 82% solar contribution at moderate payback, but investors seek long-term revenue security via indexed heat-purchase agreements [29]. Supermarket refrigeration waste-heat integration can reduce operating costs by over 90%, equivalent to annual energy savings of approximately €15,000–20,000 per medium-scale retail facility, under shared-savings contracts [35]. Ambiguous ownership of recovered heat discourages industrial partners, highlighting the need for standardized pricing and liability rules [43].

The underlying mechanism is a shift from asset-centric to relationship-centric business models: digital monitoring creates verifiable data streams that enable performance-based contracting and lower perceived risk. In this sense, digital transparency functions as a new form of collateral that replaces physical ownership with information credibility. However, most studies treat financial and contractual design qualitatively, and very few quantify the extent to which digital data reduces risk premiums or financing costs. There is also a geographic bias: virtually no longitudinal data exists from developing heating markets where capital access is limited. Future research should employ integrated techno-economic and institutional modeling to determine how digital assurance mechanisms (such as blockchain verification and real-time measurement) could formalize heat-trading contracts and enable large-scale renewable integration.

3.2.2. Smart Grid and Decentralized Market Models

Roughly eight of the identified business-model papers investigate how digitalization facilitates market decentralization and multi-energy integration. These studies represent the transition from infrastructure optimization to ecosystem coordination. The temporal distribution of this literature is recent, nearly all post-2018, reflecting the rapid diffusion of fourth- and 5GDH concepts. Methodologies range from techno-economic simulation [34] and residential heat-demand forecasting using machine-learning approaches [46] to game-theoretic market analysis [39,44]. Most cases are European, with Denmark, Sweden, and Germany dominating.

Findings converge on the enabling role of digital control and data exchange. Joint optimization of wind and DH resources reduces both curtailment and cost [34], while 5GDH local-market models emphasize the financial resilience of co-ownership arrangements [44]. Dynamic-pricing simulations demonstrate that flexible tariffs can maintain profitability under fluctuating renewable supply [45]. Collectively, these studies show that digitalization converts thermal infrastructure into a market-responsive asset capable of participating in electricity balancing, carbon trading, and flexibility services.

Conceptually, this literature marks a move from firm-centric to platform-mediated business logic. The DH operator becomes a market orchestrator linking multiple energy carriers and stakeholders. Predictive analytics and automation enable this orchestration by synchronizing production, storage, and demand across sectors. Yet, literature remains largely normative. Few empirical evaluations assess transaction costs, governance complexity, or equity effects of decentralization. Without such evidence, claims of economic sustainability remain provisional. Moreover, while studies acknowledge regulatory barriers, they seldom analyze institutional co-evolution—how law and market design adapt to digital platforms. Integrating institutional economics perspectives and comparative regulation analysis would allow researchers to move beyond technological determinism and evaluate how digital markets redistribute value and risk among actors.

3.2.3. Customer-Centric and Co-Creative Strategies

A third cluster of research explores the social reconfiguration of DH through customer participation and co-creation, accounting for roughly one-quarter of business-model papers. This stream directly connects digital transformation with consumer engagement, positioning information and behavioral flexibility as new economic assets. Studies span multiple scales, from building-level experiments with smart meters [38] to municipal governance models [10]. Methods include behavioral response simulation, participatory foresight, and design thinking.

Across cases, digital platforms enhance transparency and responsiveness. IIoT-enabled dashboards that allow consumers to monitor heat use in real time have been shown to reduce total demand by 26% [38]. Co-creation processes involving pilot user groups in iterative service design further strengthen consumer participation [47]. The City Model Canvas has been used to analyze municipal–private partnerships, showing that public ownership combined with digital service management improves both accountability and efficiency [10]. Crowdfunding and shared-savings contracts also enhance user commitment and lower system return temperatures [48].

The mechanism underlying these successes is data-driven empowerment: digital feedback loops align consumer behavior with system optimization. Customers become prosumers whose decisions influence not only energy efficiency but also revenue streams. This creates a two-way dependency: utilities rely on consumer participation for flexibility, while consumers rely on utilities for data transparency and trust.

However, the empirical base of the literature is thin. Many projects are small-scale pilots, often lacking control groups or long-term follow-up. Moreover, behavioral heterogeneity is under-examined; most models assume rational economic response, neglecting social and cultural factors. Privacy and data-governance issues, though acknowledged, are rarely quantified in economic terms. Future work should merge behavioral economics with digital business model design to assess how incentive schemes (e.g., gamification, dynamic tariffs) influence sustained participation. Longitudinal research could reveal whether co-creative models lead to durable revenue diversification or merely temporary efficiency gains.

Recent behavioral and stated-preference studies also highlight willingness to pay (WTP) as a key determinant of adoption. Consumers show higher WTP for digital DH services when benefits are framed in terms of comfort, reliability, and transparency rather than pure cost savings. Integrating WTP metrics into business-model design can therefore help utilities calibrate tariffs, service tiers, and incentive schemes that reflect perceived user value and enhance market acceptance.

3.2.4. Policy and Financial Mechanisms for Sustainable District Heating

The success of business-model innovation in digitalized district heating ultimately depends on the institutional environment that governs pricing, investment, and risk distribution. Across the reviewed literature, policy and financial mechanisms emerge as decisive enablers of scalability, linking technological feasibility with economic viability. Rather than focusing on technical optimization, these studies examine how regulatory design, market incentives, and financing instruments, such as green bonds, public–private partnerships, and performance-based tariffs, shape utilities’ ability to experiment with and sustain digital business models. The evidence collectively shows that without adaptive regulation and coordinated financing, even mature digital technologies remain confined to pilot scale.

Common findings highlight risk aversion, rigid tariff regimes, and fragmented governance as systemic barriers. Municipal utilities often have limited capacity to experiment with digital tariffs due to procurement rules [36]. Split incentives and non-standardized pricing also deter private participation in China’s industrial waste-heat utilization [37]. The absence of standardized contracts further delays investment in low-temperature networks [49]. Proposed instruments include green bonds, energy-efficiency funds, and PPP arrangements to distribute risk and leverage private capital. In addition, studies emphasize that the balance between consumer effort and realized savings is critical for large-scale adoption. Digital retrofits and behavioral engagement, such as interface learning and data-sharing consent, require modest time investment but yield measurable energy savings of 10–20% within three to five years. However, households often need minor equipment upgrades, including smart thermostats, advanced metering interfaces, or compatible heat exchangers, to fully benefit from digital heating services. Clear communication of expected payback periods and technical requirements is therefore essential to ensure consumer trust and widespread participation. Digitalization shifts regulators from price-setting toward rule-setting for data exchange, cybersecurity, and market transparency. However, most policy analyses remain descriptive; quantitative evidence on financial instrument impact and equity is limited. Comparative policy and socio-legal analyses are therefore recommended to clarify how governance innovation co-evolves with digital technology adoption.

