Redefining Energy Management for Carbon-Neutral Supply Chains in Energy-Intensive Industries: An EU Perspective

Abstract

Energy-intensive industries (EIIs) face mounting pressure to reduce greenhouse gas emissions while maintaining international competitiveness—a balance that is central to achieving the EU’s 2030 and 2050 climate objectives. In this context, energy management (EM) emerges as a strategic instrument to decouple industrial growth from fossil energy consumption. This study proposes a redefinition of EM to support carbon-neutral supply chains within the European Union’s EIIs, addressing critical limitations of conventional EM frameworks under increasingly stringent carbon regulations. Using a modified systematic literature review based on PRISMA methodology, complemented by expert insights from EU Member States, this research identifies structural gaps in current EM practices and highlights opportunities for integrating sustainable innovations across the whole industrial value chain. The proposed EM concept is validated through an analysis of 24 EM definitions, over 170 scientific publications, and over 80 EU legal and strategic documents. The framework incorporates advanced digital technologies—including artificial intelligence (AI), the Internet of Things (IoT), and big data analytics—to enable real-time optimisation, predictive control, and greater system adaptability. Going beyond traditional energy efficiency, the redefined EM encompasses the entire energy lifecycle, including use, transformation, storage, and generation. It also incorporates social dimensions, such as corporate social responsibility (CSR) and stakeholder engagement, to cultivate a culture of environmental stewardship within EIIs. This holistic approach provides a strategic management tool for optimising energy use, reducing emissions, and strengthening resilience to regulatory, environmental, and market pressures, thereby promoting more sustainable, inclusive, and transparent supply chain operations.

1. Introduction

In the European Union (EU), the medium-term industrial policy focuses on facilitating energy, climate, environmental, and digital transitions while simultaneously preserving global competitiveness, maintaining employment, and fostering innovation in clean technologies [1,2,3,4,5,6]. Manufacturers can substantially lower their environmental footprint by optimising resource utilisation and reducing waste, ensuring their operations align with global sustainability objectives.

While energy management (EM) has long been recognised as a tool for improving industrial efficiency, most existing frameworks—including ISO 50001 (ISO 50001:2018 Energy management systems-Requirements with guidance for use)—remain narrowly focused on facility-level performance, energy audits, and static efficiency metrics. The literature reflects this fragmentation: studies typically isolate technical, managerial, or policy aspects without offering an integrated, systems-level perspective that reflects the complexity of decarbonising energy-intensive industries (EIIs). Furthermore, little attention is paid to aligning EM practices with evolving EU policy instruments such as the Green Deal, Fit-for-55, or the Emissions Trading Scheme (ETS). This paper addresses that gap by proposing a comprehensive redefinition of EM that (i) incorporates digital technologies, (ii) integrates energy services across supply chains, and (iii) embeds strategic alignment with EU regulatory and funding mechanisms. By doing so, it advances the conceptual foundations of EM and offers a practical framework for achieving carbon neutrality in energy-intensive sectors.

EIIs are pivotal in maintaining the EU’s industrial strength and economic sovereignty [5]. These industries, including iron and steel, nonferrous metals, non-metallic minerals, refining, pulp and paper, chemicals, and cement, are essential for manufacturing value chains [7]. However, they are also among the largest greenhouse gas (GHG) emitters, accounting for a significant share of industrial emissions. In line with the European Green Deal, the EU has committed to achieving climate neutrality by 2050, necessitating a transformative approach to decarbonising EIIs.

Around 60% of EU companies view energy costs as a significant barrier to investment, a challenge 30 percentage points higher than their US counterparts. Electricity retail prices for industrial sectors in Europe are currently two to three times higher than in the US and China. Since 2021, EIIs have experienced a production decline of 10-15%, with compressed profit margins persisting even as energy prices decline [5,8].

Energy efficiency (EE) is a critical pillar for mitigating high energy costs, GHG-related costs, and investment burdens, yet its full potential remains untapped [9,10]. Despite the recognised potential for energy savings, many cost-effective measures remain underutilised due to various barriers, which are traditionally explained by the “barrier model.” While most research and policy efforts focus on technology-driven solutions, operational measures can also achieve significant EE improvements. One of the most effective and cost-efficient measures to support EE is EM. Integrating technology and operational strategies is crucial to the success of EM practices [10]. EM refers to the systematic monitoring, control, and optimisation of energy use in industrial processes, aiming to reduce consumption and costs while minimising environmental impact [11]. EM is classified as an EE policy measure, as regulatory frameworks and governmental policies primarily drive its development and effectiveness [12,13]. The literature identifies 58 distinct EM practices, highlighting their versatility and effectiveness [14]. Moreover, a financially viable approach to improving EE must integrate energy-efficient technology investments with continuous EM strategies [15,16]. Notably, the energy management system (EMS) market is projected to grow from USD 49.01 billion in 2025 to USD 84.34 billion by 2029, with a CAGR of 13.8% per year (%/year) [17].

Digital transformation is already one of the primary drivers of change in the energy sector. The growing adoption of renewable energy, challenges related to resilience, and the increasing focus on sustainability are key factors pushing the industry to evolve and embrace digital technologies [18]. EM, energy decentralisation, smart grids, and electric vehicle charging infrastructure are the central areas of digital transformation in the energy sector.

In the industry, the transition toward digitalisation is under the cover of Industry 4.0, which presents a unique opportunity to advance cleaner production strategies. This concept embraces technologies such as cloud computing, the Internet of Things (IoT), cyber–physical systems, digital twin [19], and big data analytics [20,21,22,23,24,25].

In turn, the digital transformation of EM is crucial for achieving the EU’s energy and industry policy objectives [26]. This system-wide initiative underlines the importance of digitisation in developing a sustainable and secure energy services market, ensuring data privacy, and fostering investment in digital energy infrastructure. Integrating digital technologies in EM could enhance real-time monitoring, predictive maintenance, and process optimisation. This shift would support operational efficiency, regulatory compliance, cybersecurity, and the scalability of energy solutions, aligning EM with broader sustainability and energy goals.

Despite the availability of cutting-edge solutions, EM remains undervalued in the industrial sector’s low-carbon transition. Its limited scope, regulatory barriers, and the complexity of industrial production have prevented widespread adoption [27,28]. Even within EIIs, EM often lacks strategic prioritisation, due to political reluctance to enforce mandatory EM measures. The Council of the European Union highlights the “lack of requirements and incentives for implementing energy management systems” as a major barrier to improving industrial energy efficiency [29].

The outdated nature of the current EM framework exacerbates these challenges, failing to align with the ongoing digital transformation of industrial operations. Emerging technologies such as AI, IoT, and big data analytics are revolutionising manufacturing and process optimisation, yet EM remains disconnected from these innovations [5,28]. Without integration into Industry 4.0, SGs and digital control systems, EM risks becoming obsolete—unable to respond to real-time energy market signals or dynamically optimise industrial operations. This disconnection highlights the urgent need for a redefined EM framework, incorporating digital tools and modern approaches to ensure its relevance and effectiveness in the evolving industry.

This research, presented in this article, was inspired by the observation that no standardised or universally accepted EM definition or framework exists, leading to inconsistencies in implementation and limiting cross-sectoral collaboration [30,31]. Moreover, EM does not fully benefit from recent developments in the power and manufacturing sectors, nor does it align with broader economic, societal, and sustainability objectives. Traditional EM falls short in addressing cost competitiveness, decarbonisation strategies, and the circular economy [32]. This misalignment leads to rigid, outdated EM practices that fail to respond to evolving market demands and regulatory pressures. By integrating EM into a broader industrial and policy framework, the future EM model should serve as a strategic enabler that aligns with the EU’s long-term goals of sustainability, economic resilience, and environmental stewardship.

This article contends that EM must be redefined to fully capitalise on emerging energy EE opportunities, digitalisation, and strategic decision-making. Rather than being a mere technical update, this transformation requires a conceptual shift that integrates Industry 4.0 technologies, regulatory governance, and market incentives into a cohesive framework.

The primary aim of this research is to rethink and redefine the EM framework, enabling it to:

• Fully exploit the EE potential across the entire industrial supply chain;
• Leverage digitalisation and AI-driven technologies to enhance decision-making processes;
• Achieve both energy and non-energy benefits, including sustainability, resilience, and cost optimisation.

We have stated the main research question:

“How must the concept and functional scope of EM be redefined to effectively support the decarbonisation of EIIs in the EU, while accounting for technological innovation, policy alignment, and supply chain integration?”

By critically examining current EM practices, technological advancements, and evolving policy frameworks, this paper seeks to identify pathways for reforming EM to ensure its continued relevance in the carbon-neutral supply chains of EIIs.

EM in the industry is often referred to as industrial energy management (IEnM) [28,33]. The scope of this article focuses on EM within “energy-intensive industries.” However, this term is broadly defined as “industries consuming more than a specified threshold of energy”, which positions them as prime candidates for energy-saving initiatives.

The article is structured as follows: the methodology of the research and analysis based on the literature sources is described in Section 2; the effect of the literature review performed, together with the identification of gaps in previously published research and approaches to the topic, is presented in Section 3; the proposal for a new concept of energy management is presented in Section 4, together with a discussion presented in Section 5; the limitations of the performed research and the scope of future research are presented in Section 6 and Section 7, respectively; Section 8 contains the conclusions.

2. Materials and Methods

2.1. Research Design and Literature Review

A modified systematic literature review (SLR) was the primary method used to ensure comprehensive coverage of both established EM functions and emerging trends that may influence the role of EM in EII decarbonisation. The review followed the PRISMA methodology [34], ensuring a transparent and replicable process for selecting relevant studies.

The methodology aimed to cover both standard research areas in EM and emerging fields that could influence future developments in EM. Articles were collected from Scopus and Web of Science (WoS) databases. A search of these databases was complemented by a snowball approach using Research Rabbit software (https://www.researchrabbit.ai/, accessed on 10 March 2025). Additionally, grey literature was included, recognising the dynamic nature of energy-related political and business trends. Key EU directives, reports, and strategies, including the EED recast, were examined for their relevance to the evolving role of EM. By exploring multiple databases and leveraging snowball techniques, the research ensured that no significant aspect of EM was overlooked in the context of EIIs. The search fields were “Title”, “Abstract”, and “Keywords”. Only publicly available articles with complete bibliographic data were included; languages were limited to English.

2.2. Keyword Strategy and Data Collection

The SLRs are usually made using a limited 5…6 keywords provided by the authors. We assumed that the authors’ keywords may not sufficiently convey modern industry trends, indicating that recent development areas are not adequately reflected in EM studies. While essential novel trends were identified in other industrial sectors, they are not conceptually embedded in current EM models. This suggests a gap in the literature where emerging trends and innovative practices have not been prioritised or deeply explored in EM research. Therefore, this research tried to ensure that the keyword approach captures all emerging trends, especially those not clearly articulated in the EM literature. A two-phase keyword strategy was applied to ensure the thoroughness of the literature review:

• First Phase (Standard Keywords): Focused on established EM concepts, the first phase involved searching using standard keywords, including “Energy Management (EM)”, “Energy Management Systems (EMS)”, “Energy Efficiency (EE)”, and “Energy-Intensive Industry (EII).” The intent was to map the current landscape of EM practices in EIIs and assess the extent of their implementation and effectiveness.
• Second Phase (Non-Standard Keywords): A more exploratory phase involved searching with non-standard keywords drawn from the EU energy innovation taxonomy and the EU taxonomy for sustainable activities. This phase aimed to identify emerging trends in EU-distance areas.

Combining these two phases enabled an inclusive understanding of how EM is currently positioned within EIIs and what needs to be accomplished to extend its impact (Figure 1).

The proposed conceptual framework’s structure is consistent with the recent literature that highlights the need for integrated, multi-level approaches to EM in complex industrial systems. For instance, Kilinc-Ata [35] highlights the convergence of digital tools and managerial strategies in shaping modern EMS within the EU’s decarbonisation agenda. Similarly, Chatzinikolaou and Vlados [36] stress the importance of cross-functional energy integration and reporting transparency as critical components of strategic EM in industrial sectors. These findings support the need to move beyond traditional, siloed efficiency strategies and toward a systems-based EM framework as proposed in this paper.

2.3. Data Analysis and Visualisation

Thematic analysis enabled the categorisation of recurring themes and the identification of gaps in current EM practices. Furthermore, VOSviewer software version 1.6.19 was employed for bibliometric analysis to cluster keywords based on their links within the processed database (Figure 2). The software’s clustering algorithm visually grouped keywords into the macro-trends identified in the study, including political, legislative, regulatory, technological, economic, and societal factors.

This query step identified the knowledge necessary to answer why and how the EM definition should be extended. It highlighted poorly represented or missing elements in EM research that are likely to shape its future scope and operations. This approach revealed areas with the most potential for growth and innovation by providing a comprehensive view of how EM research is distributed. Furthermore, bibliometric methods quantified the research gaps, offering a data-driven foundation for the EM redefinition.

Figure 2 illustrates the complex, multi-level governance environment in which EM operates, particularly in the context of the EU’s evolving climate and energy policy landscape. By mapping EM across horizontal (market-based instruments, sectoral regulation) and vertical (international, EU, national) axes, the figure highlights how fragmented policy instruments currently influence industrial decarbonisation. This conceptual layout underscores the need for a redefined EM framework that not only integrates digital and operational layers but also aligns structurally with diverse regulatory and financing streams. As such, the figure provides a policy-theoretical foundation for embedding EM into the EU’s multi-scalar governance model.

2.4. Practical Expert Input

In addition to the literature review, this study benefited from qualitative input from industry experts. During the Plenary Meeting of the Concerted Action on EED in Budapest (2024), 40 experts from various EU MSs participated in a Mentimeter survey and subsequent discussions. The survey was designed to identify the challenges and barriers to implementing Article 11, “Energy Management Systems and Energy Audits” of the EED recast. Table discussions that followed the survey allowed for a deeper analysis of these barriers.

3. Literature Review

3.1. Energy Management Environment

Our query found about 10,000 papers on the EM in the context of industry and manufacturing from 1970 to 2024—9886 in Scopus and 2769 in WoS. After merging the two sets and removing duplicates using Mendeley, the resulting set contained 9102 papers (Figure 3). However, after applying the procedure described in the Section 2.2, when “industry” was limited to “energy-intensive industry” and combined with EM or EMS, only 153 items were left. This tiny fraction indicates that EM is seldom analysed in the context of EIIs. Furthermore, EM, excluding EMS, is discussed in the context of EIIs in even fewer papers.

Visualising links and clustering by keywords from the analysed articles highlight several important aspects of the current EM research landscape and its application across various EIIs (Figure 2). The clustering reveals central themes such as “Energy Efficiency”, “Energy Management Systems (EMS)”, “Renewable Energy Sources (RES)”, and “Industry 4.0”. These central themes indicate the primary focus areas in the current research on EM. Surrounding the central clusters are peripheral topics like “Cost Benefit Analysis”, “Smart Grids”, “Energy Storage”, and “Environmental Impact”. Although not central, these topics support the main themes and indicate areas where EM research intersects with broader energy and sustainability issues.

The policy is strongly represented in the literature, highlighting the robust EM dependence on political frameworks. This indicates that effective EM strategies are heavily influenced by governmental policies and regulations, which shape the implementation and success of EM measures. The lack of alignment between EM and broader socioeconomic goals, such as the circular economy and the energy union, underscores the importance of integrating these EU policy objectives into the new EM framework. Climate policy and decarbonisation efforts within EIIs also indicate that EM is central to achieving long-term environmental targets. EM is frequently discussed when talking about climate issues, such as GHG emissions, carbon dioxide reduction, and global warming. This linkage emphasises the role of EM in addressing critical environmental challenges and mitigating the impacts of climate change through improved energy practices. Other environmental issues, including environmental protection and recycling, are also prominently mentioned. The new EM definition should encompass these strategic imperatives to ensure relevance and efficacy.

There are strong links between EM and the energy and power sectors. EM is often analysed within the context of RES developments, such as wind power and traditional energy issues, like energy demand, power markets, electric power generation, SGs, energy storage, and energy planning and analysis. The research interest is notably divided between future energy systems’ supply and demand sides, reflecting a comprehensive approach to understanding and optimising energy use.