In summary, business-model innovation is pivotal yet immature. The transition from centralized, producer-dominated structures to multi-stakeholder ecosystems introduces new roles, revenues, and coordination mechanisms, but most innovations remain pilot-stage. Cross-cutting deficiencies include limited quantitative evaluation of financial performance, inadequate understanding of consumer behavior in data-driven environments, and fragmented regulatory experimentation. From a theoretical standpoint, these findings extend frameworks such as the Business Model Canvas and Value-Network Theory by demonstrating that digital transparency, data governance, and policy flexibility form new structural layers of business models in critical infrastructure sectors.

This relationship illustrates how digital transparency acts as information collateral, transforming trust into an economic asset that underpins new contractual and governance models. Quantitatively, only around 20–25% of studies engage with these economic and governance dimensions compared with around 45% emphasizing technical automation (Section 3.3).

3.3. Digital Technologies and Automation in District Heating Business Models

Digital technologies form the technical core of DH digitalization, providing the analytical and control capabilities that underpin emerging business models. The reviewed studies collectively demonstrate that AI, ML, IoT, and automation are driving measurable gains in network efficiency, reliability, and flexibility. Most empirical work originates from Nordic and Central European systems, where long-established DH infrastructures and digital policy support have enabled large-scale pilots and validation studies. In contrast, recent research from East Asia focuses on rapid deployment and system integration, illustrating how regional technological maturity and policy context shape both the pace and direction of digital adoption.

Although technology has progressed, research largely focuses on the technical aspects. They demonstrate performance gains (lower losses, faster fault detection, optimized scheduling), but seldom explore how these digital capabilities translate into sustainable revenue structures or customer-oriented value propositions. As a result, a clear gap persists between technical feasibility and commercial scalability. This section synthesizes evidence across three core technological dimensions that underpin digital transformation in DH: heat-demand forecasting and load prediction, intelligent fault detection and system optimization, and smart-grid automation and control. Before analyzing these, it is important to note that fourth-generation district heating (4GDH) integrates renewable energy sources and low-temperature networks, whereas fifth-generation district heating (5GDH) extends this concept by introducing decentralized, bidirectional, and digitally controlled systems that enable sector coupling and multi-energy integration. These dimensions, summarized in Table 4, together illustrate how digitalization reshapes operational logic while revealing unresolved challenges in integrating technological and business innovation.

Table 4. Focus Areas and Key Findings on Digital Technologies and Automation.

Focus Area	Representative Findings and Implications
Heat-demand forecasting and load prediction	ML-based forecasting improves accuracy by 10–25% over statistical baselines [25,29]. Hybrid approaches such as ICA-SVM and EMD-SVM outperform single algorithms [30]. Deep-learning models (LSTM, CNN) perform robustly across climates [26,31], enabling dynamic tariff design and supply–demand balancing.
Intelligent fault detection and system optimization	AI-driven image analysis via UAVs detects underground leaks with more than 95% accuracy [50]. ML-based predictive maintenance anticipates faults up to five days ahead, cutting downtime by 25% [51]. virtual sensors detect substation fouling 61% earlier [35]. Reinforcement-learning and MPC algorithms enhance fuel efficiency and cut CO2 by around 3%.
Smart-grid and automation technologies	IoT alarm systems, digital twins, and AI-based scheduling enable real-time anomaly detection and dynamic control [52,53]. Multi-boiler MPC reduces GHG emissions by around 2.8% [40]. Cloud-based frameworks integrate waste-heat recovery [54], while 3D-GIS/NB-IoT twins coordinate demand response, lowering peak load around 12%.

3.3.1. Heat Demand Forecasting & Load Prediction

Accurate heat-demand forecasting forms the operational backbone of digital DH. Traditional empirical or regression-based methods have long supported short-term scheduling, yet the reviewed literature shows a decisive shift toward data-driven and hybrid machine-learning approaches capable of higher temporal granularity and adaptive learning. Over a dozen studies published between 2014 and 2024 explore algorithmic innovations ranging from support vector regression (SVR) and k-nearest neighbors (k-NN) to advanced deep learning architectures such as LSTM and CNN.

Most papers report error reductions of 10–25% relative to conventional autoregressive or polynomial models [25,26,29,46]. Hybrid methods that fuse signal decomposition (ICA, EMD) with ML classifiers achieve even higher precision [30,31], particularly under nonlinear and highly variable load conditions. In practical terms, these improvements translate into significant cost savings: one Nordic utility reduced peak-load overestimation by around 15%, yielding annual fuel and standby-capacity savings estimated at €150,000 [55].

Beyond short-term prediction, several works embed forecasting within multi-objective optimization frameworks that balance energy efficiency, emissions, and economics [29]. By coupling ML algorithms with solar-assisted or storage-integrated DH systems, these studies demonstrate how digital intelligence can enable hybrid energy portfolios and support participation in ancillary electricity markets.

While forecasting accuracy is a recurring success, integration into business-model practice remains limited. Only a few papers discuss how predictive analytics could underpin dynamic pricing, contractual guarantees, or “heat-as-a-service” offerings. The disconnect arises partly because forecasting research sits at the engineering layer, while tariff innovation lies within regulatory and managerial domains. Bridging this gap requires interdisciplinary modeling that links forecast reliability to financial instruments—such as risk-based pricing or flexibility premiums that monetize information accuracy. Furthermore, the literature is geographically skewed toward temperate-climate datasets; few studies test algorithm transferability to regions with higher volatility or limited data infrastructure. Recent empirical analysis [46] confirms that machine-learning models can maintain stable forecasting performance under real-world residential conditions, supporting the transition from laboratory evaluation to operational deployment in smart heating management. Comparative studies across climates and institutional settings would strengthen external validity and clarify how forecasting performance scales globally.

3.3.2. Intelligent Fault Detection & System Optimization

A second major research stream concerns predictive maintenance and system optimization. Here, AI and ML replace reactive fault management with anticipatory diagnostics based on real-time sensor data and pattern recognition. Approximately 15 papers focus explicitly on fault detection across pipelines, substations, and CHP units.

Results demonstrate impressive technical performance. UAV-mounted thermal cameras analyzed with convolutional neural networks detect underground pipeline leaks with more than 98% accuracy [50,51], while hybrid sensor-integration approaches combining hydraulic and thermal data [56] further enhance localization accuracy and fault-detection robustness in buried pipeline networks. Virtual sensors using surrogate modeling detect substation fouling 61% earlier than conventional monitoring [35]. ML-based fault prediction in CHP boilers reduces unplanned outages by around 25%, saving over €300,000 per heating season, which corresponds to roughly 3–5% of annual operating expenditure for a typical municipal plant [51]. Recent hybrid models integrating machine learning with mathematical-statistical diagnostics [57] improve fault classification under uncertain or imbalanced data conditions, providing greater robustness for real-time fault management. Reinforcement-learning algorithms applied to dispatch optimization lower fuel use and CO₂ emissions by 2–4% [40,58].

Collectively, these achievements demonstrate the economic potential of predictive analytics, yet their integration into routine operations remains limited. Legacy SCADA systems, proprietary architectures, and cybersecurity constraints hinder full digital-twin deployment, so many utilities adopt only partial AI modules or vendor-specific dashboards—limiting cumulative learning. Another underexplored area is business-model adaptation: few projects specify how cost savings are shared among providers, operators, and customers. Potential mechanisms include outcome-based maintenance contracts, insurance-linked performance guarantees, or data-licensing agreements that could transform AI reliability into a tradable service. Comparative institutional analyses could further clarify how governance structures influence commercialization pathways.

Despite these gaps, the trajectory is clear: digital diagnostics are moving DH toward proactive, risk-adjusted asset management. Future work should include longitudinal cost–benefit analyses and standardized data protocols to support widespread adoption.

3.3.3. Smart Grid and Automation Technologies

The third and broadest cluster extends digitalization to system-wide automation, encompassing IoT sensor networks, edge- and cloud-based computing, and real-time optimization via digital twins. More than 20 papers since 2019 examine aspects of “smart” or “self-optimizing” DH grids that dynamically coordinate production, storage, and consumption.

IoT and sensor integration constitute the enabling infrastructure. Studies [52,53] develop rule-based alarm systems that detect anomalies, such as abrupt temperature changes in return flows, while other works integrate thousands of distributed sensors using low-power NB-IoT protocols to improve data resolution and responsiveness. Digital-twin frameworks that combine hydraulic modeling, 3D-GIS visualization, and machine learning enable continuous simulation of network states [59]. Such systems enable predictive control, adjusting flow and temperature set-points in real time, and support demand-response coordination between residential and commercial users. Reported benefits include a 12% peak-load reduction and a 2–3% decrease in energy consumption without compromising comfort.

Automation also redefines generation scheduling. AI-driven MPC for multi-boiler and CHP plants optimizes dispatch to minimize operational cost and emissions [40,60]. Cloud-based platforms couple waste-heat recovery with real-time data analytics, improving overall energy efficiency [41,54,61]. Together, these advances illustrate how digital connectivity transforms DH networks into cyber-physical systems capable of decentralized coordination.

Yet the literature repeatedly notes the absence of corresponding economic evaluation frameworks. Few studies quantify how automation affects the marginal cost of heat, tariff structures, or payback of digital investments. Similarly, the social dimension, how automation influences employment, skills, or perceptions of data privacy, is rarely examined. Without such evidence, the “smart-grid” narrative risks remaining technologically deterministic.

A promising research frontier lies in coupling automation with new contractual and pricing mechanisms. For example, pay-per-use or subscription models could leverage automated metering to bill customers based on comfort levels rather than energy volume, aligning technical efficiency with consumer value. Digital twins might also underpin performance-based service guarantees, in which utilities charge premium tariffs for reliability or emissions performance. Developing and testing such hybrid techno-economic models would bridge the current divide between operational excellence and financial innovation.

When viewed collectively, these three technological dimensions reveal a progressive deepening of digital integration. Forecasting provides informational foresight; predictive maintenance ensures operational reliability; automation achieves systemic coordination. Each stage builds upon the previous one, gradually evolving DH toward a data-centric, self-learning infrastructure.

Temporal analysis of the publications indicates an acceleration after 2019, coinciding with the diffusion of IoT platforms and the rising accessibility of open-source ML libraries. Methodologically, a clear trend toward hybridization is evident: more than 60% of the studies published after 2021 combine multiple algorithms or data sources rather than relying on single techniques. However, replication and benchmarking remain problematic because datasets are often proprietary and geographically restricted. The absence of standardized metrics for accuracy, efficiency gain, or cost reduction undermines meta-comparison across cases—a recurring weakness that future collaborative datasets could address.

From a theoretical perspective, the technological literature implicitly contributes to the digital-twin and cyber–physical-system paradigms, framing DH as an evolving socio-technical organism where sensors, algorithms, and human operators co-adapt. Yet most engineering studies stop short of engaging with socio-economic or governance theories, leading to disciplinary silos. Integrating system-engineering models with institutional and behavioral frameworks would yield a more comprehensive understanding of how digital intelligence reshapes decision-making, accountability, and trust in public-utility contexts.

3.4. Customer Engagement & Value Creation in Digital District Heating

Digitalization is shifting DH from a supplier-centric utility to a participatory, information-rich ecosystem in which end users co-produce efficiency, flexibility, and service quality. Real-time monitoring, IoT-enabled metering, and interactive platforms allow households and organizations to visualize consumption, respond to tariff signals, and participate in demand-response or co-investment schemes. Yet the magnitude and durability of these gains depend on usability, trust, and the alignment of incentives through policy and market design. For a schematic of how technology, business, and governance interact to shape engagement and value creation, see Figure 3 and its expanded caption. Table 5 synthesizes the literature along four interlinked focal areas (digital interaction, co-creation and customization, public–private collaboration, and policy/market strategies) and anchors the analysis that follows.

Figure 3. Conceptual Framework for the Digital Transformation of District Heating.

Table 5. Focus areas and key findings on customer engagement and value creation.

Focus Area	Key Findings from Literature
Digitalization & Smart Grid–Enabled Customer Interaction	Real-time monitoring and digital twins enhance efficiency, though customer education and trust remain essential [38,62]
Co-Creation & Customization in Business Models	Customer participation in service design fosters energy efficiency, requiring innovative financing tools [47,48]
Public–Private Collaboration for Sustainable District Heating	Municipal-private partnerships can balance operational costs and sustainability objectives but face policy barriers [10,36]
Policy & Market Strategies for Consumer Participation	Regulatory incentives, dynamic tariffs, and support for emerging technologies align consumers with district heating goals [63,64]

3.4.1. Digitalization and Smart Grid-Enabled Customer Interaction

Across pilots and case studies, real-time feedback consistently changes consumption behavior. A frequently cited implementation, an interactive “smart and transparent” platform built on a 3D-GIS digital twin with NB-IoT metering, reported a 26.11% reduction in energy use when end users received instant performance data and actionable guidance, showing how visibility can catalyze sustained adjustments in set-points and timing of demand [38]. This behavioral effect, however, is contingent: studies emphasize that sustained use requires intuitive interfaces, plain-language communication, and credible data-security practices, not just technical capability.

The broader evidence base suggests that digital inclusion matters: uneven digital literacy and low trust in external automated control limit uptake, especially in socially diverse districts [62]. Special attention is needed for older adults and low-literacy users, including larger-font displays, simplified workflows, and assisted onboarding, to ensure equitable access and durable engagement. Reported public perceptions (e.g., discomfort with AI-assisted control and privacy concerns) indicate the need for human-centered design and transparent pricing dashboards that help users understand savings pathways and the degree of automation they can opt into. Policy-oriented analyses, therefore, call for explicit data-governance rules (ownership, access, cybersecurity, liability) as part of consumer protection in digital heating services.

Digital platforms create new informational assets, timely, disaggregated, user-level data that enable comfort-oriented products and flexible tariffs. To translate this into durable value, utilities must pair the platform with user education, privacy assurances, and interface standards that make participation effortless. These adaptations map directly onto the technology–business–governance feedback in Figure 3.