Few papers focus on the EM application in specific EIIs, such as construction, manufacturing, chemical, iron and steel, gas, cement, pulp and paper, power, buildings, coal, petroleum, and automotive. However, even fewer articles encompass the entire spectrum of EIIs, suggesting a need for more comprehensive studies that consider the interconnectedness of different industrial sectors. Vital clusters group papers on EE, energy conservation, energy utilisation, and EMS, including the ISO 50001 family of standards. These clusters indicate focused research areas that enhance energy performance and standardise EM practices.

Surprisingly, technologies form weak clusters in the analysis, indicating that the diffusion of new digitalisation trends into EM, except for the IoT, is not explicitly visible. The development of new manufacturing technologies, such as Industry 4.0, is also not prominently marked, highlighting a disconnect between EM practices and cutting-edge technological advancements. As decarbonisation technologies advance, EM must also evolve to integrate these new technologies, facilitating the transition to low-emission energy sources and optimising energy flows within industrial processes.

Economic issues related to EM are tackled in the literature, covering topics such as energy markets, competition, cost-benefit analysis, economic analysis, sustainable development, investment, cost reduction, cost-effectiveness, and marketing. These papers underscore the economic dimensions of EM and the importance of financial considerations in implementing EM strategies. Economic drivers further reinforce the need for a redefined EM. The emphasis on cost reduction and enhancing industry competitiveness necessitates an EM approach to optimising energy use throughout the entire business chain. Moreover, the transformation of markets, especially with the rise of green products and energy services, calls for an EM definition attuned to market dynamics, including managing risks such as carbon leakage and capitalising on financing opportunities for EE investments.

The EM literature does not visibly represent non-energy EM benefits, such as reduced waste, improved productivity, reduced water consumption, reduced maintenance, a “green” public image, or an improved work environment. This gap suggests that the broader implications of EM on environmental sustainability and social well-being have not been sufficiently explored or emphasised. Organisational and labour factors are equally important. The need for solid managerial support and a cultural shift toward energy-conscious practices within organisations is evident. The novel EM definition should embed EM practices across all levels of the organisation, fostering proactive engagement and ensuring long-term commitment to EE. Developing a skilled workforce capable of managing new business models and advanced technologies is also critical. The redefined EM must prioritise human resource development to support the industry’s sustainability and competitiveness.

Research and development (R&D) should be crucial in advancing EM strategies. The progress in scientific computing, simulation, and modelling tools drives the need for a more sophisticated EM approach. Few analyses cover EM energy modelling and simulation methods, and even fewer address practical case studies. This indicates a need for more applied research demonstrating EM practices’ real-world effectiveness and practical implementation. The novel EM definition should enable these advances to improve the accuracy of energy and material flow analyses, enabling better decision-making and optimisation of industrial processes.

The literature review highlights several areas and emerging trends that are not yet the focus of intensive EM research in EIIs. While progress has been made in the “standard” EM fields, significant “white spots” remain, as confirmed by the findings. The diverse clusters identified emphasise that improving EM in the industry is a complex challenge, influenced by numerous interdependent variables, including political factors, technological advancements, economic conditions, contingency planning, environmental considerations, and human behaviour. This complexity underscores the need for a more comprehensive and integrated approach to EM research and practice.

3.2. Comparative Review of EM Definitions

This section examines various definitions of EM found in the literature, highlights key similarities and differences, and identifies critical gaps that must be addressed to develop a more comprehensive and future-oriented EM framework.

Over the years, numerous definitions of EM have emerged, each reflecting different perspectives, objectives, and scopes.

Table 1. Selection of definitions of EM in the literature.

Source	Definition
[37]	EM is “resources and the supply, conversion and utilisation of energy. It applies to resources as well as to the supply, conversion and utilisation of energy.” It involves monitoring, measuring, recording, analysing, critically examining, controlling, and redirecting energy and material flows through systems so that the least power is expended to achieve worthwhile aims.
[38]	At the most elementary level, energy management may be thought of as “task energy use”; e.g., the provision of as much energy as is needed, when it is needed, where it is needed, and with the quality required.
[39]	EM is the judicious and effective use of energy to maximise profits and enhance competitive positions through organisational measures and optimisation of energy efficiency in the process.
[40]	EM is the proactive, organised, and systematic coordination of procurement, conversion, distribution, and use of energy to meet the requirements, taking into account environmental and economic objectives.
[41]	Energy management is the usage and application of technology, including planning and operation of both production and consumption of energy with a view to enhancing energy efficiency of an organisation (VDI, 2007, p. 3).
[42]	EM is considered the proactive and systematic coordination of procurement, conversion, distribution, and use of energy within a company, aiming at continuously reducing energy consumption and related energy costs.
[43]	EM is the efficient and effective use of energy to maximise profits (minimise costs) and enhance competitive positions.
[44]	EM is the strategy of meeting energy demand when and where it is needed. This can be achieved by optimising energy using systems and procedures to reduce energy requirements per unit of output while holding constant or reducing the total costs of producing the output from these systems.
[45]	EM in production includes control, monitoring, and improvement activities for EE.
[13]	Systematic use of management and technology to improve an organisation’s energy performance
[46]	EM is considered a combination of energy efficiency activities, techniques, and management of related processes, which result in lower energy costs and CO2 emissions.
[47]	Energy management is understood as the sum of all measures and activities that are planned or executed in order to minimise the energy consumption of a company or institution. It influences the organisational and technical processes as well as patterns of behaviour and labour in order to reduce, within economical constraints, the consumption of energy and increase energy efficiency.
[48]	“Energy management involves the systematic tracking, analysis and planning of energy use. Energy management systems include energy management activities, practices and processes”.
[49]	EM is the collective term for all the systematic practices for minimising and controlling both the quantity and cost of energy used in providing a service.
[50]	EM is also the strategy of adjusting and optimising energy, using systems and procedures to reduce energy requirements per unit of output while holding constant or reducing the total costs of producing the output from these systems.
[51]	EM is the activity within the operation of industrial, commercial, and public sector facilities, monitoring, auditing, managing, and implementing improvements to systems that demand and consume energy.
[52]	EM is the systematic monitoring and control of energy-related activities.
[10]	“Energy management can be defined as the procedures by which a company works strategically on energy, while an energy management system is a tool for implementing these procedures”.
[53]	EM can be defined as the process of monitoring, controlling, and conserving energy in a system. It also means planning and management of energy production and consumption units.
[27]	EM comprises the systematic activities, procedures, and routines within an industrial company, including the elements of strategy/planning, implementation/operation, controlling, organisation, and culture, and involving both production and support processes, which aim to continuously reduce the company’s energy consumption and its related energy costs.
[54]	In general, EM can be understood as using means and methods aimed at sustainable, cost-effective utilisation of all available energy-related resources to improve the functioning of energy systems, and efficient use of energy, where the safe functioning of the energy system and energy supply reliability and quality are secured. (This definition of EM is taken from a narrower definition of Q-power management (Q-power is the reactive electrical power).
[55]	Energy management is a set of inter-related or interacting elements to establish an energy policy and energy objectives, and processes and procedures to achieve those objectives.
[56]	Energy management is a fusion of technology and management to increase the efficiency of production and enhance the results of output energy performance. It is necessary that management is related to renewable energy so that proper integration of energy systems can be achieved.
[57]	EM is the judicious and effective use of energy to maximise profits (minimise costs) and enhance competitive positions.

Source: Own work.

provides an overview of selected EM definitions from the literature, illustrating the evolution of the concept over time.

The analysis of the various definitions of EM reveals several vital similarities and differences that underscore the evolving nature of this concept. Across different sources, specific themes remain consistent, highlighting the systematic and organised nature of EM. For instance, many definitions, such as those from [37,42,45], describe EM as involving structured processes like monitoring, controlling, and optimising energy use. This systematic approach to EM is echoed by most sources, suggesting that EM is viewed as an essential tool for ensuring effective energy usage. Another recurring theme is the emphasis on EE and optimisation. Several definitions, including those from [39,44], stress that the reasonable and effective use of energy is vital to maximising profits and enhancing competitive positions. This idea of EE is central to most conceptualisations of EM, reflecting its importance in both reducing costs and improving operational performance. Economic considerations are also prominently featured in many definitions. Whether it is [43,49,57], EM is frequently associated with cost reduction and profitability. This economic dimension highlights the role of EM in ensuring that companies cost-effectively manage their energy resources, supporting broader goals of competitiveness and sustainability.

Moreover, environmental objectives are increasingly becoming integral to EM definitions. The Association of German Engineers [40,46] emphasise that EM must consider environmental and economic objectives, illustrating the growing importance of sustainability in modern EM practices. This indicates that EM is about reducing costs and achieving broader environmental goals, particularly in industries with significant energy consumption and GHG emissions.

However, there are notable differences in the scope and focus of EM definitions. Some, like [37], provide a broad view that encompasses the entire energy supply chain, including energy conversion and utilisation. Others, such as [49], take a more focused approach, concentrating specifically on minimising and controlling energy usage in providing services. This variance in scope suggests that while EM is universally recognised as crucial for managing energy resources, the specific objectives and focus areas can vary significantly depending on the industry or context. There are also differences in the integration of technology within EM. For example, [13] highlights the importance of incorporating technology into EM practices, suggesting that technological advancements are critical in improving energy performance. In contrast, [27] emphasise organisational and cultural aspects, such as integrating EM into broader corporate strategies and operations. This reflects the multidimensional nature of EM, which must balance technological, organisational, and cultural considerations to be fully effective.

Finally, while some definitions of EM narrowly focus on production and process efficiency, others take a more holistic view. Refs. [27,54] extend EM to include organisational culture and long-term sustainability, showing that EM must evolve to encompass broader strategic goals. This contrasts with definitions like those from [45], which are more narrowly focused on EE within production processes.

A comparative analysis of these definitions reveals several common themes:

• Systematic and structured approach—Most definitions emphasise EM as an organised and methodical process involving monitoring, controlling, and optimising energy use [37,42,45].
• Focus on efficiency and competitiveness—Many definitions highlight EM’s role in reducing energy costs, improving productivity, and enhancing market competitiveness [39,44].
• Economic and environmental integration—EM is frequently linked to profitability and sustainability, aiming to balance economic performance with environmental responsibility [40,46].

However, while these definitions share fundamental principles, they vary in scope and focus, ranging from narrow operational perspectives, such as process-level energy monitoring, to broader strategic frameworks, including organisational policies and long-term planning.

In general, this analysis reveals that while most existing EM definitions emphasise energy efficiency, they fall short of addressing supply chain-level coordination, policy integration, and digital transformation. Such conceptual limitations reinforce the need for a broader, systemic redefinition aligned with decarbonisation objectives.

The industrial sector’s dynamic brings new challenges, better knowledge of the present situation, and the ability to predict the future [58]. Recognising and understanding the numerous drivers that can bring profound shifts in EM is essential. Therefore, some papers relevant to the future of EM definitions were selected from the scrutinised literature for an in-depth analysis. This review explores key aspects of EM frameworks and highlights gaps related to a novel EM definition.

3.3. Relevance to the Novel EM Definition

The diversity of EM application areas has resulted in fragmentation and a sector-specific focus, hindering its development, particularly during periods of sector integration and digitalisation [59,60,61,62,63] and digitalisation [64,65,66,67]. Cooremans and Schonenberger [30] emphasise that fragmented EM practices result from the lack of a unified definition, leading to inefficiencies in implementation.

This fragmentation complicates the adoption of digital tools and cross-sectoral solutions, limiting innovation and the exchange of best practices across sectors [64,65,66]. Sector-specific EM practices exacerbate the problem by creating silos that prevent cross-sectoral collaboration. For example, industrial EM primarily focuses on production efficiency, whereas urban EM targets energy-dependent public services, resulting in inconsistent approaches and missed opportunities for integration and innovation. Ritchie [60] calls for cross-sectoral frameworks that promote collaboration and the standardisation of best practices to bridge these divides. Similarly, Hasan et al. [68] propose a characterisation-based framework for industrial EM services, emphasising the need to integrate decision-making and innovation across sectors. However, cross-sectoral frameworks that promote collaboration and the standardisation of best practices can bring significant added value by fostering innovation, improving resource efficiency, and enabling holistic solutions. Analysing EM across sectors—such as integrating SGs and RES—is indispensable for achieving comprehensive EM, ensuring synergy between industrial, urban, and energy network operations while enhancing flexibility and sustainability.

A couple of years ago, Lee et al. [69] forecasted several pivotal trends shaping the future of EM, including the development of EMS equipped with human intention feedforward control, the seamless integration of EMS with SGs, and the fusion of EM with energy efficiency-oriented production management. These trends aim to enhance the responsiveness and adaptability of EM systems, aligning them more closely with human operators’ intentions and the dynamic conditions of energy networks.

The novel EM framework should adopt a reflexive model that evolves, combining structural elements from traditional EM models with situated decision-making. This approach addresses the complexity and variability of industrial environments, emphasising flexibility and continuous improvement. Integrating technological innovations with human-centric operational practices will enhance EE and align with the broader goals of adaptability, sustainability, and digital transformation outlined in the novel EM definition.

Cross-sectoral integration and digital transformation are critical components of the proposed framework. These elements facilitate knowledge exchange, promote best practices, and enable holistic energy solutions across different energy domains, including SGs and RES. The absence of a standardised, universally accepted EM definition has created inconsistencies in practical implementation and hindered cross-sectoral collaboration. Without a cohesive framework, organisations experience inefficiencies and miss opportunities for synergy across industries, limiting the potential for consistent benchmarking and scalable best practices. These studies collectively underscore the importance of an integrated, cohesive EM framework that can evolve with emerging technologies, foster cross-sectoral collaboration, and drive the decarbonisation and sustainability goals central to the novel EM definition.

3.4. Emerging Digital Technologies in a New Approach to EM in EII

Knowledge—both scientific and technological, as well as sector-specific—is a critical driver of innovation. Extensive knowledge facilitates the adoption of both radical and incremental innovations, enabling industries to integrate new technologies and improve existing processes effectively. In [33], the authors present a comprehensive knowledge-based perspective on industrial EM, emphasising the shift toward data-driven, digitally integrated EM strategies.

3.4.1. The Role of Artificial Intelligence in Energy Management for Energy-Intensive Industries

The integration of AI into EM systems is revolutionising operational practices within EIIs. As industries strive to meet stringent decarbonisation goals and improve operational efficiency, the novel EM definition must incorporate advanced AI technologies to drive sustainable development and maintain competitiveness. Key technologies shaping this transformation include AI-driven optimisation, big data analytics, blockchain, digital twin (DT), IoT, and cloud computing. Each technology plays a distinct role in modernising EM frameworks.

AI presents significant opportunities for transforming EM in EIIs by enhancing predictive analytics, optimising energy consumption, and improving operational efficiency. Several studies highlight the transformative role of AI in energy optimisation, sustainability, and cost reduction, provided that infrastructure investments and technical challenges are adequately addressed.

AI-driven predictive models allow industries to anticipate energy demand fluctuations, optimise load balancing, and enhance system reliability. Refs. [70,71,72,73,74] emphasise how AI’s data-driven approaches improve forecasting accuracy, enabling industries to proactively adjust energy consumption patterns, thereby minimising waste and reducing operational costs. Suresh et al. [74] further demonstrate that AI-integrated DSM optimises energy distribution, ensuring efficient energy use within industrial networks. AI-powered systems can dynamically adjust parameters in response to real-time energy demand, reducing peak loads and stabilising industrial energy consumption.

AI can potentially optimise energy system operation and reliability, ensuring both techno-economic advantages and improved sustainability outcomes [75]. AI-based fault detection systems can identify inefficiencies, prevent system failures, and facilitate predictive maintenance, reducing downtime and operational risks. The authors [72] argue that AI enhances decision-making in industrial EM by integrating real-time data analytics with automated control systems, further promoting sustainable energy practices.

Integrating renewable energy into EIIs presents challenges related to variability, reliability, and storage optimisation. AI-driven algorithms enhance the efficiency of renewable energy systems, enabling their seamless integration into existing power grids [76]. AI can dynamically adjust power generation based on weather patterns, demand fluctuations, and grid stability requirements, ensuring optimal utilisation of renewable energy sources.

Swarnkar et al. (2023) in [77] highlight that AI applications span multiple stages of the renewable energy process, from site selection and design to operation and maintenance. By analysing historical performance data and environmental variables, AI can identify optimal locations for solar and wind farms, maximising energy production and cost savings.