3.4.2. Co-Creation and Customization in Business Models

Digitalization enables a transition from one-size-fits-all offerings to co-designed services in which customers help shape pricing, service levels, and investment priorities. Prosumer and co-ownership models, supported by real-time data and shared-savings contracts, feature prominently. Case studies show that crowdfunding and energy-saving contracting can reduce return temperatures and bills while increasing commitment, with projects reporting higher user satisfaction when co-ownership options are offered alongside demand-response programs [48].

Forward-looking studies (e.g., [47]) envision engagement as a continuous process: pilot groups test features (e.g., alerts, comfort guarantees, gamified goals), iterate on tariff preferences, and co-decide on retrofit priorities. The result is not merely a tariff change but an ongoing governance relationship that leverages digital traces to refine offerings. Still, scaling beyond pilots remains difficult.

The literature notes heterogeneity of preferences, the risk that early enthusiasm decays, and the challenge of designing incentives that are fair across income levels and dwelling types. Hence, repeated calls for supportive regulatory frameworks and risk-tolerant financing to lower participation barriers and protect vulnerable users when dynamic pricing is introduced. Although customized, co-created services can move engagement from episodic to structural. But to scale, they require standardized participation contracts, customer-facing analytics that explain “how” savings arise, and safeguards that ensure equity when flexibility is monetized. Ultimately, these business-model choices are shaped by regulatory adaptation (tariff codes, consumer protections) as highlighted in Figure 3.

However, several case studies are based on self-selected or highly motivated communities, which may introduce participation bias. These early adopters often display stronger environmental or technological enthusiasm than the general population, meaning that outcomes from pilot projects may overstate engagement potential in broader deployment. Future research should therefore test co-creation models under more representative social conditions to evaluate their long-term scalability and realism.

3.4.3. Public–Private Collaboration for Sustainable District Heating

Municipal accountability and private digital expertise can be complementary [10]. City-scale analyses show that public ownership combined with private operation achieves cost control and sustainability goals when roles are clear and performance is digitally verified [36]. Instruments such as the Value Proposition Canvas and City Model Canvas have been used to allocate responsibilities and align incentives across partners.

Despite the promise, two constraints recur. First, procurement and risk aversion slow the adoption of sophisticated IoT and analytics platforms; second, data rights and privacy become a negotiation arena between municipalities, vendors, and citizens. Comparative studies of municipal behavior identify divergent postures—some authorities limit digital investments to avoid fiscal exposure, whereas others actively leverage PPPs (Public–Private Partnership), green bonds, and outcome-based contracts to fund modernization and citizen-facing services. Documented cases report overhead cost reductions (around 15%) and improved peak-demand resilience when PPP financing unlocks large-scale IoT upgrades, but the enabling condition is a clear governance and data-sharing framework. In practice, “bureaucratization” (e.g., lengthy tendering, compliance documentation, fragmented approval chains) can delay or dilute PPP benefits unless processes are streamlined and responsibilities codified in the contract. For engagement to scale city-wide, PPPs must hard-wire data governance, service guarantees, and shared risk into contracts; otherwise, digital pilots remain isolated islands without citizen trust or financial durability.

3.4.4. Policy and Market Strategies for Consumer Participation

The literature converges on the idea that regulation shapes behavior: dynamic tariffs, consumer protections, and compatibility rules for complementary technologies (e.g., building-level heat pumps) steer participation and create room for new business models. Analyses show that traditional heat-tariff structures are poorly suited to real-time, AI-enabled pricing and call for reforms that permit time-of-use rates, flexibility contracts, and market-based balancing services—paired with clear guardrails for data protection and liability [64].

Where complementary technologies are recognized and integrated rather than treated as competitors, cost and emissions reductions are larger, but utility perceptions often lag: heat pumps are sometimes seen as an erosion of the customer base rather than as a source of flexible, low-carbon heat within the network [63]. Studies recommend customer-focused regulatory frameworks and incentive schemes (e.g., rebates combined with dynamic time-of-use tariffs) that increased participation by about 40% in one reported context, illustrating how policy can reconcile utility and consumer interests. Participation is a market design variable. Tariff flexibility, consumer safeguards, and pro-complementarity rules (e.g., for heat pumps) enable utilities to monetize responsiveness without compromising equity or privacy. In addition, open algorithm governance can be translated into practical user terms by disclosing how data are used to adjust tariffs or optimize comfort, using plain-language explanations and visual indicators rather than technical specifications. Such transparency helps non-experts understand automated decision processes and fosters long-term trust in digital heating systems. These policy updates also enable business-model adaptation (subscription/comfort-as-a-service, performance guarantees), reinforcing the technology–business–governance loop depicted in Figure 3.

3.5. Challenges & Implementation Barriers in Digitalized District Heating

Despite significant progress in digital technologies, business-model innovation, and customer engagement, the transition toward large-scale, intelligent DH remains constrained by multiple barriers that impede technological diffusion and long-term scalability. While digitalization promises efficiency gains, cost reduction, and customer participation, its real-world implementation is hindered by an intricate web of technical, financial, regulatory, and institutional factors. The reviewed literature consistently identifies four principal categories of challenges (technical and operational, economic and financial, regulatory and policy, and market and stakeholder), each of which critically influences the success of digital transformation. These dimensions are summarized in Table 6, which consolidates the main findings and citations.

Table 6. Focus areas and representative findings on challenges and implementation barriers.

Focus Area	Key Findings from Literature
Technical and Operational Barriers	Leak detection, fault prediction, and system optimization face challenges related to sensor accuracy, predictive reliability, and integration complexity [40,41,51,57,61].
Economic and Financial Constraints	High investment costs and uncertain ROI (Return on Investment) hinder adoption, requiring cost-sharing mechanisms and new revenue streams [34,35,39,47].
Regulatory and Policy Challenges	Legal uncertainties, split incentives, and rigid market structures limit the scalability of waste heat recovery and flexible pricing models [37,44,49].
Market and Stakeholder Adoption Issues	Industry reluctance, sector competition, and lack of skilled personnel slow adoption of AI, waste heat partnerships, and integrated energy solutions [43,63,65].

3.5.1. Technical and Operational Barriers

Technical limitations remain the most frequently discussed obstacle to digital-DH implementation. Legacy infrastructure, heterogeneous control systems, and limited interoperability complicate the integration of AI, automation, and IoT technologies. Studies highlight sensor inaccuracies, data inconsistencies, and predictive model instability as recurring issues that undermine real-time analytics and fault detection [57].

Leak detection provides a typical example. UAV-based infrared imaging combined with CNNs achieves nearly 98.6% detection accuracy [51], yet scaling beyond pilot tests is difficult due to high deployment costs, stringent airspace regulations, and data-processing complexity. AI-based failure prediction for CHP boilers can forecast faults up to five days in advance [40], but inconsistent data quality, uncalibrated sensors, and rare-event bias limit practical reliability. Similarly, ML-based diagnostic systems [41] and virtual-sensor approaches [61] demonstrate notable efficiency gains, such as 61% earlier fouling detection, but require continuous retraining to address changing load conditions, rendering them resource-intensive.

These examples illustrate that technological maturity does not equal operational readiness. Integration of multiple vendor platforms into legacy SCADA environments often causes data bottlenecks and cybersecurity vulnerabilities. Furthermore, many utilities lack standardized data architectures or digital twins, preventing holistic system optimization. The increasing technical complexity of digital DH systems further amplifies this challenge. As automation and AI-driven control expand, they demand multidisciplinary skill sets combining IT, engineering, and data science expertise—competencies that are often scarce in municipal utilities. Without systematic training or knowledge-transfer programs, this complexity can become a practical barrier to large-scale implementation Capacity constraints—particularly shortages of data engineers, control specialists, and cybersecurity professionals—further slow operationalization. Consequently, although AI-driven diagnostics enhance performance, scalability depends on infrastructure renewal, interoperability standards, targeted workforce upskilling, and sustained investment in digital reliability. The literature, therefore, calls for coordinated sensor calibration programs, open-data protocols, and lifecycle cost analyses to convert laboratory advances into field-level dependability.

3.5.2. Economic and Financial Constraints

The second barrier category concerns the economics of digitalization. High capital intensity, uncertain payback, and misaligned risk allocation repeatedly emerge as deterrents to adoption. Studies demonstrate that digital upgrades (smart meters, automation platforms, and advanced analytics) require multi-million-euro investments, often beyond municipal utilities’ borrowing capacity.

For example, analysis of supermarket heat-recovery models by [35] shows a 93% potential reduction in operating costs, yet implementation stalls due to upfront capital and contractual complexity. Similarly, research on low-temperature DH [47] finds that digitalization raises capital expenditure even as it improves efficiency, highlighting the need for innovative financial design to ensure long-term viability. Strategic bidding models integrating wind energy into DH [34,39] offer new revenue streams through power-market participation, but exposure to electricity-price volatility and heat-market regulation introduces substantial financial risk.

The evidence underscores a structural paradox: technical gains are clear, but business incentives remain ambiguous. Conventional tariff schemes do not reward data-driven efficiency or flexibility, leaving utilities unable to recover the costs of digital investments. Consequently, adoption depends on external financing instruments, such as public–private partnerships, government subsidies, or revolving green funds, that redistribute risk and extend repayment horizons. Administrative “bureaucratization” (multiple approvals, lengthy procurement cycles) can also delay financing and deployment, indicating the need for streamlined governance pathways.

Empirical gaps persist. Very few studies quantify return on investment (ROI) or net present value trajectories for digital retrofits, and cost–benefit analyses often omit intangible benefits such as avoided emissions or data-driven resilience. Addressing this evidence deficit is vital: policymakers cannot design effective incentive schemes without credible financial benchmarks. The literature thus calls for harmonized evaluation metrics and comparative financial models that integrate technical, social, and environmental payoffs.

3.5.3. Regulatory and Policy Challenges

Regulation constitutes both a bottleneck and an enabler of digital transformation. Most jurisdictions retain rigid tariff systems, ambiguous ownership rules, and fragmented governance, which collectively discourage innovation. Legal ambiguities surrounding waste-heat ownership and third-party access are well documented. Ref. [49] identifies contractual uncertainty and unclear liability as central obstacles to large-scale urban waste-heat recovery. Ref. [37] highlights split incentives and restricted market entry in China’s DH sector, which undermines private investment despite supportive rhetoric. Comparative European research [44] shows that even technologically advanced 5GDH systems depend critically on dynamic-pricing authorization, yet regulators often lack mechanisms to approve variable tariffs or prosumer transactions.

Regulatory lag extends to data governance: few countries have explicit rules defining rights to sensor or consumer-behavior data generated within municipal networks. This vacuum creates privacy risks and deters cross-sector integration. Moreover, energy-efficiency directives rarely synchronize with data-protection laws, leading to compliance uncertainty for utilities experimenting with AI analytics. Process-level “bureaucratization” (e.g., overlapping permits, fragmented supervisory mandates) further slows regulatory adaptation.

The literature therefore converges on the need for coherent multi-level governance reform—standardized heat-pricing frameworks, transparent data-ownership rules, and clear conditions for third-party service participation. Policy instruments such as regulatory sandboxes, adaptive licensing, and performance-based incentives could bridge experimentation and compliance. Without such enabling reforms, even technically validated solutions risk stagnating at the pilot stage.

3.5.4. Market and Stakeholder Adoption Issues

Beyond infrastructure and policy, market acceptance and stakeholder alignment determine whether digitalization moves from demonstration to diffusion. The studies reviewed depict a conservative and risk-averse industrial culture within many DH organizations, compounded by skill shortages and sectoral competition.

Ref. [65] observes that despite extensive academic exploration of ML-based optimization, real-world uptake remains minimal. Barriers include a lack of digital expertise, skepticism about algorithm transparency, and the perceived complexity of integrating AI into everyday operations. Ref. [43] emphasizes governance deficits in industrial waste-heat partnerships, unclear accountability, negotiation deadlocks, and limited trust among partners, which prevent collaborative investments. Similarly, [63] notes competitive tension between heat-pump providers and DH utilities, with the latter often viewing distributed heat pumps as threats rather than complementary assets, thereby fragmenting potential sector coupling.

This resistance reflects deeper institutional inertia: municipal utilities accustomed to stable, volume-based revenue models see little incentive to adopt disruptive digital technologies that alter risk distribution or require new skill profiles. Meanwhile, workforce constraints impede implementation even where management is supportive; the sector faces a chronic shortage of data scientists, control engineers, and cybersecurity specialists. Practical remedies include targeted training, shared hiring pools via PPPs, and clearer value-communication between vendors and regulators to align expectations.

The literature also highlights a communication gap between technology developers and policymakers. Vendors emphasize technical capability, whereas regulators and investors seek quantifiable public benefits, leading to misaligned expectations. Building capacity through targeted training, joint-industry platforms, and co-governance mechanisms could help align these perspectives.

Analysis across these four categories reveals deep interdependence. Technical barriers often stem from or amplify economic and policy bottlenecks: poor interoperability increases investment risk; unclear regulation discourages financing; market resistance slows policy experimentation. Conversely, overcoming one constraint, such as establishing standardized data protocols, can simultaneously reduce technical uncertainty, attract investors, and enhance stakeholder trust.

A temporal pattern is also visible. Earlier studies (pre-2018) focused primarily on hardware and control optimization, whereas recent work integrates cybersecurity, data governance, and institutional readiness into barrier analysis. Geographically, Nordic and Central-European contexts dominate empirical evidence, while research from emerging markets (e.g., Eastern Europe, Asia) highlights infrastructural gaps and weaker governance as primary constraints. This asymmetry suggests that institutional maturity correlates strongly with digital-transition progress: regions with stable regulatory regimes and financial instruments report faster diffusion of smart-heating technologies.

Overall, the evidence depicts a classical socio-technical lock-in in which mature infrastructure, entrenched business practices, and static regulation co-evolve to slow disruption. Progress, therefore, depends less on new technology than on institutional learning, capacity building, and cross-sector coordination that gradually reconfigures existing systems.