3.4.2. AI-Driven Optimisation in Energy-Intensive Industries

AI-driven optimisation is pivotal in enhancing EE and reducing operational costs in EIIs. Machine learning (ML) and reinforcement learning (RL) algorithms optimise energy consumption by predicting usage patterns, dynamically adjusting system parameters, and improving DSM. For instance, Suresh et al. in [74] discuss the use of AI-driven energy forecasting and DSM to enhance consumer engagement and optimise energy distribution in energy-intensive sectors. These models help reduce energy costs while improving reliability and sustainability. Additionally, Keramati Feyz Abadi et al. in [78] highlight how AI algorithms improve EE in manufacturing by optimising scheduling, real-time control, and production processes, contributing to cost reduction and emission control.

3.4.3. Big Data Analytics in Energy Management

Big data analytics (BDA) has emerged as a transformative tool in EM, particularly within EIIs. The integration of BDA into EM within EIIs is advancing rapidly. Research highlights its potential to significantly enhance efficiency, sustainability, and operational optimisation. By processing and analysing vast datasets generated from industrial systems, BDA facilitates real-time monitoring, predictive maintenance, fault detection, and optimisation of energy flows.

BDA’s ability to process massive datasets enables industries to extract meaningful insights into energy consumption patterns, leading to more informed decision-making and optimised resource allocation. This integration supports the novel EM definition by promoting data-driven decision-making, enabling industries to transition from a reactive to a proactive approach to energy management.

References [78,79] emphasise that integrating BDA with AI is critical for demand prediction, SG management, and optimising energy consumption patterns in EIIs. Their research highlights how big data-driven insights enhance operational performance and foster EE across industrial settings. Ahmad et al. in [73] demonstrate that BDA facilitates predictive analysis of energy systems, allowing for better EM and even cyberattack prevention in EIIs. Their work underlines the dual role of BDA in enhancing energy performance and securing industrial systems from external threats.

Recent studies have proposed diverse methodologies and frameworks to effectively leverage BDA in EM in EIIs. Bevilacqua et al. in [80] propose a data analytics model tailored for IoT applications, integrating data from various sources to improve energy-aware decision-making in industrial environments. Their research identifies challenges in data integration and underscores the role of overall equipment effectiveness (OEE) in boosting resource efficiency and productivity. Zhang et al. [81] introduce a BDA framework (BDDAF) explicitly designed for energy-intensive manufacturing industries. This framework addresses the complexities of energy consumption in harsh production environments by proposing data acquisition, mining, and analysis methodologies. The study highlights the importance of integrating BDA with traditional energy consumption analysis to uncover hidden insights and support energy-efficient decision-making [81].

BDA’s application extends beyond isolated industrial processes to SGs, allowing industries to manage energy on a broader scale. Ghasemi and Rajabi [82] discuss analytical methods for applying BDA within SGs. Their work provides insights into how industries can leverage BDA to enhance system reliability and EE, crucial for EIIs adopting smart energy solutions. Sievers and Blank [83] review the exploitation of BDA technologies in the energy sector, focusing on SGs and building energy management. While not exclusive to EIIs, their insights into data-driven EM strategies are applicable to industrial contexts seeking to optimise energy efficiency through high-level data architectures.

3.4.4. Blockchain in Energy Management Systems

Blockchain technology offers significant potential for enhancing EM in EIIs by improving energy trading, decentralising energy systems, and integrating RES. Its secure and transparent nature addresses key challenges in energy transactions and decentralised system management. When combined with AI and the IoT, blockchain enhances trustworthy and efficient energy management, particularly in industrial energy trading and peer-to-peer energy-sharing networks.

Blockchain technology facilitates peer-to-peer (P2P) energy trading by enabling decentralised, transparent transactions without intermediaries. This capability is especially advantageous in EIIs, where large-scale energy transactions are common, and operational transparency is critical. Andoni et al. in [84] provide a comprehensive review highlighting blockchain’s potential to streamline energy trading processes, reduce transaction costs, and enhance transparency in EIIs. The study emphasises that blockchain can support the development of decentralised energy markets, allowing industrial entities to trade energy directly, thus improving efficiency and reducing reliance on centralised power providers. Sasikumar et al. in [85] propose a secure and energy-efficient consensus mechanism that combines AI and blockchain technologies for the IoT. This mechanism enhances transparency in energy transactions and promotes efficient resource management in EIIs, supporting operational and sustainability goals.

Blockchain is pivotal in decentralising energy systems, enabling industries to effectively manage distributed energy resources (DERs). The immutable ledger characteristic of blockchain ensures data integrity, essential for monitoring, verifying, and optimising energy consumption in decentralised systems.

Bhavana et al. in [86] discuss how blockchain enables decentralised EM, allowing industries to optimise energy use and integrate RES seamlessly. The study emphasises the importance of real-time data sharing and decision-making facilitated by blockchain in decentralised systems. The Waverley software [87] discusses how blockchain complements AI and IoT applications, enhancing transparency and security in decentralised energy management systems.

Jiang et al. in [88] identify critical obstacles, such as the need for efficient consensus mechanisms and clear regulatory frameworks, emphasising the role of interdisciplinary research in overcoming these barriers. Addressing these challenges is essential to fully unlocking blockchain’s potential in EM and ensuring its effective integration into industrial energy systems.

Integrating renewable energy into industrial operations presents challenges related to variability and reliability. Blockchain technology addresses these issues by ensuring transparent tracking of energy generation and consumption, enhancing the verification of renewable energy credits, and supporting sustainable energy practices.

Andoni et al. [84] highlight blockchain’s role in managing renewable energy within EIIs, demonstrating its potential to improve traceability and efficiency in energy transactions. Their study suggests that blockchain enables automated demand response and streamlined energy settlements, ultimately enhancing system reliability and market transparency.

3.4.5. Digital Twin Technology for Energy Optimisation

Digital twins (DT) are a virtual model of a physical system that mirrors its real-world counterpart through continuous data exchange. DTs serve as virtual replicas of physical systems, allowing for real-time monitoring, simulation, and optimisation of energy use. DTs play a crucial role in predictive maintenance, fault detection, and process optimisation within EIIs. By simulating operational conditions and analysing energy consumption patterns, DTs enable industries to anticipate equipment failures, optimise resource allocation, and reduce carbon footprints, all of which are pivotal for achieving decarbonisation goals.

A review discusses recent advancements in data-driven models for industrial energy savings, emphasising the role of digital twins and data infrastructures in optimising energy consumption [24]. Research explores the incorporation of digital twins and AI with surrogate modelling to optimise hybrid and sustainable energy systems, highlighting the synergy between these technologies in managing complex energy data [24]. Cakir et al. [89] introduce a reinforcement learning-based adaptive digital twin model designed for green cities, which can be adapted for industrial applications to enhance energy efficiency through real-time learning and adaptation. This research highlights the intersection of IoT, AI, and digital twins in optimising industrial energy management. Surrogate modelling for energy systems discusses the use of AI-driven surrogate modelling within digital twins to manage hybrid and sustainable energy systems, providing a framework for efficient energy management in EIIs [90].

Yu et al. in [91] emphasise the role of energy digital twins in industrial energy management, facilitating real-time optimisation and substantial reductions in carbon footprints. The authors discuss the lifecycle applications of DTs across industrial sites, highlighting their contribution to sustainability and EE. Kerkeni et al. in [92] explore how DTs, when integrated with AI, enable predictive maintenance and real-time diagnostics in manufacturing environments. Their study illustrates how DTs reduce unexpected equipment failures and optimise energy use, supporting the proactive EM principles central to the novel EM definition.

Recent studies offer diverse methodologies and frameworks for implementing DTs in energy management within EIIs, focusing on enhancing energy efficiency, sustainability, and operational optimisation. Yu et al. [91] conducted a systematic review to accelerate the understanding and application of energy digital twin technology in industrial settings. Their study presents a multidimensional classification framework for DTs, summarising their applications throughout the site lifecycle and proposing methods to reduce carbon and environmental footprints in local industrial areas. A study by Ma et al. [93] examines the integration of digital twin and big data technologies to propose a sustainable smart manufacturing strategy for EIIs. Their research underscores the importance of information management systems in achieving energy efficiency and aligning with sustainability goals. Billey and Wuest in [94] provide a literature review on the application of energy digital twins in smart manufacturing systems. The study identifies current gaps in research and suggests future directions for integrating DTs into energy management practices within EIIs. Perossa et al. in [25] perform a systematic literature review on DT applications for energy consumption management in manufacturing. Their work explores various features and characteristics of DT implementations, providing insights into their potential benefits and challenges in industrial energy management.

Data-driven approaches in sustainable manufacturing leverage advanced analytics, product lifecycle management (PLM), and real-time monitoring to optimise energy usage and reduce environmental impacts in EIIs. By systematically analysing energy consumption patterns, industries can implement more efficient production processes, enhance resource utilisation, and minimise waste, aligning with the broader sustainability goals to be outlined in the novel EM framework.

Rolofs et al. in [95] present a data-driven cleaner production strategy specifically tailored for energy-intensive manufacturing industries. The study emphasises the use of product lifecycle management (PLM) to optimise sustainability across various stages of production, from raw material acquisition to product end-of-life. By integrating data analytics with lifecycle management, the research highlights how manufacturers can achieve energy efficiency and sustainable resource utilisation while maintaining competitive performance. This approach directly supports the novel EM definition’s emphasis on leveraging data to drive sustainability and efficiency across the entire business chain.

The integration of DTs with cyber–physical systems (CPS) represents a significant advancement in industrial EM. When combined with CPS, which bridges the digital and physical domains, this integration facilitates flexible and adaptive energy management solutions in manufacturing environments. Färe et al. in [96] propose a conceptual framework that integrates digital twins with cyber–physical production systems to create flexible energy management solutions in manufacturing facilities. The study explores how the synchronisation of digital and physical systems can enhance real-time energy monitoring, fault detection, and predictive maintenance. This integration enables dynamic adjustments in energy consumption in response to fluctuating production demands, thereby strengthening both operational efficiency and sustainability.

3.4.6. Relevance of DT to the Novel Energy Management Definition

The data-driven cleaner production strategy by Rolofs et al. [95] emphasises the role of lifecycle data in achieving sustainable manufacturing, while Färe et al.’s [96] integration of digital twins and cyber–physical systems demonstrates the potential for real-time, flexible energy management in EIIs.

Both studies underscore critical aspects of the novel EM definition, which should advocate for a holistic, data-driven approach to industrial energy management that extends beyond traditional operational boundaries. These approaches address key components of the novel EM framework, including:

• Comprehensive Coverage: By incorporating energy management practices across the entire product lifecycle and integrating physical systems with their digital counterparts, these strategies ensure that all aspects of energy use are considered, aligning with the EED recast focus on comprehensive energy efficiency measures.
• Digitalisation and Real-Time Optimisation: The use of big data analytics, real-time monitoring, and predictive algorithms reflects the novel EM definition’s emphasis on leveraging digital technologies to optimise energy management processes.
• Sustainability and Decarbonisation Goals: Both approaches contribute to achieving decarbonisation targets by reducing energy consumption and emissions, supporting broader EU sustainability goals, and aligning with ESG (Environmental, Social, and Governance) reporting requirements.

While DT applications in EIIs are growing, broader research explores their role in smart energy systems and networks, offering valuable insights for industrial adaptation. Although not exclusively focused on EIIs, Aghazadeh Ardebili et al. [97] investigate the technological enablers, design choices, management strategies, and computational challenges associated with DTs in SGs.

The integration of DTs in EM is rapidly advancing, offering substantial improvements in energy efficiency, sustainability, and operational optimisation. The novel EM definition should emphasise proactive, technology-integrated energy management by enabling real-time monitoring, predictive maintenance, and data-driven decision-making through the adoption of digital twins (DTs). However, challenges such as data integration, system interoperability, and the need for standardised frameworks must be addressed. Continued research is essential to fully harnessing DTs’ benefits in industrial energy management, supporting the transition to sustainable and efficient industrial practices.

3.4.7. IoT and Sensor Technology for Real-Time Energy Monitoring

The IoT and sensor technology have revolutionised EM in EIIs by enabling real-time data collection, predictive maintenance, and process optimisation. When integrated with AI, IoT devices provide granular insights into energy consumption, facilitating precise energy optimisation, fault detection, and improved operational efficiency. The novel EM definition should emphasise digitalisation and proactive energy optimisation, aligning with recent research that highlights IoT’s transformative role in industrial energy management.

Goel [98] explains how IoT, combined with AI, facilitates real-time monitoring of energy consumption, enabling efficient load management and fault detection in EIIs. This integration leads to significant energy reductions while maintaining operational efficiency, underscoring the potential of IoT in driving sustainable industrial practices. IoT devices collect continuous, detailed data on energy usage, enabling industries to monitor and optimise energy flows in real-time. This capability allows for efficient load management, fault detection, and system optimisation, contributing to reduced energy consumption and operational costs. These technologies also play a critical role in enhancing energy and material efficiency in industrial processes, aligning with broader decarbonisation goals [99].

Designing a conceptual framework for industrial energy management systems (IEnMS) involves utilising IoT, big data, and data analytics to construct effective cyber–physical system architectures. This approach includes steps from data acquisition to the end-user decision-making process, demonstrating how such frameworks provide objective methodologies for selecting appropriate IEnMS tailored to specific industrial needs [28,33]. Bevilacqua et al. [80] propose a data analytics model integrating IoT data from various sources to enhance energy-aware decision-making in industrial settings. The study addresses challenges in developing data architectures due to the diversity of data sources and highlights the role of real-time analytics in improving energy management. Ref. [100] discusses IoT-based energy management platforms that enhance monitoring and control of energy consumption in industrial facilities. These platforms support real-time data acquisition and analysis, enabling informed decision-making to improve energy efficiency and reduce operational costs. The study by Ullah et al. [28] highlights the urgent need for a holistic IEnMS framework that leverages modern technologies such as the IoT, big data, and data analytics. While the AI-related EMS topics have been widely explored in the context of home energy management systems (HEMS), there remains a significant gap in research focused on IEnMS implementations.

Thilakarathne et al. in [101] introduce the concept of the Green Internet of Things (GIoT), focusing on reducing the energy consumption of IoT devices to maintain environmental sustainability. This paradigm is crucial for developing energy-efficient IoT solutions that minimise environmental impact while maintaining operational efficiency in industrial applications. Despite the significant advancements, the integration of IoT in EM faces challenges, particularly related to data security, interoperability, and energy consumption of IoT devices.

3.4.8. Cloud Computing in Scalable Energy Management

Cloud computing provides the necessary infrastructure to support AI and big data analytics in energy management, enabling scalable and flexible processing of vast energy datasets crucial for large-scale industrial applications. Its ability to optimise energy usage and improve efficiency has led to significant cost savings in energy-intensive industries. For instance, cloud-based energy management systems have demonstrated substantial reductions in energy costs in manufacturing processes [23,81].

One of the key advantages of cloud computing is its adaptability to fluctuating energy demands, making it particularly beneficial for energy-intensive industries [102]. Through efficient resource allocation and scheduling, cloud-based systems enhance energy efficiency, resulting in lower operational costs. Techniques such as virtualised cloud computing further contribute to economic and energy savings.

Beyond cost reduction, cloud computing also plays a pivotal role in environmental sustainability. By optimising energy efficiency and facilitating the integration of renewable energy sources, cloud-based solutions contribute to lowering CO₂ emissions and supporting global sustainability goals [103].

The growing integration of Industry 4.0 technologies within energy management systems underscores the importance of cloud computing and big data in the energy sector. As industries increasingly adopt these technologies, there is a greater need for expertise in leveraging cloud-based solutions for energy optimisation [104].

Cloud computing also enhances DSM in EIIs, helping reduce energy costs and improve EE. This is achieved through advanced technologies such as the IoT and cyber–physical systems, which enable real-time energy monitoring, dynamic load balancing, and process optimisation [23,81].

Furthermore, IoT-based cloud solutions leverage noninvasive sensors and robust communication networks to collect and analyse energy consumption data. This real-time monitoring capability provides actionable insights for industries seeking to enhance their energy management practices [105]. References [79,98] emphasise how cloud platforms support data storage, processing, and AI model deployment in real-time energy management applications. The scalability of cloud-based systems makes them particularly well-suited for energy-intensive industries, which generate and process large volumes of energy data.

3.4.9. Limitations of AI in Energy Management for Energy-Intensive Industries

Despite its potential, the adoption of AI in EM for EIIs faces several critical limitations, including technological, regulatory, and organisational barriers. A significant challenge in industrial AI adoption is the lack of expertise in AI implementation, data science, and machine learning models. Many organisations struggle to build AI-driven energy management solutions due to limited access to professionals with the necessary technical and domain-specific knowledge [106]. This gap hinders AI deployment and reduces confidence in its effectiveness for real-time energy optimisation and strategic decision-making.