3.6. Interdisciplinary Insights: The Integration of Digital Technologies, Business Models, and Implementation Strategies in District Heating

Digital transformation in DH is inherently interdisciplinary because it links advanced engineering, evolving business models, and adaptive regulation to achieve sustainability, economic viability, and long-term sectoral resilience. Although AI-driven forecasting, IoT-enabled monitoring, and predictive analytics promise substantial gains in efficiency and decarbonization, utilities still confront formidable technical, financial, and governance barriers. The literature therefore stresses that business-model innovation serves as the connective mechanism that translates digital capability into practical value by providing new revenue models, customer-engagement approaches, and flexible market frameworks that can mainstream digital solutions. Figure 3 provides a visual summary of these interactions and has been enriched to highlight enabling and constraining feedbacks across layers.

The reviewed studies converge on three cross-cutting insights:

• Digital technologies act as catalysts for consumer interaction and value creation.
• Implementation of these technologies is shaped by enduring technical, financial, and policy constraints.
• Business-model innovation functions as the structural enabler that bridges technology and policy, ensuring scalable and inclusive deployment of digital DH.

3.6.1. Digital Technologies as a Driver of Customer Engagement and Value Creation

Digital technologies are redefining the relationship between energy providers and end users. Research increasingly shows that AI-based analytics, IoT-enabled smart meters, and digital-twin models move DH from supplier-centered control toward interactive, customer-centered services that merge operational optimization with behavioral participation. The resulting two-way data flows create both informational and economic value, but they also demand deliberate policy support to safeguard privacy and equity. Non-expert usability (accessibility features, plain language, assisted setup) is a prerequisite for durable benefits and is now emphasized alongside data-governance requirements.

• Technological shifts in interaction.

Digital tools now allow consumers to monitor consumption, adjust behavior, and even automate decision-making. Studies show that real-time feedback and predictive control improve user awareness and efficiency. For example, an implementation that combined 3D-GIS digital-twin visualization with NB-IoT communication achieved a 26.11% reduction in total energy use by providing transparent feedback to users [38]. These findings underscore the transformative potential of visibility and transparency in everyday energy decisions.

• Emergence of prosumer and co-ownership models.

Enhanced participation is not limited to data access. It includes co-creation, prosumer investment, and shared-savings contracts that align consumer and provider incentives [47,66]. Shared-ownership frameworks yield measurable efficiency and trust benefits because households gain both agency and financial stake in performance outcomes. Yet diffusion is slowed by privacy concerns, the complexity of dynamic pricing, and the lack of algorithmic transparency [36,64]. Addressing these issues requires adaptive tariff designs, open algorithm governance, and clear consumer-rights policies that legitimize automated decision support.

• Enabling conditions for sustained participation.

Technology alone cannot maintain engagement. Studies stress that user education, intuitive interfaces, and incentive-driven digital tools are crucial for long-term participation [62]. Gamified applications and transparent billing reduce skepticism and increase adoption, while regulatory frameworks that guarantee digital inclusion for low-literacy or low-income groups help ensure equitable benefits. Future work should incorporate behavioral science and human–computer-interaction insights into AI design to promote trust and inclusivity in data-driven heating ecosystems.

3.6.2. Challenges in the Adoption and Implementation of Digital Technologies in District Heating

Although the technical promise of digitalization is evident, large-scale deployment remains constrained by interconnected technical, financial, and policy obstacles. The studies reviewed portray digital transformation as a multi-layered process that demands simultaneous advancement in infrastructure, organizational capability, and institutional alignment. Reducing bureaucratic friction (streamlined permits, coordinated oversight) and addressing skills shortages are cross-cutting enablers.

• Technical and operational bottlenecks.

Utilities must retrofit decades-old infrastructure while maintaining service continuity. Sensor calibration, data quality, and algorithm reliability remain problematic. Investigations of legacy SCADA integration reveal high complexity and cost [61,65]. UAV-based thermal imaging achieves excellent detection accuracy for underground leaks [51], yet operational expenses and flight regulations restrict full deployment. Likewise, inconsistent data streams and obsolete communication protocols compromise the reliability of AI-based boiler-failure prediction [40]. These challenges demonstrate that digital readiness depends as much on system standardization as on algorithmic sophistication.

• Financial and policy barriers.

High capital costs, uncertain returns, and limited cost-sharing mechanisms continue to inhibit scaling [65]. Even when digital control reduces fuel use, utilities and municipalities hesitate to commit funds without predictable revenue recovery. Fragmented policy frameworks and inflexible tariffs discourage dynamic pricing, decentralized operation, and integration with complementary technologies such as heat pumps [37,44,49]. Case studies of 5GDH show that, in the absence of common standards and clear revenue mechanisms, innovation remains isolated within pilots. Public–private partnerships are often proposed as partial solutions [10], but they require legal clarity and long-term financing structures to succeed.

• Bridging the implementation gap.

Holistic strategies are needed to align technical capacity with regulatory and market conditions. Recommendations across studies include revising tariff codes to allow real-time or performance-based pricing, introducing investment tax credits or AI-adoption subsidies, and creating digital-skills programs inside utilities. These measures could normalize digital operation as standard practice rather than experimentation, moving DH from isolated demonstration toward sector-wide transformation.

3.6.3. Business Model Innovations as a Catalyst for Digital Transformation

Technological and policy readiness alone cannot guarantee adoption; digital success ultimately hinges on the business logic that links efficiency gains to sustainable revenues and user value. The literature identifies business-model innovation as the bridge connecting technical feasibility with financial and organizational legitimacy. Regulatory adaptation (dynamic tariffs, data-rights clarity, sandboxing) is integral to this bridge, enabling new offers such as comfort-as-a-service and performance-based guarantees.

• Emerging, future-oriented frameworks.

Successful implementation requires business models that integrate co-creation, sector coupling, and continuous data analytics. These frameworks connect DH with renewable power, industrial waste heat, and urban-energy management to diversify income and improve resilience [10,38,47]. Municipal or hybrid ownership structures often perform best because they combine accountability with the agility to test new digital platforms. Real-time data transparency within these structures strengthens both investor confidence and consumer trust.

• Barriers to customer-driven models.

Transitioning from centralized to collaborative service provision challenges entrenched cultures and regulatory habits. Utilities must involve users and external partners in decision-making. Conservative management, limited consumer awareness, and perceived competitive threats inhibit this shift. For instance, when heat-pump integration demonstrates cost and emissions reductions, incumbents sometimes view the technology as a rival instead of a complement [63]. Overcoming this resistance requires incentive-aligned engagement, such as performance-based bonuses, community-financing instruments, and risk-sharing contracts that protect both utilities and customers.

• Policy and market preconditions for scale.

Persistent issues of market fragmentation, contractual ambiguity, and static pricing continue to constrain the development of new models [37,44,49]. Establishing standardized legal templates, risk-sharing finance mechanisms, and flexible tariff guidelines appears essential for experimentation and replication. Regulatory reforms that open markets to private participation, reward prosumer contributions, and streamline waste-heat integration can create a reinforcing cycle in which initial digital successes attract additional investment and trust. Without such reform, technically advanced solutions remain trapped at demonstration scale.

The interdisciplinary insights across these themes reveal that digital transformation in DH is a co-evolutionary process. Technology provides the operational capacity for optimization, while business models and policy frameworks determine the speed and scope of adoption. Integration among these dimensions transforms DH from a physical network into a socio-technical ecosystem governed by data, trust, and collaborative governance.

The reviewed literature collectively advances three key propositions:

• Alignment of value systems. Technological innovation achieves impact only when technical metrics, financial returns, and social values are mutually reinforcing. Digital transparency can align these dimensions by converting operational data into shared decision evidence for consumers, utilities, and regulators.
• Institutional learning and adaptability. Continuous feedback among engineering, economics, and governance is essential. Pilot projects function as learning laboratories where tariff design, user behavior, and digital performance co-adapt.
• Inclusive digital governance. Equity and participation must remain central to digital heating design. Regulations should guarantee fair data use, protect privacy, and support access for vulnerable groups so that the benefits of digitalization extend beyond technologically advanced or affluent communities.

4. Discussion

Digitalization is reshaping DH systems from static, centralized utilities into adaptive, data-driven socio-technical ecosystems. The evidence synthesized in this review reveals that while AI, ML, IoT applications, and automation already deliver measurable improvements in efficiency, reliability, and predictive control [13], their long-term impact depends on how effectively they are embedded in business strategies, user engagement models, and regulatory structures. In other words, the core challenge is no longer technological feasibility but systemic integration. Across the literature, technical readiness exceeds institutional readiness, producing a misalignment that prevents many promising pilots from reaching commercial maturity [14].

Four thematic domains underpin this transformation: digital technologies and automation, business-model innovation, customer engagement and value creation, and implementation barriers. The Discussion integrates findings across these domains, positioning digitalization as an enabling process whose success hinges on the coordination of technology, governance, and social participation. To avoid redundancy, this Discussion consolidates insights that were previously split between the Results and Discussion sections, providing an integrated interpretation of evidence.

4.1. Emerging Research Trends

• Digital technologies and automation as dominant focus

Nearly half of the reviewed articles emphasize AI-based forecasting, IoT-enabled monitoring, and automated fault [25,26]. These technologies have consistently reduced operational costs, improved fault-response times, and enhanced load balancing. Yet their commercial value remains underexplored because most studies evaluate technical performance rather than economic viability. The literature demonstrates that predictive maintenance and automation improve efficiency, but they only become sustainable when coupled with revenue models that monetize savings, such as dynamic tariffs or comfort-guarantee services. As observed by [14], enduring digital transitions depend on multi-stakeholder collaboration and adaptive regulation rather than technical optimization alone.

• Business-model innovation as an emerging frontier

Despite widespread attention to technology, business-model innovation remains underrepresented. Only a minority of studies analyze how digitalization alters value propositions or revenue logic [45,47]. Evidence on heat-as-a-service, co-ownership arrangements, and multi-energy coupling suggests new pathways for economic sustainability, yet most remain conceptual or confined to pilot scale [67]. These findings indicate a structural lag between technological and financial innovation. Bridging this gap requires integrative research that combines digital-performance metrics with cost–benefit analysis, risk assessment, and stakeholder economics.

• Growing importance of customer engagement and value creation

Digital transformation has also shifted the social dynamics of heating. Consumers are evolving from passive recipients to active participants who interact with digital platforms and co-create efficiency gains [10,38]. Studies confirm that interactive dashboards and mobile applications improve awareness and enable behavioral adjustments, but adoption varies with usability, transparency, and trust. Privacy concerns and pricing complexity remain major deterrents. The implication is clear: technological sophistication must be matched by social accessibility. Platforms that integrate intuitive interfaces and transparent pricing achieve higher participation rates, confirming that user experience is a determinant of digital diffusion. This inclusivity is particularly critical for older adults and low-literacy users, reinforcing the need for universal design and assisted digital onboarding.

• Persistent and interconnected barriers

Recurring challenges across the literature form a barrier complex that spans technical interoperability, economic feasibility, regulatory rigidity, and market acceptance [63,68]. Legacy infrastructure and static tariff systems discourage innovation even when technology is available. Workforce skill shortages, sectoral competition, and fragmented data governance reinforce this inertia. Administrative bureaucratization, lengthy tenders, multi-agency approvals, and fragmented oversight, further slows innovation diffusion. Addressing these barriers requires bundled interventions that combine policy flexibility, financial incentives, and institutional capacity building rather than isolated technical fixes.

4.2. Conceptual Framework: Interactions Among Technology, Business, and Governance

To interpret these findings, this study proposes a conceptual framework (Figure 3) that situates digitalization as the integrative element linking technological capability, business model evolution, customer participation, and institutional adaptation. The framework captures the feedback loops that determine whether digital transformation translates into long-term value creation.

• Digital technologies and operational transformation. AI, IoT, and automation underpin predictive maintenance, dynamic load management, and real-time optimization [27,61]. These technical functions form the material basis for digital DH but achieve systemic value only when embedded in business models that allocate cost and benefit equitably.
• Business-model innovation and value capture. Digitalization enables new service designs, such as performance contracts, decentralized energy trading, and sector coupling [10,39], yet regulatory rigidity and uncertain ROI still constrain adoption. Adaptive financial instruments and flexible pricing are prerequisites for scaling.
• Customer engagement and digital interaction. Smart metering and co-creation platforms empower users to monitor and modify consumption [48,62]. Studies show that transparent feedback and incentive schemes increase participation, though concerns about privacy and fairness remain barriers. Engagement, therefore, depends on a triad of technical functionality, social trust, and regulatory protection.
• Implementation barriers and institutional readiness. Legal ambiguity, investment risk, and stakeholder inertia restrict diffusion [44,49]. Addressing these issues demands coordinated governance that links local utilities, regulators, and technology providers through risk-sharing and standardization mechanisms.

The framework extends existing socio-technical transition and business-model frameworks by explicitly integrating data transparency, governance adaptation, and consumer co-creation as reinforcing mechanisms of system change. Earlier approaches, such as the layered business-model framework for low-temperature systems [49] or the cooperative game-theoretic analyses of fifth-generation networks [44], address specific dimensions. In contrast, this integrated framework synthesizes them within a multi-level transition logic [42], clarifying how technological innovation, economic feasibility, and governance co-evolve in digital DH. It thus contributes a novel theoretical dimension: the notion of digital transparency as “information collateral”, transforming trust into an economic asset that underpins contractual innovation and user engagement.

Moreover, the framework highlights how digitalization redefines value creation and governance mechanisms by converting operational data into both a market resource and a regulatory tool. This reconfiguration enables adaptive tariff models, outcome-based regulation, and participatory oversight mechanisms where verified performance data serve as a basis for policy incentives and compliance. Consequently, governance evolves from static rule enforcement toward data-driven coordination and trust-based collaboration between utilities, regulators, and consumers.