Recent studies highlight the emerging skill and knowledge demands necessary for Industry 4.0 and EM, emphasising digital skills, big data analytics, virtual/augmented reality, cyber–physical systems, 3D printing, smart factories, and 5G networks [107,108]. These advancements raise concerns about whether IEEs are adequately prepared to meet the challenges of Industry 4.0 and effectively implement EM and energy efficiency initiatives. Their findings call for a novel EM definition, advocating for a systematic, interdisciplinary, forward-looking approach that leverages AI and digital solutions to optimise energy management in manufacturing and EIIs.

AI relies on vast and diverse datasets from IoT sensors, industrial control systems, and energy monitoring platforms. However, interoperability issues—stemming from heterogeneous data formats, legacy infrastructure, and lack of standardised protocols—impede seamless integration across industrial systems. Ensuring smooth data exchange and processing remains a major obstacle to AI-driven EM solutions [106].

Unclear regulatory frameworks surrounding AI-driven decision-making in energy systems create uncertainty for stakeholders. Decision-makers are hesitant to invest in AI technologies due to uncertain legal implications, compliance risks, and potential liabilities [109]. Additionally, ethical concerns—including data privacy, security risks, and algorithmic transparency—further complicate the adoption of AI in industrial energy management applications.

Organisations often struggle to quantify AI’s economic and operational benefits in managing variable renewable energy sources and industrial energy efficiency. A key challenge lies in estimating AI’s value-creation potential, particularly in cost savings, return on investment (ROI), and risk mitigation associated with renewable energy integration [109]. This uncertainty discourages industries from prioritising AI investments in EM frameworks.

3.4.10. Challenges

While AI enhances energy efficiency, predictive maintenance, and decision-making, significant barriers hinder its full implementation. Ref. [75] explores the unforeseen challenges associated with AI adoption, which may temper overly optimistic views regarding its seamless integration. These include concerns about data privacy, integration complexity, and the need for large datasets to train AI models effectively. Addressing these challenges is critical to ensuring that AI-driven EMSs are transparent, secure, and aligned with sustainability objectives.

Additionally, the energy consumption of AI itself must be considered, ensuring that the benefits of AI deployment outweigh its environmental footprint. Ref. [75] categorises these challenges based on common dependency attributes, offering strategic recommendations to mitigate them. The findings stress the need for multidisciplinary collaboration between AI developers, energy experts, and policymakers to create robust AI governance frameworks.

A major hurdle in AI-driven EM is the scalability of AI models and the integration of diverse data sources. Many EIIs operate legacy energy management systems that lack interoperability with modern AI-driven frameworks. Refs. [71,98] stress that ensuring robust, scalable AI solutions capable of handling large-scale, heterogeneous energy data is fundamental for AI-driven optimisation.

To be effective in energy management, AI models must handle the complexities and variabilities inherent in energy systems. Ref. [98] highlights that AI model robustness is essential for adapting to dynamic industrial environments. Furthermore, continuous improvement and validation of AI algorithms are necessary to maintain reliability, prevent bias, and ensure adaptive performance over time.

While AI presents substantial benefits for enhancing EM in EIIs, overcoming these challenges requires focused research and strategic interventions. Developing transparent AI models, strengthening data security, and establishing standardised frameworks will be critical for AI adoption. Additionally, addressing AI’s energy consumption is necessary to ensure its application aligns with sustainability goals.

3.4.11. Summation

AI technologies have the potential to revolutionise energy management in EIIs by enhancing forecasting, grid optimisation, demand-side management, and renewable energy integration. However, challenges such as data security, algorithm transparency, scalability, and regulatory uncertainties hinder widespread adoption. Additionally, AI’s own energy consumption must be managed to ensure sustainability. Despite these challenges, AI-driven solutions offer substantial benefits, including improved system reliability, predictive maintenance, and optimised energy use. AI also facilitates seamless renewable energy integration and enhances operational efficiency. The role of real-time data analytics and machine learning in DTs further strengthens predictive energy optimisation and adaptive control in complex industrial environments.

The integration of IoT and sensor technologies in EIIs has transformed energy management by enabling real-time monitoring, predictive maintenance, and system optimisation. The novel EM definition should emphasise the integration of IoT in enhancing energy efficiency and reducing operational costs by leveraging data-driven, proactive energy management frameworks. While challenges such as data security and system interoperability persist, ongoing research and technological advancements continue to drive the adoption of IoT in industrial energy management, contributing to sustainable and efficient industrial operations.

While AI presents game-changing opportunities for EM in EIIs, these barriers must be systematically addressed, ranging from technical limitations to regulatory and organisational challenges. Future research and industry collaboration should focus on:

• Developing standardised AI frameworks to enhance data interoperability;
• Providing regulatory clarity and AI governance models to facilitate adoption;
• Investing in AI education and workforce training to bridge the expertise gap;
• Demonstrating AI’s tangible value through pilot projects and case studies.

The novel EM definition should emphasise proactive, data-driven energy management, necessitating integrating advanced digital technologies, and should integrate AI technologies to enable real-time monitoring, predictive analytics, and automated decision-making. A comprehensive, technology-driven EM framework is essential to overcoming existing barriers and maximising AI’s role in driving sustainability, efficiency, and competitiveness in EIIs. The novel EM definition should focus on digital transformation and management of all energy resources to enable energy synergy within EIIs and to enable real-time monitoring, diagnostics, and optimisation of industrial processes, ensuring enhanced energy efficiency, sustainability, and operational resilience.

3.5. Research Gap

Despite the rapid advancement of AI, big data analytics, digital twins, blockchain, and IoT technologies in the field of EM, their integration within EIIs remains fragmented, reactive, and inadequately structured. Current literature predominantly addresses isolated technological applications, such as predictive maintenance, energy forecasting, and peer-to-peer energy trading, but fails to propose a cohesive, holistic EM framework that seamlessly incorporates these digital solutions into long-term strategic energy planning.

Furthermore, while several studies acknowledge technical challenges like data interoperability, system scalability, and AI algorithm transparency, there is a distinct lack of unified approaches that comprehensively tackle these issues within EM systems explicitly tailored for EIIs. Notably, existing EM frameworks often neglect broader sustainability objectives, including decarbonisation goals, regulatory compliance (e.g., alignment with the Energy Efficiency Directive recast (EED), and the environmental footprint of digital technologies themselves. This oversight limits industries’ ability to fully leverage emerging technologies’ transformative potential in achieving operational efficiency and sustainability targets.

The literature review indicates that while AI technologies have started to penetrate the field of energy efficiency, including EM in EIIs, their application remains fragmented and largely confined to pilot projects or specific use cases. There is a conspicuous lack of a conceptual framework that systematically embeds AI-driven technologies into EM processes. Without such a framework, industries struggle to transition from traditional EM practices—which are often compliance-driven and narrowly focused—to a proactive, data-driven strategy that leverages real-time optimisation, predictive analytics, and sustainable development principles.

The novel EM framework must integrate cutting-edge technologies such as AI-driven optimisation, big data analytics, blockchain, digital twin, IoT, and cloud computing to address this gap. These technologies collectively transform EM from a reactive process into a proactive, dynamic system capable of driving significant energy efficiency improvements, reducing operational costs, and supporting broader sustainability goals in EIIs. Additionally, this integration aligns with decarbonisation objectives, enhances regulatory compliance, and improves industrial competitiveness in an increasingly digitalised energy landscape.

Moreover, traditional EM frameworks tend to focus narrowly on energy efficiency within individual enterprises, neglecting the broader energy chain and its systemic interconnections. This fragmented approach limits the ability to conduct comprehensive carbon footprint assessments and hinders opportunities for cross-sectoral collaboration that are essential in the context of decarbonised industrial ecosystems. The absence of a unified EM framework also restricts industries from fully exploiting the potential of renewable energy integration, energy storage optimisation, and demand-side management (DSM).

Furthermore, the integration of advanced technologies such as AI, IoT, and big data analytics within EM frameworks remains insufficient, impeding both real-time optimisation and long-term strategic planning. Existing frameworks fail to adequately align with governance and policy directives, overlook the importance of standardised metrics for performance evaluation, and neglect the critical role of human and organisational factors in achieving energy management goals.

In conclusion, bridging these gaps requires a redefinition of EM that transforms it from a narrowly focused operational activity into a comprehensive, forward-looking strategy. The novel EM framework must incorporate technological innovations, foster collaboration across industrial sectors, and align with both decarbonisation and sustainability objectives. This redefined approach will ensure that EM becomes a strategic enabler of industrial sustainability, capable of navigating the complexities of modern energy systems while driving economic competitiveness and supporting global climate goals.

4. Novel Energy Management Concept

4.1. Fundamental Functions of Energy Management

In industry, the primary EM objective is to ensure the reliable, high-quality, and efficient use of energy while supporting the sustainable growth of companies [110,111]. In practice, the operational EM goal is to maintain seamless manufacturing operations and optimise energy use under emission constraints.

Lee et al. [69] have categorised EM functions into six essential groups.

Table 2. EM fundamental functions.

Function Group	Role	Example
Basic function group	Monitors the energy usage of the whole site and a single facility and achieves the management or energy-saving target.	Measure and control process EE; provide reports of energy use and efficiency; energy usage reporting system based on Web/Intranet/Java technology and an Oracle database; typical daily operation/real time; data records; load patterns recording.
Control function group	Based on the effective energy usage monitoring functions, various control functions for equipment, buildings, or factories were developed for energy usage control.	Peak load control; power quality control; industrial load shaping measures; machine soft start control; demand control display; billing control.
Analysis function group	Analysis of industrial processes, daily company operation, and factory or equipment operation, prediction of future energy usage variation, determination of control settings, or management strategies.	Forecast/predict future energy usage, optimise the controlling state, diagnose the operation conditions of the equipment, point out possible fault detection, and suggest the maintenance schedule for the equipment. Energy demand prediction and forecast; online prediction decision-making; energy consumption forecast; analysis of load forecasting; optimisation of energy supply; automatic maintenance scheduling. Optimal management and maintenance for the machine, reducing energy control strategy for power systems in industrial settings by fostering adequate maintenance. Optimised settings for equipment; mathematical optimisation model.
Management function group	Organises staff management, database management, and control functions.	Operation procedure recommendations; production system converted from a production-driven to a consumer-oriented activity/supply chain management; ISO 50001 for performing the Plan, Do, Check, Act (PDCA) loop. Energy saving performance measurement and verification by IPMVP; implementation of ISO 14001 (ISO 14001:2015-Environmental management systems) environmental management systems; EM intensity; model of the production for carbon emission reduction; process control and management systems; energy balance management system for supporting business processes; object-oriented relational database for increasing EE; total quality management-based energy conservation [69,112]. Applying wireless sensor networks (WSN) for knowledge-based management [113]. Data mining for understanding, processing, and modelling energy usage [81].
Advanced function group	Integrates analysis and control functions to achieve model-assisted control. Expert system/AI development is also included. Combines the model analysis and control functions to strengthen the EMS control function.	Mathematical programming for targeting energy usage and efficiency; EM with the assistance of Markov reward models; input–output analysis for the energy activities of the whole plant; model-assisted control in a product factory; energy management information model [69]. WSN for the industrial plant; EM matrix; enabling EM for planning energy-efficient factories [113].
Specific function group	Embraces all other applications.	EM with cogeneration system; establishing energy-technology complexes; seven approach to EM [111]; distributed control systems for energy saving; automatic commissioning system; modular energy system analysis; boiler control.

Source: Own work based on the taxonomy of [69].

outlines the various fundamental functions of EM systems, providing insights that underscore the necessity of redefining EM. The monitoring and control functions, including real-time data collection and process control, reflect the growing need for advanced technologies such as AI and IoT in optimising energy use across systems. Current EM systems are inadequate in integrating these cutting-edge technologies, emphasising the need for a more modern, optimised approach to EM that goes beyond basic EE to focus on comprehensive energy optimisation. The analysis and advanced functions underscore the importance of predictive models, real-time forecasting, and AI-driven decision-making to reduce energy loss and optimise system performance. These functions align with modern energy demands, where traditional EM approaches cannot often manage increasingly complex energy flows and system behaviours, especially in EIIs operated with their own distributed energy sources, e.g., RES and CHP, and supplied from SGs. The management and specific functions further stress the necessity for a holistic EM definition. Critical tasks like optimal energy supply management, process optimisation, and predictive maintenance highlight the need for EM systems to integrate advanced analytics and continuous data-driven improvements. This shift is critical for addressing broader sustainability and decarbonisation goals.

These comments demonstrate how existing EM practices are outdated, and a novel EM approach is crucial for aligning with technological advancements, regulatory requirements, and broader energy optimisation strategies. This EM redefinition would support organisational goals while driving technological innovation and contributing to global sustainability objectives.

Contrasting the current EM definitions with the required EM functions calls for an extension of the EM concept, which would better consume innovative opportunities and better suit the economic and sustainability objectives of the industry. To perform the EM functions effectively, we propose some aspects of EM that should be improved. The novel EM definition shall help perform these fundamental EM functions. Moreover, it shall embrace the functional requirements with the missing elements identified in the literature review and incorporate them into suitable wording.

4.2. Extended Concept of Energy Management

To address the identified areas for improvement, the future EM definition must evolve into a comprehensive framework that integrates advanced technologies, fosters long-term strategic planning, and aligns with sustainability goals while extending its scope across the entire business chain. The proposed novel definition incorporates these critical elements, effectively redefining EM to better reflect its expanded purposes, scope, and transformative potential: Energy management means the application of proactive techniques, procedures, and practices across the entire business chain of the organisation to improve energy efficiency by implementing improvement measures in energy generation, transformation, use, and storage, leveraging advanced digital technologies such as AI, IoT, and big data analytics; setting long-term objectives and strategies; continuously measuring, analysing, and reporting energy use using standardised metrics; monitoring relevant variable factors that influence energy demand and normalising performance indicators accordingly; integrating with other management systems; aligning with regulatory frameworks; monitoring progress; supporting demand-side management programs and ancillary services; and promoting transparency and stakeholder engagement. It aims to achieve economic and sustainable goals cost-effectively while ensuring energy security, resilience, and personnel comfort and safety, contributing to corporate social responsibility and driving energy services market transformation.

The proposed EM definition effectively encapsulates the multifaceted responsibilities and objectives of managing an organisation’s energy. This definition coherently incorporates many elements, aspects and objectives, like coverage of the entire business chain; a coherent approach to the whole energy transformation chain; a broad set of energy techniques, procedures and practices, energy use, transformation, storage, and generation, setting objectives and strategies; continuous monitoring of actual energy consumption, measuring progress; achieving economic and sustainable goals; and finally, ensuring energy security, resilience, and personnel comfort and safety. By applying proactive techniques, procedures, and practices across the entire business chain, the definition provides a comprehensive approach covering all aspects of EM—from generation to end use. This holistic perspective is crucial for organisations aiming to optimise energy use, reduce costs, and minimise environmental impacts.

By replacing “energy efficiency” with “energy optimisation”, the definition can be expanded to encompass all energy resources, extending beyond a sole focus on efficiency. This change aligns with the evolving practices of large industrial energy users, who increasingly develop their own energy capacities to counter high energy costs and reduce dependence on external sources. This broader perspective integrates energy loss reduction with optimising energy-related activities, such as production, load balancing, and storage, which are particularly relevant for enterprises utilising their own energy sources, such as RES. On-site RES capacity decreases reliance on fossil fuels, while efficient energy storage ensures that surplus energy is effectively utilised during periods of low demand. Load balancing shifts energy consumption to off-peak times, reducing dependence on inefficient and high-emission power plants. SGs facilitate real-time energy flow adjustments, and AI and IoT-enabled process optimisation further minimise energy losses. This approach is particularly crucial in grids with distributed energy resources, focusing on optimising local energy flows. DSM also provides further opportunities to improve EM [114].

5. Discussion

5.1. Future EM Roles

Contrasting the findings on the energy EM surrounding and the discussion about EM definitions, we can identify many aspects that the novel EM framework enables to include in future EM practice.

The redefinition of EM addresses significant gaps in its current conceptualisation, expanding its scope and functionality to meet the demands of a rapidly evolving industrial and regulatory landscape. The proposed future-oriented definition of EM integrates insights from existing practices while emphasising innovative approaches to enhance efficiency, sustainability, and competitiveness. The aspects identified for improvement underscore the need for this transformation and provide a roadmap for the novel EM framework.

Data are the main product of EM, which can be used for two primary practical purposes:

• Raw data for online monitoring and control of the technology process;
• Processed data as input information for decision-making, e.g., in accounting, investments, benchmarking, and documentation.

Managing data complexity is a major challenge in energy management. Energy management systems collect large volumes of data from diverse sources such as sensors, meters, and various control systems. This influx of information can lead to data overload, making it difficult to analyse effectively, extract actionable insights, and support informed decision-making. Reliable and consistent data are a cornerstone for achieving a successful energy transition as they ensure market certainty and support informed political decision-making throughout the process [5]. Enhancing the quality, interoperability, dissemination, and timely availability of energy data and statistics within the EU is essential to meet these objectives.

5.1.1. Focus on Governance and Policy Alignment

EM operates across multiple levels—national, regional, local, and organisational—each shaped by distinct governance frameworks, policies, and tools that influence energy efficiency (EE) outcomes [115]. Governance, encompassing legislative frameworks, institutional arrangements, and coordination mechanisms, is pivotal to ensuring the effective implementation of EM strategies. Steuwer [116] defines EE governance as a combination of these elements, providing the structural support, incentives, and compliance mechanisms necessary for driving EM adoption and monitoring progress. However, many traditional EM definitions fail to explicitly incorporate governance aspects, disconnecting organisational practices and overarching policy objectives. This gap becomes increasingly significant as industries face mounting pressure to align with international sustainability regulations and decarbonisation goals.

At the national level, EM is driven by comprehensive policies and legislative measures aimed at steering the country’s entire energy landscape. This includes formulating energy and climate laws, implementing emissions trading schemes like the EU Emissions Trading System (EU ETS), and nationwide public awareness campaigns. Such initiatives are designed to promote EE and raise awareness, thereby influencing broader economic and environmental outcomes.

Regionally, EM is tailored to address specific characteristics of an area, such as local energy generation structures, trade dynamics, and grid transmission efficiencies [117]. Regional energy policies often reflect these localised conditions, ensuring that energy strategies are both efficient and contextually appropriate. For instance, regions with abundant renewable resources may focus on integrating RES into their energy mix, while areas with older infrastructure might prioritise grid modernisation efforts. Studies highlight how regional governance mechanisms can effectively balance local needs with broader national energy goals, promoting sustainable development at multiple scales [118].

At the local level, EM initiatives focus on community-based efforts to address immediate energy needs and leverage local resources. Municipal energy planning, local energy cooperatives, and district heating systems are examples of how local governments can foster sustainable energy practices [119]. Local authorities often play a crucial role in implementing EE measures, facilitating public engagement, and encouraging behavioural changes among residents and businesses. The integration of EM into urban planning and smart city initiatives further underscores the importance of local governance in achieving sustainability targets [46].

Within companies, EM is driven by internal policies focused on profit maximisation, regulatory compliance, and the cultivation of a sustainable public image. Tools such as demand-side management (DSM), virtual power plants (VPPs), Industry 4.0 technologies, and SGs are leveraged to optimise energy use and integrate sustainable practices into operations. However, the translation of national or regional energy policies into actionable strategies at the company level often faces barriers, such as financial constraints, lack of technical expertise, and organisational inertia [120,121,122]. Several studies have identified the low strategic priority of EM in industrial companies as a significant barrier to EE, exacerbated by insufficient market signals and political will to enforce stricter regulations [123,124]. Moreover, the effective implementation of the redefined EMS framework should be supported by non-refundable EU financing instruments, such as the Modernisation Fund, the Just Transition Fund, or cohesion policy programmes, to help industries overcome investment barriers and accelerate widespread adoption.

The novel EM definition addresses these gaps by embedding governance and policy alignment into its core framework. By integrating governance elements at all levels—national, regional, local, and organisational—the redefined EM framework ensures that energy management strategies are technically sound and aligned with broader regulatory and societal objectives. This holistic approach enhances the ability of industries to navigate the complex landscape of energy policies, facilitating compliance with international standards and driving progress toward decarbonisation goals. The novel EM framework also promotes cross-sectoral collaboration and knowledge sharing, enabling organisations to adopt best practices and innovate in response to evolving energy challenges.

By considering EM across these various levels, a holistic approach is achieved, ensuring that EE and sustainability are addressed comprehensively and aligned with broader societal and economic objectives.

5.1.2. Expanding the Scope of EM Across the Business Chain and Industrial Sectors

Expanding the scope of EM across the entire business chain and industrial sectors is essential for achieving comprehensive sustainability and efficiency. Traditional EM practices often concentrate on isolated processes within individual enterprises. This leads to fragmented efforts that overlook significant opportunities to reduce energy consumption and GHG emissions throughout the supply chain. This narrow focus can result in inefficiencies and missed opportunities to mitigate energy consumption and GHG emissions across the entire business chain [125]. A comprehensive EM approach is essential for accurate carbon footprint assessments, particularly in industries with complex supply chains [6,30].

Future EM strategies must encompass the entire business chain to overcome these limitations, especially in sectors where upstream processes—like mineral extraction and transportation—significantly contribute to energy consumption and carbon intensity. This broader scope enables precise lifecycle carbon footprint assessments, ensuring that environmental evaluations adequately consider upstream emissions. For example, in EIIs, where downstream asset acquisition is standard, a lifecycle perspective is crucial for sustainable decision-making and maintaining competitive advantages.

Regulatory frameworks should evolve to support this holistic approach. Transitioning from periodic energy audits to mandatory EMS implementation would ensure continuous and systematic EM for large energy users. Expanding EM practices to a wider range of organisations—through regulatory mandates or voluntary initiatives—would reinforce the importance of comprehensive energy management, aligning EM with sustainability goals and promoting decarbonisation across various sectors. This integrated strategy allows EM to address inefficiencies at every stage of the energy chain, enhancing its overall impact and effectiveness. While implementing a robust EMS reduces the administrative burden and cost of periodic energy audits by enabling continuous monitoring and improvement, it does not remove the need for independent audits. Periodic audits remain essential to verifying compliance and benchmark performance and ensuring that energy optimisation measures deliver the intended benefits.

Moreover, extending EM beyond internal operations aligns companies with corporate social responsibility (CSR) objectives, treating decarbonisation as an industry-wide concern. This approach fosters flexibility in policies at both corporate and national levels, balancing energy savings with the development of new capacities. Consequently, EM becomes integral to broader sustainability and competitiveness efforts, supporting objectives like environmental stewardship, energy transformation, circular economy principles, and water conservation [126].

By adopting this expansive and integrated EM framework, industries can effectively manage energy consumption and emissions throughout the entire business chain, contributing to global sustainability goals and enhancing long-term competitiveness.

5.1.3. EM Data in Reporting

Currently, EM data are not effectively and transparently communicated to all stakeholders. Future EM frameworks should provide data for cost-benefit assessments to public authorities and society, supporting non-energy statistics required by law, such as Environmental, Social, and Governance (ESG) reporting. Improved communication will facilitate objective evaluation of decarbonisation progress, enable data-driven discussions among stakeholders, and promote learning and expertise development [127].

EM data are critical in a bottom-up statistical approach, providing granular insights into energy use and efficiency improvements. For instance, the EU’s Corporate Sustainability Reporting Directive (CSRD) underscores the necessity of engaging all economic sectors to achieve a climate-neutral and circular economy. This directive will require about 50,000 companies to transparently report their impacts on nature, among other things, in the years to come (“Sustainable economy: Parliament adopts new reporting rules for multinationals”, European Parliament, 10 November 2022). Since energy consumption is integral to supply chains, reducing energy use and improving EE is fundamental. As a result, energy considerations must be seamlessly incorporated into sustainability reporting standards, particularly concerning environmental and climate-related matters, ensuring a comprehensive approach to sustainability and decarbonisation. Additionally, energy efficiency reporting should shift away from using tons of oil equivalent (toe) as the primary unit since this does not adequately capture the performance of RES, SG, and CHP systems. Reporting should instead use universally applicable units such as megajoules (MJ) and tons of CO₂ equivalent (tCO₂e), enabling comprehensive and transparent tracking of all energy optimisation measures.

Expanding the reach of EM requires promotion and raising staff awareness. Achieving these goals depends on uniform data-based reporting of EM program outcomes, fostering industrial clusters, supporting community roles in energy initiatives, and invigorating training programs on EM. Enhancing information availability, transparency, and consistency regarding EM impacts and practices is vital. Additionally, recognising that companies are implementing and maintaining EMS is critical in promoting CSR [128].

EM data have an enormous role in ESG reporting. Organisations currently reporting on ESG matters employ a variety of standards and frameworks that encompass both narrative descriptions and measurable data. EM plays a pivotal role in ESG reporting by providing quantifiable data on an organisation’s energy consumption, efficiency initiatives, and environmental impact. These data are essential for stakeholders, including investors, regulators, and customers, who increasingly prioritise sustainability and responsible resource management. Standardised frameworks, e.g., ISO 50001, enable organisations to systematically monitor and improve their energy performance, supporting ESG objectives. Moreover, accurate EM data enhance the transparency and credibility of ESG disclosures, facilitating informed decision-making and demonstrating a commitment to environmental stewardship. As noted in the Microsoft Sustainability Report, “GHG emissions accounting and ESG data management are crucial for sustainability and ESG reporting.” [129].

Incorporating robust EM practices into ESG reporting aligns with regulatory expectations and positions organisations to capitalise on opportunities associated with sustainable operations, such as cost savings, improved reputation, and competitive advantage. As highlighted by Plante Moran, “ESG, the focus on environmental, social and governance impacts of a business, is gaining attention from investors, communities, and regulators. For energy companies, the evolving reports are bringing significant challenges—and opportunities”.

Future EM will make data analysis available to all staff, creating an energy-saving-involved workforce. Additionally, anonymised insensitive data will be publicly available for political decisions, research, and environmental control. This openness will enable public monitoring of decarbonisation efforts, providing additional motivation for organisations.

The increasing reliance on EM data necessitates a careful balance between transparency and robust data protection to maintain trust and ensure secure operations. As the energy sector becomes more digitalised, data processing, usage, and cybersecurity challenges have become more prominent. Addressing these issues is crucial to upholding the integrity and security of energy systems [26,130].

The extensive use of data in EM raises concerns about who processes this information and how it is utilised. Ensuring that data practices adhere to high ethical and privacy standards is essential to maintain public trust and protect individual rights.

5.1.4. Driving Long-Term Strategic Decisions and Sustainable Investments

Traditional EM frameworks have predominantly focused on short-term operational efficiency, often neglecting the critical need for long-term strategic planning. This limited approach restricts industries from making informed decisions regarding future technology investments, compliance with evolving emission regulations, and alignment with broader sustainability goals. As industries grapple with integrating RES, hydrogen-based solutions, and carbon capture and storage (CCS), EM must evolve into a comprehensive tool that supports sustainable, resilient, and competitive decision-making [27].

Future EM frameworks must leverage data for immediate operational control and strategic purposes such as technology investment planning, scenario analysis, and regulatory compliance. By utilising EM data effectively, industries can evaluate the feasibility of emerging technologies—including hydrogen economies, RES-based electricity, e-gas, and CCS—vital for achieving decarbonisation targets. This strategic use of energy data will guide industries in making high-capital investments, ensuring both short-term efficiency and long-term financial viability.

Beyond guiding technology investments, EM must facilitate the integration of long-term energy system solutions. This includes optimising diverse energy mix technologies and adopting electricity, heat, and hydrogen storage solutions. These integrated approaches enhance industries’ capabilities for reliable energy planning while also enabling them to contribute ancillary services such as demand-side management (DSM) and grid stabilisation. By embedding these solutions within EM frameworks, industries secure their energy supply and bolster the energy system’s overall reliability and resilience, aligning with sustainability objectives.

The transformation of EM from a reactive operational tool into a proactive strategic framework bridges the gap between immediate efficiency gains and long-term resilience. This evolution positions EM as a critical mechanism for industries navigating the energy transition, ensuring that energy use supports both environmental imperatives and economic competitiveness.

Moreover, EM frameworks incorporating strategic data utilisation enable industries to align with broader sustainability and governance initiatives. For example, the Principles for Responsible Investment (PRI), established in 2006, outlines six voluntary principles designed to integrate ESG considerations into investment practices [131]. EM data can directly support such frameworks, ensuring operational decisions are consistent with long-term ESG goals. However, it is important to note that supportive legal and financial frameworks are essential for stimulating investments in sustainable energy infrastructure. Without clear regulatory guidance and financial incentives, industries may struggle to fully embrace sustainable practices, even with robust EM systems in place.

5.1.5. Driving Energy Services Market Transformation

EM must actively contribute to transforming energy services markets by supporting EE programs and enabling resource acquisition initiatives that extend beyond short-term goals. For instance, EIIs can leverage EM data to participate in DSM programs, optimise energy use, and improve global competitiveness [128]. By fostering industrial clusters and promoting collaboration among companies, EM can create platforms for sharing best practices, enhancing local community engagement in reducing environmental impacts.

From a broader perspective, EM data are pivotal for driving market transformation, particularly in the energy-efficient machinery, apparatus manufacturing, and energy services sectors [132]. By contributing to the development of energy services markets, EM can evolve from a resource acquisition-centric role to one that actively supports DSM programs and fosters long-term EE improvements.

Additionally, EM programs that demonstrate significant energy and GHG reductions, verified using standardised methods, should receive priority in public financial incentive programs. This approach would further motivate industries to adopt advanced EM practices, contributing to broader sustainability goals.

5.1.6. Standardising Metrics and Benchmarking

Methodologies and metrics for calculating unit energy demand for specific products, such as a tonne of steel, shall rely on benchmarks derived from EM data [128,133,134]. Without such standardisation, there is a risk of significant assessment discrepancies due to varying methodologies, differing thermodynamic boundaries, unstandardised and inaccurate measurement methods, and inconsistent external conditions [135]. To address these issues, it is essential to standardise energy-saving reporting methods to capture GHG benefits and ensure comparability across sectors reliably.

The effectiveness of EM practices is often hindered by the absence of standardised measurement and verification (M&V) protocols and energy metrics. The lack of uniform energy performance indicators (EPIs) creates inconsistencies in benchmarking, making it difficult for industries to assess EE improvements and quantify GHG reductions transparently and comparably [136]. Without standardised methodologies, organisations struggle to evaluate energy savings reliably, limiting their ability to make informed investment decisions and demonstrate progress toward sustainability goals. Normalising energy performance indicators concerning the values of the relevant variable factors is mandatory to ensure a proper means of setting energy optimisation targets and to track the progress over time accurately.

Future EM frameworks must adopt internationally recognised M&V standards such as the International Performance Measurement and Verification Protocol (IPMVP), ISO 50006 (ISO 14001:2015-Environmental management systems), ISO 50015 (ISO 50015:2014 Energy management systems — Measurement and verification of energy performance of organizations — General principles and guidance), and EN 16212 (EN-16212 Energy Efficiency and Savings Calculation, Top-down and Bottom-up Methods) to address these challenges. These protocols enable transparent and fair comparisons across industries while ensuring the accuracy and reliability of energy performance assessments. Standardised benchmarking is particularly crucial for compliance with frameworks like the EU ETS, where precise data are required to monitor emissions and energy consumption.

The adoption of advanced digital tools can further improve EM evaluation practices. Emerging technologies such as digital twin, predictive modelling, and machine learning algorithms offer innovative solutions for optimising energy use and refining performance assessments. Integrating these technologies with standardised reporting frameworks will enable real-time analytics and continuous monitoring, ensuring that industries can track improvements effectively and adjust strategies accordingly [136,137].

Furthermore, standardised metrics will support the recognition of organisations implementing advanced EMS, such as ISO 50001. This recognition can incentivise industries to adopt best practices, promoting the widespread adoption of EMS and fostering a culture of continuous improvement. By integrating standardised evaluation and reporting frameworks, EM can provide reliable benchmarking data, support innovation, and contribute to global sustainability objectives.

Finally, current EM practices do not always utilise the latest technologies for accurate and cost-effective measurements. Future EM frameworks must incorporate data measurement, transmission, storage, and analytics advancements while integrating with automation and digitalisation trends. This technological evolution will enable continuous evaluation and analysis of decarbonisation efforts, ensuring that EM remains a dynamic and effective tool for achieving long-term energy efficiency and sustainability goals.