The conceptual framework is intended not only as a visual summary of relationships among the four domains (digital technologies, business-model innovation, customer engagement, and implementation barriers) but also as a theoretical device for interpreting causal linkages observed across the literature. It shows how digitalization acts as both a driver and a mediator in the transformation of DH. Technological capabilities such as forecasting, automation, and IoT integration generate new possibilities for efficiency and flexibility; business models translate those capabilities into economic value; customer participation determines legitimacy and market reach; and governance either accelerates or constrains the entire process through regulation and finance. The framework, therefore, functions as a lens for identifying the mechanisms of co-evolution that connect technical performance, financial sustainability, and institutional adaptation.

In extending socio-technical transition theory, the framework also accounts for end-user implications through what can be termed the “smart-home ripple effect”: digital heating platforms interact with building-level automation, EV charging, and demand-response systems, amplifying flexibility and household energy intelligence. This systemic integration reinforces the central argument that digital DH operates not in isolation but within the wider ecosystem of smart urban infrastructure.

To make this framework analytically useful, the review distills several illustrative propositions that summarize how the evidence points to specific, testable relationships. These propositions are not hypotheses to be verified within this paper but conceptual statements that can guide future empirical and comparative research:

• Proposition 1 (Technology–Economy linkage). When forecasting and automation technologies achieve demonstrably higher accuracy and reliability, utilities become more inclined to adopt flexible or performance-based tariff schemes that monetize efficiency gains [25,49]. This relationship reflects the shift from technical optimization to economic valorization of digital capability.
• Proposition 2 (Technology–User linkage). Customer-facing initiatives that combine transparent dashboards, real-time data feedback, and voluntary control options produce higher satisfaction and stronger behavioral engagement than fully automated systems that limit user agency [10,38]. This finding underscores the importance of perceived control and transparency as essential mediators of acceptance in digital heating.
• Proposition 3 (Governance–Diffusion linkage). Regions that offer regulatory sandboxes, standardized data governance frameworks, and access to blended financing instruments, such as green bonds, scale digital heating solutions faster than regions governed by rigid tariff and licensing regimes [69]. This pattern demonstrates how institutional flexibility and risk sharing facilitate systemic innovation.

These propositions operationalize the conceptual framework by translating its qualitative relationships into researchable claims. They offer a foundation for building future mixed-method studies that combine technical performance data, economic evaluation, and policy analysis to validate how digitalization generates value across different contexts.

4.3. Implications for Practice and Policy

• Industry implications

For operators and technology providers, digital transformation represents both a technical and strategic inflection point. Companies must shift from volume-based sales toward service-oriented business models such as predictive-maintenance contracting, performance guarantees, and integrated energy-service offerings [70]. Embedding AI-driven monitoring into these models can reduce operational costs while differentiating services based on reliability or environmental performance. Strategic partnerships among DH operators, IoT developers, and municipalities distribute financial risk and facilitate technology transfer. Investment in workforce upskilling, user-centered design, and data-literacy programs is essential to bridge the skills gap identified across Section 3.5 and Section 4.1.

• Policy and regulatory implications

Regulators and policymakers are key enablers of diffusion. Evidence underscores the effectiveness of dynamic pricing, standardized data-governance frameworks, regulatory sandboxes, and hybrid public–private investment schemes [69]. Establishing regulatory sandboxes allows controlled experimentation with novel tariffs and digital contracting without punitive consequences. At the same time, robust consumer protection and privacy protocols are needed to maintain trust. Integrating these measures within national heating strategies could accelerate adoption while safeguarding equity.

• Research implications

For scholars, the framework calls for interdisciplinary methodologies that unite engineering, economics, and behavioral science. Studies should measure both technical performance (algorithm accuracy, sensor reliability) and socio-economic outcomes (ROI, payback, user satisfaction). Living labs and longitudinal pilots can generate real-world data, while comparative policy research can identify how different regulatory environments shape diffusion trajectories. Future work should also address ethical dimensions, such as data ownership and fairness in AI-driven decision making, ensuring that digitalization contributes to inclusive energy transitions [48,62].

Overall, this integrated Discussion underscores that digital transformation in DH is a co-evolutionary process in which technology, business models, customer engagement, and governance evolve interdependently. AI and IoT create operational intelligence; innovative business models convert that intelligence into value; and adaptive policy establishes legitimacy and scale. Successful digitalization, therefore, requires alignment among efficiency, profitability, and social acceptance. When these dimensions converge—supported by participatory governance and end-user empowerment—district heating can transition from a static utility to a collaborative, low-carbon ecosystem that supports the broader goals of sustainable urban energy systems.

5. Conclusions

This scoping review synthesized 66 peer-reviewed studies to examine how digitalization is transforming DH business models. The analysis is structured around digital technologies and automation, business-model innovation, customer engagement and value creation, and challenges and implementation barriers, revealing that digitalization can substantially enhance operational efficiency, enable decarbonization, and foster service-oriented and participatory business approaches. Yet, despite technological maturity, implementation remains uneven because institutional frameworks, financial mechanisms, and consumer behaviors have not evolved at the same pace. The review, therefore, concludes that sustainable digital transformation in DH depends on the co-evolution of technology, business logic, customer participation, and policy adaptation.

Theoretically, this study advances the understanding of DH digitalization as a co-evolutionary socio-technical transition rather than a purely technical modernization process. By integrating insights from engineering, economics, and governance, it provides a conceptual framework that illustrates the feedback loops linking technological capability, business model design, and institutional readiness. This framework extends socio-technical transition theory by demonstrating how digital transparency, data governance, and consumer trust form new structural dimensions of value creation and legitimacy. It also highlights that alignment among efficiency, economic viability, and social legitimacy is the central condition for scaling digital solutions beyond the pilot stage.

From a practical perspective, the review underscores that the potential of AI, IoT, and automation will only be realized when supported by adaptive business models, coordinated financing, and participatory digital platforms. Translating technical innovation into commercial success requires flexible tariffs, market-based incentives, and regulatory experimentation that allow utilities to recover investments while protecting consumers. Equally, intuitive and transparent digital interfaces, such as digital twins and mobile applications, can strengthen user engagement and trust, ensuring that consumers become co-creators of system efficiency rather than passive beneficiaries.

Because this review relied on English-language and European-dominated studies and excluded databases such as Google Scholar and EBSCO, the results may not capture emerging evidence from Asia, North America, or developing regions. Consequently, the generalizability of its findings is constrained by geographic and linguistic biases. Future comparative research should expand database coverage, incorporate non-English studies, and examine institutional diversity across different policy and market environments. Moreover, empirical evidence linking technological performance to long-term financial and behavioral outcomes remains limited. Future work should therefore integrate detailed case analyses and industry data to validate the conceptual framework across diverse regulatory and cultural settings. Comparative regional studies could clarify how governance structures, market maturity, and social norms shape the diffusion of digital DH, while techno-economic and behavioral assessments are needed to quantify costs, payback periods, and user responses to AI-enabled systems.