Double counting is a well-recognised issue in EE statistics, occurring when multiple entities claim credit for the same energy savings, thereby inflating reported achievements and undermining the integrity of EE programs. This problem can arise from overlapping programmatic savings or misallocation of energy reductions between different initiatives. Reliable EM data can substantially eliminate the problem. Implementing comprehensive EM data collection across the entire production chain is crucial to mitigating double counting. By systematically tracking energy consumption and savings at each production stage, companies can prevent duplication and redundancy in data gathering and processing. This approach ensures that energy savings are accurately attributed, reflecting actual performance improvements and maintaining the credibility of EE statistics.

Moreover, standardised data collection methods and clear guidelines are essential to avoiding inconsistencies that may lead to double counting. The IEA emphasises the importance of organising energy data systematically to prevent such issues.

5.1.7. Integration with Organisational Management Systems

Integrating EM into existing organisational management systems, such as environmental management systems and quality management systems (QMS), is critical for embedding energy efficiency into the broader strategic and operational framework of an organisation [138]. This integration ensures that EM is not treated as a standalone function but as a cohesive element that aligns with environmental, quality, and operational objectives. By harmonising EM with these systems, organisations can streamline processes, reduce redundancies, and achieve optimised operations across multiple domains.

Furthermore, aligning EM with regulatory frameworks, such as ISO 50001 for energy management and ISO 14001 for environmental management, facilitates compliance and promotes the systematic adoption of best practices. This structured approach simplifies adherence to legal requirements and enhances overall organisational performance by fostering a culture of continuous improvement and sustainability.

However, traditional EM frameworks often remain loosely connected to other management and quality systems, which limits potential synergies. This fragmented approach can hinder comprehensive decision-making and reduce the effectiveness of energy-saving initiatives. The novel EM definition addresses this limitation by advocating for the seamless integration of EM with all existing management systems. This integration is essential for unlocking synergies that are crucial for achieving decarbonisation goals, enhancing energy efficiency, and maximising system-wide benefits.

The novel EM framework ensures that energy considerations are consistently factored into strategic decisions, operational processes, and quality initiatives by embedding EM within the broader organisational management infrastructure. This holistic approach supports decarbonisation efforts and contributes to long-term sustainability, resilience, and competitive advantage.

5.1.8. Supporting Organisational Culture and Human Factors

Traditional EM frameworks often overlook the critical role of human-centric practices, such as personnel engagement, behavioural change, and leadership, in driving energy efficiency and sustainability outcomes. However, incorporating these elements is essential for fostering a culture of continuous improvement and aligning energy objectives with broader organisational goals. Engaging employees at all levels, from top management to operational staff, ensures that energy efficiency becomes an intrinsic part of the organisational ethos rather than an isolated technical function.

Authors of [27] emphasise that elements of organisational culture in human energy management include education, training, motivation, and internal communications. These components are vital for creating a supportive environment where employees are empowered to contribute to energy-saving initiatives. Similarly, ref. [139] highlights that effective leadership and a positive organisational culture significantly influence employee motivation and energy levels, increasing engagement and overall well-being.

The novel EM definition addresses this gap by advocating for the seamless integration of human factors into energy management strategies. Leadership plays a pivotal role in setting the tone for energy-conscious behaviour, establishing clear policies, and championing energy initiatives. Supportive leadership within the organisation increases employee motivation and energy, fostering a sense of ownership and accountability.

Regarding data utilisation, current EM practices often limit access to a small group of technical or managerial staff, restricting the broader workforce’s ability to contribute to energy-saving initiatives. The novel EM framework proposes democratising data access, making energy consumption information available to all employees. By equipping staff with real-time data on energy use—particularly at the machine and floor levels—individuals can actively monitor and adjust their behaviours to reduce energy consumption. This transparency drives immediate energy savings and cultivates long-term awareness and engagement across the organisation.

On an individual scale, EM focuses on influencing staff energy behaviours through targeted incentives, feedback mechanisms, and the integration of energy considerations into daily tasks. For example, floor-level EM equipment allows operators to monitor machines’ energy usage, providing immediate feedback that can guide more efficient practices. This personal engagement contributes to tangible energy savings while reinforcing the organisation’s broader sustainability goals.

By embedding human-centric approaches into EM, the novel framework ensures that energy management is not solely a technical or managerial function but a shared organisational responsibility. This alignment between technical measures and human behaviour is crucial for achieving sustained energy efficiency, supporting decarbonisation efforts, and enhancing overall organisational resilience.

5.1.9. Contributions to Broader Sustainability Goals

Traditional EM frameworks have primarily focused on EE and cost reduction, often neglecting broader sustainability objectives such as corporate social responsibility (CSR), energy security, and circular economy principles. However, a more comprehensive EM approach is necessary as industries transition toward decarbonisation and long-term sustainability. While EE remains a core pillar of EM, the literature highlights the need for a more holistic perspective that incorporates environmental, social, and economic dimensions. Ates and Durakbasa [46] argue that modern EM must evolve to address energy performance, societal well-being, and environmental stewardship. This requires integrating circular economy principles to promote resource efficiency, waste minimisation, and lifecycle management of materials. Additionally, EM should account for energy security, ensuring a stable and resilient energy supply for industrial operations. Furthermore, personnel comfort and well-being should be recognised as critical components, alongside community engagement and social responsibility, positioning industries as active contributors to local and global sustainability efforts.

Future EM frameworks must integrate these broader goals while maintaining their traditional focus on efficiency and optimisation. Achieving this requires better alignment with policy and regulatory frameworks, ensuring compliance with global sustainability standards, such as the EU Green Deal and corporate ESG reporting requirements. EM must also embrace circular economy models, extending beyond energy use to include material efficiency, waste recovery, and renewable energy integration. Furthermore, integrating EM into corporate sustainability roadmaps strengthens CSR initiatives, fostering stakeholder engagement and demonstrating commitment to environmental objectives.

These emerging trends underscore a broader movement toward integrating advanced technologies and control mechanisms in EM. The novel EM definition embraces these developments by advocating for systems that are not only technologically sophisticated but also aligned with human factors and sustainable practices. By incorporating human intention feedforward control, EM systems can anticipate and respond to operator actions more effectively, leading to improved energy efficiency. The integration with SGs facilitates real-time communication and coordination between energy producers and consumers, optimising energy distribution and consumption. Furthermore, combining EM with production management ensures that energy efficiency is embedded within the core operational strategies of organisations, promoting a holistic approach.

The future EM should significantly depart from traditional approaches, expanding its scope beyond its current reach. Table 3 illustrates the key characteristics that distinguish current EM practices from future-oriented approaches, highlighting how the novel EM framework will foster decarbonisation and drive long-term industrial competitiveness.

5.2. Role of EM in Removing Energy Efficiency Barriers

Barriers to EE in EIIs have been well recognised [27,120,121,122,124,140,141,142,143,144,145,146,147]. Effectively addressing EE barriers and drivers requires an extension of the EM concept to harness novel opportunities better and meet economic and sustainability objectives in the industry [148].

Table 3. Characteristics of the current and future EM.

Characteristics of the Current EM	Characteristics of the Future EM	Impact on and Significance to Decarbonisation
Impact limited within the boundaries of the industrial enterprise	Encompasses the whole business chain. Enables better incorporation of Industry 4.0 concept. Increases transparency in programs supported by public sources, e.g., environmental funds. Serves meeting other mega objectives, e.g., industry competitiveness, mitigation of environmental harmfulness, far-reaching energy transformation, circular economy, and water preservation [149]. Demonstrates commitment to CSR.	Enables a holistic view of decarbonisation as an issue for the whole industry rather than a single company (organisation). Builds flexibility into policies at the company and national levels. Provides a level playing field for the demand and supply sides (energy savings vs. new capacity).
Loosely incorporated with other management systems in operation	Closely linked with all existing management systems in operation in the industry enterprise.	Enables synergies for the decarbonisation process. Focus on overall system benefits.
Data for the industrial company’s own use available to a limited circle of workers (staff)	Making data analysis available to the whole staff creates energy-saving-involved staff. Anonymised insensitive data are stored in databanks available publicly, e.g., for political decisions, research, and environmental pollution control.	Enabling public monitoring of the effects and progress of efforts toward decarbonisation is an additional motivation for organisations
Limited and ineffective use of the EMS-delivered data	Performs extensive analytics, e.g., AI, big data [150]. Equipped with auxiliary modules for economic and environmental assessment in post-processing.	Improves diagnostics, acquisition, and analysis of indicators describing the state of the decarbonisation process. Can comprehensively prove to what extent it decreases energy consumption and contributes to emission reduction. Promotes the emergence of innovative services, e.g., delivering integrated DSM options that include efficiency, demand response, EM, and self-generation measures through coordinated marketing and regulatory integration. Ensures appropriate storage and access to data (free databanks). Unlocks benefits through increased connectivity beyond one’s company to suppliers, other end users, business operations, other facilities, and the energy market (smart manufacturing) [151].
Use of energy data mainly limited to operational energy control and decisions	Energy data are also used in multicriteria decision-making of strategic value, e.g., technology change or large investment. Enables maximising operational and long-term profits.	Possibility to use data for current emission regulations and technology planning, considering the sustainability degree, e.g., decarbonisation. EM technologies facilitate different kinds of DSM programs.
Wide use of non-standardised M&V protocols and energy metrics	Standardisation enables fair and transparent comparability of energy metrics, e.g., energy consumption unit, GHG emission unit.	Improves conditions for benchmark analysis of the decarbonisation progress.
Data not used in communication to all stakeholders	Delivers data for cost-benefit assessment of the industrial company for public authorities and society, e.g., local communities. Delivers data for non-energy statistics as required by the law, e.g., to ESG [152,153].	Objectively evaluates the decarbonisation progress. Enables data-based discussions among all stakeholders. Enables learning from others to build expertise.
Not always uses the latest technologies to ensure accurate, cheap, and verified measurements and then analyses long-term practical solutions [135]	Uses the latest energy data measurement, transmission, storage, and analytics technologies. Coupled with other technological mega-trends like automation, ICT, and digitalisation.	Enables ongoing evaluation and analysis of the effects of actions conducive to decarbonisation.

Source: Own work.

Table 3. Characteristics of the current and future EM.

Characteristics of the Current EM	Characteristics of the Future EM	Impact on and Significance to Decarbonisation
Impact limited within the boundaries of the industrial enterprise	Encompasses the whole business chain. Enables better incorporation of Industry 4.0 concept. Increases transparency in programs supported by public sources, e.g., environmental funds. Serves meeting other mega objectives, e.g., industry competitiveness, mitigation of environmental harmfulness, far-reaching energy transformation, circular economy, and water preservation [149]. Demonstrates commitment to CSR.	Enables a holistic view of decarbonisation as an issue for the whole industry rather than a single company (organisation). Builds flexibility into policies at the company and national levels. Provides a level playing field for the demand and supply sides (energy savings vs. new capacity).
Loosely incorporated with other management systems in operation	Closely linked with all existing management systems in operation in the industry enterprise.	Enables synergies for the decarbonisation process. Focus on overall system benefits.
Data for the industrial company’s own use available to a limited circle of workers (staff)	Making data analysis available to the whole staff creates energy-saving-involved staff. Anonymised insensitive data are stored in databanks available publicly, e.g., for political decisions, research, and environmental pollution control.	Enabling public monitoring of the effects and progress of efforts toward decarbonisation is an additional motivation for organisations
Limited and ineffective use of the EMS-delivered data	Performs extensive analytics, e.g., AI, big data [150]. Equipped with auxiliary modules for economic and environmental assessment in post-processing.	Improves diagnostics, acquisition, and analysis of indicators describing the state of the decarbonisation process. Can comprehensively prove to what extent it decreases energy consumption and contributes to emission reduction. Promotes the emergence of innovative services, e.g., delivering integrated DSM options that include efficiency, demand response, EM, and self-generation measures through coordinated marketing and regulatory integration. Ensures appropriate storage and access to data (free databanks). Unlocks benefits through increased connectivity beyond one’s company to suppliers, other end users, business operations, other facilities, and the energy market (smart manufacturing) [151].
Use of energy data mainly limited to operational energy control and decisions	Energy data are also used in multicriteria decision-making of strategic value, e.g., technology change or large investment. Enables maximising operational and long-term profits.	Possibility to use data for current emission regulations and technology planning, considering the sustainability degree, e.g., decarbonisation. EM technologies facilitate different kinds of DSM programs.
Wide use of non-standardised M&V protocols and energy metrics	Standardisation enables fair and transparent comparability of energy metrics, e.g., energy consumption unit, GHG emission unit.	Improves conditions for benchmark analysis of the decarbonisation progress.
Data not used in communication to all stakeholders	Delivers data for cost-benefit assessment of the industrial company for public authorities and society, e.g., local communities. Delivers data for non-energy statistics as required by the law, e.g., to ESG [152,153].	Objectively evaluates the decarbonisation progress. Enables data-based discussions among all stakeholders. Enables learning from others to build expertise.
Not always uses the latest technologies to ensure accurate, cheap, and verified measurements and then analyses long-term practical solutions [135]	Uses the latest energy data measurement, transmission, storage, and analytics technologies. Coupled with other technological mega-trends like automation, ICT, and digitalisation.	Enables ongoing evaluation and analysis of the effects of actions conducive to decarbonisation.

Source: Own work.

The novel EM definition can play a role in overcoming the barriers to EE by introducing a comprehensive, data-driven, and proactive EM approach (

Table 4. Barriers to EE that are directly related to EM and the role of EM in removing the barriers.

Type of Barrier	Specification of Identified Barrier to EE	Role of EM in Removing the Barrier	Contribution of the Novel EM Definition
Political	Insufficient political commitment, poor policy coordination, and long-term planning create investment insecurity [154].	EM delivers data-based arguments for political decisions, ensuring fair allocation of GHG quotas and reducing costs of EE obligations (EEO) on competitiveness [29].	The novel EM definition promotes real-time data-driven decision-making, providing evidence for better policy alignment and public support for EE.
	Strong political lobbies in the energy supply sector [155].	Helps balance supply-side lobbying with transparent energy-saving data.	Data transparency helps shift focus from supply to energy efficiency.
Regulatory and Institutional	Energy tariffs and price regulations discourage EE investments, e.g., declining block tariffs [156].	EM helps design multi-tariff systems and exposes the benefits of EE oversupply capacity increases.	Novel EM frameworks could advocate dynamic pricing and flexible tariffs driven by real-time data and load management.
	Institutional bias toward supply-side investments; ineffective EEO implementation (e.g., white certificate schemes, voluntary agreements) [157].	Improves EEO management, ensures reliable energy consumption data for fair savings targets, and eliminates double counting [158].	The novel EM framework fosters a transparent and adaptive EEO system by enabling accurate consumption data and integrating best practices.
Market	High upfront costs and insufficient incentives for EE investments [65].	Reduces market entry barriers by promoting transparency, standardisation, and risk-sharing mechanisms like ESCOs and EPC [159].	The new EM definition encourages market-based energy solutions through enhanced cooperation in the value chain and data integration across all sectors.
	Split incentives, where the benefits of EE are not captured by the investors (e.g., landlord–tenant issues) [123].	EM clarifies the economic benefits and stimulates investment in energy-saving technologies.	The novel EM framework supports financial and operational alignment between stakeholders by offering data-driven solutions.
	Insufficiently developed markets for energy services; low competition and engagement [68].	Promotes the growth of energy services and green product markets through reliable data and risk mitigation strategies.	The novel EM concept fosters market development for green services and drives competitive pricing through digital integration and real-time monitoring.
Technical	Lack of market-ready, energy-efficient core technologies; operational risks of implementing EE measures [160,161].	EM provides real-time control and promotes the use of standardised KPIs to set achievable energy-saving targets [162,163].	With advanced technologies such as AI and IoT, the novel EM concept offers predictive maintenance and optimisation, reducing operational risks.
	Uncertainty regarding future technology and regulations, hindering optimal adoption timing for new technologies [164,165].	EM enables long-term forecasting and scenario analysis to adapt energy-saving actions in evolving regulatory contexts.	The novel EM ensures technological foresight through continuous integration with emerging trends, ensuring compliance and cost-effectiveness.
	DSM requires better load control and aggregation strategies (e.g., response speed, advance notice) [132].	EM enhances load control and optimisation for DSM through precise energy flow data.	AI-driven load management in the novel EM definition offers more flexible and responsive DSM strategies for industrial applications.
Economic	High transaction costs for determining EE measures and benefits, lack of financial resources, and long payback periods [160].	EM lowers transaction costs by providing precise data, improving economic assessment, and payback period reliability [135].	The novel EM definition includes multi-tier financial tools, e.g., ESCO and third-party financing models, to reduce costs and increase investment feasibility.
	Tendency to prioritise core business expansion over EE investments [160].	Integrates energy consumption metrics into broader business management systems.	The novel EM framework encourages energy-conscious business growth strategies by embedding energy metrics into decision-making.
Financial	Financial institutions perceive EE projects as risky, due to a lack of standardisation and familiarity with EE investment benefits [160].	EM increases reliability and transparency, attracting financing through data-driven risk reduction.	The novel EM definition supports innovative financing models such as public–private partnerships (PPP) and ensures compliance with green finance standards.
Information and Awareness	Lack of sufficient data on EE potential and energy-saving costs; information asymmetry between energy sectors and industries [166].	EM strengthens decision-making through comprehensive data analysis and reduces information asymmetry between stakeholders [167].	With big data analytics, the novel EM concept empowers industries with real-time data for energy optimisation and enables informed decision-making.
	Difficulty in convincing top management to invest in EE internally due to limited understanding of its long-term benefits [121,168].	EM provides clear, measurable results to facilitate top-management buy-in for EE projects [31].	The novel EM definition fosters a culture of energy efficiency by embedding energy goals within broader corporate strategies like CSR and ESG.
Human and Behavioural	Low awareness of energy-saving opportunities, mistrust of new technologies, and a lack of readiness to implement EE measures [29].	EM creates an “energy culture” by engaging staff at all levels, increasing awareness, and promoting in-house EM expertise [169].	The novel EM concept integrates human resources with AI-driven education and training modules, encouraging proactive energy-saving behaviours.
	Lack of skilled personnel to identify and implement EE measures; low commitment from top management [33,160,169].	EM strengthens internal capacity-building, appointing energy managers and ensuring EMS implementation.	The novel EM framework builds organisational energy expertise, ensures continuous training, and fosters staff participation in energy initiatives.

Source: Own work.

). It addresses several categories of barriers, starting with political challenges. The novel EM framework enhances political coordination by supplying reliable, data-based insights that inform policy decisions on industry development. It provides transparent data on energy use and GHG emissions and aids in fairly distributing emission quotas, such as those within the EU ETS. This transparency also mitigates the impact of EEO schemes, which often impose significant costs on EIIs.

The EU policy document analysis reveals a persistent misalignment between high-level regulatory objectives and the operational instruments currently available to industrial actors. While EU climate policy instruments such as the ETS, EED, and the RED II establish overarching decarbonisation targets, they do not provide sufficiently detailed or harmonised guidance on how EMS should be structured to meet those targets. This regulatory gap complicates the ability of EIIs to translate policy into actionable EM practices. Consequently, the findings underscore the necessity for an integrated EMS model that is fully aligned with the multi-level regulatory environment of the EU.

Regulatory and institutional barriers are similarly addressed through improved design of multi-tariff systems and enhanced management of EEO instruments. The novel EM definition provides detailed monitoring and reporting mechanisms that align with regulatory requirements, making compliance more straightforward for companies. Furthermore, giving detailed data supports the development of a more robust market for energy services, such as those offered by ESCOs, thus contributing to a more structured and competitive energy market.

In tackling market barriers, the novel EM definition enhances transparency and reduces transaction costs, thus encouraging investments in EE. It facilitates the development of markets for green products and energy services, helping companies navigate the complexities of energy pricing and market structures. This approach also helps resolve the issue of split incentives, where investors may struggle to reap the full benefits of EE improvements, by providing a clearer understanding of the long-term economic gains.

The technical barriers to EE are mitigated by integrating advanced digital technologies. These technologies enhance real-time monitoring and diagnostic capabilities, enabling more precise energy demand forecasting and more effective energy flow management. As a result, the new EM definition helps companies set realistic energy-saving targets and optimise their processes, contributing to both technological improvements and decarbonisation efforts.

Economic barriers, e.g., high transaction costs and the challenge of assessing the financial viability of EE investments, are addressed by providing comprehensive energy data. These data support more accurate economic assessments, helping companies better understand the payback periods and risks associated with EE investments. The novel EM definition encourages a more integrated approach to achieving economic and environmental goals by embedding EM within broader company strategies.

Financial barriers are alleviated by increasing the credibility of EE projects in investors’ eyes. The novel EM framework provides long-term analytics and transparent data, enabling financial institutions to more effectively assess the risks and benefits of EE investments. This transparency and innovative financing models, such as ESCOs and public–private partnerships (PPP), help attract more investment into energy-efficient technologies and practices.

In addressing human and behavioural barriers, the novel EM definition fosters a culture of energy awareness and engagement across all levels of an organisation. By promoting the appointment of energy managers and the implementation of EMS, the framework ensures that EM becomes an integral part of daily operations. This cultural shift encourages proactive behaviours and long-term commitment to energy-saving measures, enhancing the overall effectiveness of EE initiatives.

Information and awareness barriers are tackled by prioritising comprehensive energy data management. The novel EM definition strengthens decision-making processes and improves stakeholder communication by reducing information asymmetry between the energy sector and industry. Advanced analytics allow companies to set realistic energy-saving targets and monitor their progress, thereby improving the overall efficiency of their energy use.

5.3. Alignments of the Novel EM Definition with EED Recast

The EU has positioned EE as a central element of its climate and energy policy, primarily through the ambitious targets and provisions that the EED recast has recently introduced. The novel EM framework aligns closely with the objectives of the EED recast (EU/2023/1791), which emphasises the “energy efficiency first” principle as a fundamental aspect of EU energy policy. This principle mandates that energy efficiency considerations be integrated into all relevant policy and major investment decisions across energy and non-energy sectors.

The recast elevates expectations for EE across MSs, focusing, among others, on the role of EMS and energy audits (Article 11). The key difference is the shift from defining the article’s scope based on the nature of the enterprise, i.e., SME or non-SME, to setting thresholds based on energy consumption levels. Under the EED recast, large enterprises consuming over 85 TJ of energy annually are required to implement an EMS. In comparison, those consuming more than 10 TJ must undergo energy audits if they lack an EMS. This directive ensures a sector-wide approach, covering all enterprises, including those under the EU ETS or IPPC licence holders. A critical aspect of the EED recast is ensuring that energy savings exceed actions linked to free EU ETS allowances. Annex V of the EED recast stipulates that energy savings can only be counted if they surpass measures tied to these allowances. Article 10a of the EU ETS Directive adds that enterprises failing to implement EMS or audit recommendations face a 20% reduction in free allocation unless equivalent GHG reductions are achieved.

Table 5. Alignment of the novel EM definition with the EED recast.

Feature of the Novel EM Definition	Characteristic	Alignment with Articles of the EED Recast (2023) 1)	Comments on Alignment
Comprehensive EE Integration	Involves embedding EE considerations into all aspects of energy-related activities across the entire business chain.	Article 3: Emphasises the ‘energy efficiency first’ principle, mandating that energy efficiency considerations be integrated into all relevant policy, planning, and major investment decisions.	Advocates for a holistic approach encompassing all energy-related activities—generation, transformation, use, and storage—across the entire business chain, ensuring that EE is a fundamental component of organisational operations and decision-making.
Comprehensive Coverage	Ensures that all stages of the business chain are thoroughly considered, leaving no part of the energy process unaddressed.	Article 27: Emphasises optimising efficiency across all stages of energy flow. Article 5: Encourages comprehensive integration of energy efficiency measures across public sector operations, serving as a model for other sectors.	System boundaries are not defined in the EED. System boundaries can be viewed as an analysed system’s physical or organisational limits in the case of energy audits and EMS of the enterprise in question. The comprehensive coverage embedded in the novel EM definition aligns with the EED’s focus on optimising each stage of the energy process, ensuring thorough attention to energy efficiency opportunities across entire supply chains and industrial sectors.
Holistic Integration	Encompasses all energy use, transformation, storage, and generation aspects for an integrated solution.	Article 27: Supports integrated strategies to improve efficiency across the entire energy system, fostering system-wide synergies. Article 5: Advocates for integrating energy efficiency in all public services and infrastructure management facets to create a cohesive EM framework.	The holistic integration emphasised in the novel EM definition aligns with the EED’s call for coordinated energy efficiency efforts, ensuring that energy management is not compartmentalised but harmonised across sectors and organisational structures.
Proactive Approach	Emphasises proactive techniques, procedures, and practices, encouraging continuous improvement in EM.	Article 8: Mandates ongoing implementation of energy-saving measures, fostering a proactive stance. Article 11: Promotes establishing systems that proactively anticipate and address energy efficiency needs.	The proactive nature of the novel EM definition is in direct alignment with the EED recast’s emphasis on continuous energy savings and proactive management. This approach helps anticipate and mitigate potential inefficiencies before they become significant issues.
Objective and Strategy Setting	Provides a structured approach with clear goals and plans to achieve EE.	Article 4: Sets binding national targets, necessitating clear objectives and strategies. Article 11: Requires setting and implementing strategic plans within energy management systems to achieve efficiency improvements.	The structured approach to setting objectives and strategies in the novel EM definition supports the EED’s requirements for clear, binding EE targets. This ensures that EM practices are goal oriented and strategically aligned with broader energy policies.
Long-Term Strategic Planning and Sustainability	Focuses on aligning EM practices with long-term strategic objectives, including regulatory compliance and sustainability goals.	Article 8: Mandates that MSs set indicative national energy efficiency contributions toward achieving the EU’s EE targets, promoting long-term strategic planning.	Focuses on long-term strategic planning, regulatory compliance, and alignment with sustainability goals, including CSR and ESG principles, to ensure enduring organisational resilience and environmental stewardship.
Continuous Monitoring	Allows for real-time data collection and analysis, enabling timely adjustments and improvements.	Article 11: Stipulates ongoing monitoring and verification of energy performance. Annex VI: Minimum Criteria for Energy Audits: Defines requirements for detailed and continuous data collection during audits. Article 28: Ensures regular monitoring and reporting on implementing energy efficiency measures.	Continuous monitoring in the novel EM definition aligns perfectly with the EED’s emphasis on regular assessment and verification of energy efficiency efforts. This real-time approach ensures that any deviations from efficiency targets are quickly identified and corrected.
Implementation of Actions	Focuses on practical application by emphasising the execution of measures to increase energy efficiency.	Article 8: Demands the active implementation of energy-saving actions to meet annual targets. Article 11: The Action Plan includes technically and economically feasible recommendations that must be submitted to the enterprise’s management. Additionally, the status of the recommendations must be publicly disclosed in the enterprise’s annual report, alongside the implementation rate.	The focus on implementing actions within the novel EM definition is crucial for translating strategies into tangible results, strongly supported by the EED’s requirements for active measures to achieve energy savings. This alignment ensures that plans are not only created but effectively executed. The novel EM definition focuses on continuous monitoring, emphasising the need for structured planning. It ensures that energy-saving actions align with the transparency and accountability requirements, enhancing EM’s role in delivering data and fulfilling regulatory mandates.
Progress Measurement	Regularly evaluates the effectiveness of EM practices, providing feedback for continuous improvement.	Article 11: Includes provisions for regular assessment and improvement cycles. Article 28: Mandates transparent systems to monitor, verify, and report progress on energy efficiency initiatives. Annex XIV: General Framework for Reporting: Provides guidelines for systematically and consistently reporting energy efficiency outcomes.	The novel EM definition’s emphasis on progress measurement ensures alignment with the EED’s continuous monitoring and reporting requirements. This systematic approach allows for consistent evaluation and adjustment of EM practices to meet set goals.
Economic and Sustainable Goals	Aligns EM with broader organisational objectives, ensuring contributions to overall sustainability and cost-effectiveness.	Article 3: Energy Efficiency First Principle: Advocates for prioritising cost-effective energy efficiency measures in policy and investment decisions. Article 30: Obliges MSs to ensure public funding and access to appropriate financing tools. Supports promoting sustainable and cost-effective EM practices through financial and technical support, aligning EM with broader organisational goals.	The novel EM definition is designed to align EM practices with broader economic and sustainability objectives, emphasising cost-effectiveness and long-term viability. By promoting energy optimisation through proactive measures and advanced technologies, the novel definition supports the effective use of financial resources, maximising the return on investment for energy efficiency projects. This alignment with Article 30 underscores the importance of targeted financial support and policy measures in achieving these goals.
Energy Security and Resilience	Ensures that EM practices contribute to the reliability and stability of the energy supply.	Article 27: Focuses on improving the reliability and resilience of energy systems. Article 11: Enhances energy security by optimising energy consumption and reducing dependence on external sources. Article 30: Supports investments that comply with established standards, ensuring quality and reliability.	By ensuring that EM practices contribute to the reliability and stability of energy supply, the novel EM definition aligns with Article 30′s goals of incentivising investments that enhance energy security. This aspect is essential for attracting private investments as it ensures that energy efficiency projects also contribute to the broader goal of maintaining a resilient energy infrastructure.
Personnel Comfort and Safety	Promotes a safe and comfortable working environment by ensuring EM practices do not compromise employee well-being.	Article 6: Public Sector Buildings: Ensures that energy efficiency improvements in buildings also enhance indoor environmental quality and comfort. Article 11: Energy Management Systems and Energy Audits: Includes considerations for maintaining or improving comfort and safety standards while implementing EE measures.	The novel EM definition’s consideration of personnel comfort and safety ensures that energy efficiency improvements are not achieved at the expense of working conditions. This focus aligns with the EED’s requirements to maintain high standards of comfort and safety in energy-efficient environments.
Certification and Standards Compliance	Aligns EM practices with regulatory frameworks and relevant standards, e.g., ISO 50001.	Article 11: EMS must be certified according to EU and international standards. Article 30: Supports investments that comply with established standards, ensuring quality and reliability.	The novel EM definition’s commitment to compliance with standards ensures that EM practices align with the highest quality standards, fulfilling the EED’s requirements for robust and verifiable energy efficiency measures.
Data-Driven Decision-Making	Focuses on continuous measurement, analysis, and monitoring to optimise energy use and achieve energy efficiency goals.	Article 11: Energy audits are required to identify and implement EE measures. Article 28: Emphasises the importance of accurate data for monitoring progress and making informed decisions Article 30: Encourages the mobilisation of private investments and cooperation between the EC and MSs.	Focuses on continuous monitoring, measurement; analysis aligns with Article 30′s emphasis on providing technical support. By ensuring that accurate and up-to-date data drive EM, the novel definition supports the development of targeted policy measures informed by reliable insights, making financial investments more secure and effective.
Enhanced Data Transparency and Stakeholder Engagement	Emphasises the importance of transparent data management and active communication with stakeholders.	Article 17: Establishes requirements for MSs to ensure that final customers have access to their energy consumption data and related information, promoting transparency and stakeholder engagement.	Prioritises transparency, stakeholder communication, and robust data management, facilitating innovative financing mechanisms and strengthening competitiveness by building trust and ensuring accountability in energy consumption and efficiency initiatives.
Integration with Other Management Systems	Ensures EM is integrated with other organisational management systems, enhancing overall efficiency and sustainability.	Article 11: Allows exemptions from energy audits for enterprises with a comprehensive EMS integrated with other systems. Article 4: Organisations must align their energy management practices with broader efficiency targets and strategies. Article 30: Encourages the mobilisation of private investments and cooperation between the EC and MSs.	The novel EM definition’s emphasis on integrating EM with other organisational systems ensures that energy efficiency is not treated in isolation but is part of a broader strategic approach. This integrated approach can make EE projects more appealing to investors, as they are part of a comprehensive plan that enhances overall organisational efficiency and sustainability.
Promoting Innovation and Technological Adoption	Entails integrating cutting-edge technologies into EM to enable real-time data analysis, predictive maintenance, and proactive energy optimisation, thereby enhancing operational efficiency and responsiveness.	Article 11: Supports the use of modern tools and methodologies for significant energy savings. Encourages the use of ICT and smart technologies to ensure the efficient operation of energy systems, aligning with the novel EM framework’s focus on technological integration. Article 28: Advocates for the use of advanced technologies in monitoring and reporting on energy efficiency improvements. Article 30: National Energy Efficiency Fund, Financing, and Technical Support: Supports investments that comply with established standards, ensuring quality and reliability.	The novel EM definition encourages the adoption of advanced digital technologies. Emphasises integrating advanced technologies such as AI, the IoT, and big data analytics to enable real-time data analysis, predictive maintenance, and proactive energy optimisation. Article 30’s focus on mobilising private investments can help fund the deployment of these technologies, which are critical for enhancing energy efficiency. By leveraging these advanced tools, organisations can achieve more significant energy savings, making the investments more attractive to private stakeholders.

Source: Own work. ¹⁾ Titles of EED Articles: Article 3. Energy Efficiency First Principle; Article 4. Energy Efficiency Targets; Article 6. Public Sector Buildings; Article 8. Energy Savings Obligation; Article 11. Energy Management Systems and Energy Audits; Article 17. Billing Information for Natural Gas; Article 27. Energy Transformation, Transmission, and Distribution; Article 28. Review and Monitoring of Implementation; Article 30. National Energy Efficiency Fund, Financing, and Technical Support.

presents the additional features of the novel EM definition and its alignment with Article 11 and other relevant provisions of the EED recast, ensuring that the novel EM framework is integrated effectively within the broader legislative context. We conclude that the novel EM definition aligns well with the EED recast provisions by including proactive management practices, setting and achieving EE objectives, continuous monitoring, implementation of efficiency actions, and progress measurement. It also focuses on economic and sustainable goals, energy security, resilience, and personnel safety.

The authors had the opportunity to examine the practical challenges of implementing Article 11 of the EED recast. Forty experts from all EU MSs thoroughly discussed the challenges during the Plenary Meeting of the Concerted Action on EED in Budapest in 2024. Through a Mentimeter survey, key challenges were identified, including the limited availability of accurate data and the lack of clear guidance on regulatory requirements. The complexity of certification processes and the absence of incentives for enterprises to pursue certification were highlighted as significant barriers to compliance with EMS. Additionally, the effective implementation of transparent and non-discriminatory criteria for energy audits is hindered by inadequate training for auditors, insufficient oversight mechanisms, and resistance from audit stakeholders. To address these challenges, strategies such as streamlining administrative processes, providing financial incentives, enhancing auditor training, and enforcing mandatory reporting requirements were suggested as effective measures to promote high-quality, cost-effective energy audits and encourage compliance with EM requirements. Then, a table discussion on EM identified further practical requirements that must be met to remove the implementation barriers of Article 11 effectively. First, robust mechanisms for data collection are essential to overcome technical challenges and improve data availability. This expert-driven process provided real-world insights that complemented the theoretical findings from the literature review.

Additionally, enhanced financial support is crucial for successful national-level data collection initiatives. Strengthening certification systems and raising awareness are necessary to ensure the effective certification of EMS. Improving awareness and enforcement mechanisms is vital to promoting energy audit compliance while streamlining administrative processes and enhancing auditor availability, which is critical for meeting energy audit deadlines. The quality and effectiveness of energy audits can be improved through educational initiatives, streamlined processes, and targeted training programs.

Furthermore, the successful implementation of Action Plans depends on securing resources, fostering collaboration, and simplifying procedures. Tailoring guidelines and enhancing communication are important for better energy and water consumption reporting. Lastly, enhancing monitoring mechanisms, establishing clear criteria, and providing better guidance are essential to ensuring that energy audits meet the required minimum standards effectively.

6. Limits of the Research

This study is exploratory and acknowledges several significant limitations that may affect the scope, representativeness, and long-term applicability of the findings. Due to space constraints, many essential topics could only be addressed superficially or were omitted entirely, for example, a complete analysis of the new technologies. Several issues are presented in a tabular format, even though they warrant in-depth discussion.

The synthesis draws on scientific publications, EU legal documents, and expert assessments. However, inconsistencies in the availability, granularity, and currency of data—particularly across different MSs—may limit the robustness of some generalisations, especially regarding national implementation capacities.

The research relies on available data and literature, which may have inherent limitations regarding accuracy, completeness, and timeliness. The rapidly evolving nature of technology and regulatory frameworks in the energy sector means that some data may become outdated quickly, potentially affecting the relevance of the conclusions.

The study predominantly focuses on the EU and its EIIs. Although the findings and recommendations may have broader applicability, the regulatory, economic, and technological contexts specific to the EU slightly limit the generalisability of the results to other regions or industries with different characteristics.

The proposed EM framework is designed at a pan-European level, but it may not be fully generalisable to all industrial sectors or regional contexts. Differences in regulatory practices, technological infrastructure, and institutional maturity across MSs could influence their adoption and effectiveness.

As the analysis is based on qualitative synthesis and expert judgment, it identifies correlations and structural relationships rather than causal mechanisms. The framework is thus interpretive in nature and does not support direct empirical inference about causality between EM practices and carbon mitigation outcomes.

The study does not provide a quantitative assessment of technological readiness or digital maturity at the national or regional level. Yet, such heterogeneity may significantly influence energy management capability and the uptake of integrated EM systems, particularly in less digitally advanced MSs.

The regulatory and technological landscape in the EU is evolving rapidly. Reforms to instruments such as the ETS, AI Act, or the Green Deal Industrial Plan could alter the context in which EM frameworks are implemented. This dynamic environment introduces uncertainties about the long-term applicability of EM in specific contexts.

The research also assumes specific regulatory stability and support for EE and sustainability initiatives in the EU and across the MSs. However, shifts in political priorities, regulatory frameworks, or enforcement mechanisms could alter the landscape for implementing the novel EM definition, potentially limiting its effectiveness.

Additionally, EM development is at different stages of maturity across MSs and industries. Various countries have adopted different approaches, face distinct barriers, and have other drivers for EM implementation [58]. As a result, the accumulated experiences are dispersed, making it difficult to demonstrate the full potential of EM in reducing energy use and GHG emissions, as well as its broader impact on the economy, environment, and society. Thus, the analysis represents an EU-averaged assessment rather than a detailed, country-specific evaluation.

Moreover, the study does not fully account for the diversity of organisations’ cultures and management practices across different industries. The adoption and success of the novel EM definition may be influenced by factors such as resistance to change, varying levels of top management commitment, and the existing energy culture within organisations.

The research focuses on the potential short- to medium-term benefits of the novel EM definition. Long-term impacts, including the sustainability and adaptability of the proposed practices to future technological and regulatory developments, remain uncertain and require further longitudinal studies.

While the EM discussion is concentrated on EIIs, many non-energy-intensive industries also struggle to improve EE by employing EM concepts. However, when discussing EM, it is crucial to distinguish between non-energy-intensive and energy-intensive companies as they face different challenges despite having the same objectives of reducing energy consumption and GHG emissions (

Table 6. Differences of EM implementation in non-energy-intensive and energy-intensive companies.

Item	Non-Energy-Intensive Companies	Energy-Intensive Companies
Financial Resources Available for EM	Typically minimal relative to turnover or revenues.	A substantial proportion of turnover or revenues allocated to energy management initiatives.
Cost of Energy in Total Costs	Significant and increasing, depending on the nature of the business.	In certain EIIs, energy costs can constitute up to 10% of total expenses.
Environmental Requirements	Generally low concerning GHG emissions.	Subject to stringent environmental regulations, including the European Union Emissions Trading System (EU ETS) for the majority of EIIs.
Decision-Making Process	Typically straightforward and expedited.	Involves lengthy, multistage decision-making processes due to the scale and impact of energy-related decisions.
Availability of Measuring and Control Equipment	Often limited to standard energy meters used for billing purposes.	Well-equipped with advanced smart meters and comprehensive energy monitoring systems.
Energy Audits	Encouraged among small and medium-sized enterprises (SMEs), as per Article 11 of the Energy Efficiency Directive (EED) recast. Often recommended and sometimes publicly funded in certain countries. Implementation of post-audit recommendations can be challenging without public financing.	Mandatory for large companies within the EU under the EED recast. Obligatory in several countries. Post-audit investments are undertaken following stringent financial profitability criteria.
EMS Implementation	Rarely implemented due to resource constraints and perceived lack of necessity.	Implemented in approximately 20–30% of enterprises, varying by industry, reflecting a higher commitment to structured energy management.
Human Resources and Expertise	Very limited availability of in-house trained staff. Rarely possess experts in energy efficiency or energy management.	Possess well-trained energy personnel. May lack specialists in energy efficiency, including advanced energy management strategies.
Technological Complexity of Energy Processes and Equipment	Generally not complex; machinery and equipment are typically standardised.	Involves complex technological processes with many non-standardised, large energy loads, aside from typical auxiliary equipment.
Awareness of Climate and Energy Issues	Generally low, though awareness is on the rise.	High awareness, driven by legal obligations and the significant impact of energy costs on operations.
Public Support and Incentives	Numerous supportive schemes available. Instruments for de-risking investments are needed to encourage energy management initiatives.	Supportive programs exist in select countries and sectors. Large R&D programs are in place for specific technologies pertinent to IEESs.

Source: Own work. Based on [171,172,173,174,175].

). For example, large companies often have more resources for implementing EMS, while SMEs may struggle with limited financial resources, human capacity, and outdated technological infrastructure. The IEA estimates that up to 60% of total energy savings can be achieved through less energy-intensive industries, including SMEs [170]. The division between non-energy-intensive and energy-intensive companies was prominent in the original EED (2012), where non-SMEs were subject to mandatory energy audits, while SMEs were only recommended to undergo audits. This division was removed in the EED recast, which replaced the criteria of financial turnover and employee numbers with energy consumption levels as the basis for energy audits and management requirements. Table 6 highlights the key differences in energy management implementation between non-energy-intensive and energy-intensive companies, emphasising variations in resources, regulatory requirements, and organisational practices.

To encourage wider EMS adoption beyond EIIs, it is advisable to consider a simplified or modular EMS framework for non-EII sectors and SMEs. Such a framework would maintain core principles of systematic monitoring and continuous improvement while scaling the scope and complexity to fit smaller organisations’ capacities.

The expert assessments underscore uneven institutional preparedness across MSs and the critical role of digital capabilities in EM adoption. These findings suggest that any standardised EM model must be flexible enough to accommodate different maturity levels while promoting harmonisation and consistency.

The authors did not attempt to provide detailed wording for a new EM standard, such as modifications to ISO 50001, but rather aimed to outline general directions for possible future changes.

7. Further Research

The limitations identified in this study highlight several avenues for future research to enhance the novel EM framework. Broadening the analysis to encompass diverse industries and geographical regions will provide a more comprehensive understanding of EM practices and challenges.

Investigating how governments can establish supportive regulatory environments that incentivise EM adoption is crucial. This entails examining the alignment of EM with national and EU-level policies, assessing the impact of subsidies and tax incentives, and understanding how regulatory changes influence EM practices in EIIs.

The rapid development of technologies such as AI, the IoT, and big data necessitates detailed technical analyses to integrate them effectively into the EM framework. Research should focus on how these technologies can enhance data collection, real-time monitoring, and process optimisation within EIIs. For instance, AI has been shown to reduce energy consumption in buildings by at least 8% through optimised heating and lighting systems.

As smart energy systems become more digitalised, managing extensive data becomes imperative. Future research should address data quality evaluation, processing, mining, security, privacy protection, auditing, sharing, and trading issues. Ensuring robust cybersecurity measures to protect against threats such as virus attacks, false data injection, and denial-of-service attacks is also essential.

Understanding the behavioural factors influencing energy consumption is vital. Studies should explore how internal factors like habits, values, attitudes, and interpersonal factors like social norms and comparisons affect energy efficiency improvements based on behavioural analysis.

Developing guidelines for integrating the novel EM framework within enterprises is necessary. This includes strategies for organisational change management, employee engagement, and the development of EM competencies. Case studies across different industrial sectors would provide valuable insights into the application and success conditions of the EM framework.

Conducting cost-benefit analyses and assessing the return on investment (ROI) of implementing the novel EM framework are essential. Research should explore the economic feasibility of widespread EM adoption, particularly concerning energy market pricing mechanisms and the development of new business models, such as Energy-as-a-Service (EaaS) or energy performance contracts (EPC). Industrial sectors, including energy, automotive, and technology, must fundamentally transform their business models to ensure long-term resilience and adapt to evolving disruptions [18].

Extending EM practices beyond individual enterprises to encompass entire supply chains could promote efficiency and sustainability on a broader scale. Studies should explore how EM can be applied across these networks and how stakeholder collaboration can enhance overall energy performance.

Assessing how EM contributes to broader social objectives, such as job creation, social equity, and community engagement, is important. Exploring the role of EM in promoting corporate social responsibility (CSR) will help understand its social and environmental benefits beyond energy savings.

Investigating how EM can serve as an innovation accelerator within industries is promising. By driving technological innovation and fostering a culture of continuous improvement, EM could support the development of new products, services, and operational models. Future studies should identify areas where Research and Development (R&D) efforts could contribute to the evolution of EM tools, methodologies, and technologies. Further development of the framework should focus on enhancing knowledge adoption at both the sectoral and organisational levels through multidisciplinary cooperation [33]. This approach will facilitate the integration of diverse expertise, ensuring that industries can effectively leverage technological advancements and best practices for improved energy management and innovation.

Future research must prioritise scalability, interoperability, and regulatory compliance to integrate AI fully within industrial energy management systems.

8. Conclusions

This article highlights the urgent need to redefine Energy Management (EM) to address the evolving challenges Energy-Intensive Industries (EIIs) face. While traditional EM frameworks have provided certain benefits, they are increasingly inadequate in supporting broader policy, economic, and societal objectives, particularly those related to EU industry decarbonisation. Existing EM approaches are often reactive and narrowly focused, lacking alignment with contemporary manufacturing technologies, advanced digital tools, and evolving environmental regulations. To remain relevant, EM must evolve into an integrated, comprehensive, and forward-looking framework that supports energy efficiency (EE) and sustainability goals across entire business chains. The novel EM definition introduces substantial advancements while retaining key elements from traditional definitions. A primary strength of the novel definition lies in its emphasis on improving EE through specific, actionable measures incorporating technological, behavioural, and economic changes, as mandated by frameworks such as the EED recast. The novel definition also emphasises human-centric factors, including personnel comfort and safety—elements often overlooked in traditional definitions.

The novel EM framework positions EM as a cornerstone of the transition to a low-carbon, digitalised, and sustainable industry. It broadens the scope of EM to encompass all energy-related activities—generation, transformation, use, and storage—across the full business chain. Unlike traditional approaches that focus primarily on internal operations, this extended framework enables organisations to incorporate external trends and signals, enhancing adaptability and responsiveness to global energy challenges.

While retaining its core focus on improving EE and reducing costs, the proposed EM framework introduces several novel elements that align with modern industrial challenges. These include the integration of advanced technologies such as AI, the IoT, and big data analytics to enable real-time data analysis, predictive maintenance, and proactive energy optimisation. The framework emphasises long-term strategic planning, regulatory compliance, and alignment with sustainability goals, including corporate social responsibility (CSR) and ESG principles. The novel EM framework becomes critical for achieving decarbonisation, energy security, and societal well-being by prioritising transparency, stakeholder communication, and robust data management.

Beyond energy and cost savings, this comprehensive approach addresses sustainable development goals, supports the EU’s climate targets, and aligns with key legislative instruments like the EED recast. The novel EM framework promotes resource efficiency and waste reduction within the circular economy and fosters better cooperation with SGs to enhance energy supply security. It strengthens manufacturing flexibility and resilience by integrating advanced data analytics with enterprise management systems while incorporating constraints such as economic viability, supply reliability, and human-centred requirements into optimisation processes.

A key strength of the novel EM framework lies in its ability to deliver accurate, transparent, and actionable energy data. These data are indispensable for operational control and strategic planning, enabling organisations to engage effectively with public authorities, local communities, technology suppliers, and financial institutions. Transparency in energy data facilitates innovative financing mechanisms, such as energy service companies (ESCOs), energy performance contracts (EPCs), and public–private partnerships (PPPs), while enhancing the competitiveness of EIIs. The global energy and materials sector exhibits the highest level of PPP engagement [18]. It also improves access to public financial support, balancing profitability with eligibility for public funding.

The effective implementation of the novel EM framework relies on EMS as its operational backbone. EMS translates strategic EM goals into actionable, measurable processes, ensuring systematic energy management. By integrating advanced digital technologies, EMS enables real-time monitoring, predictive maintenance, and continuous improvement in energy performance, empowering organisations to meet both economic and sustainability objectives.

In addition, securing access to non-refundable EU financing mechanisms is crucial for enabling industries to implement the comprehensive EMS framework outlined in this study, thereby ensuring alignment with the EU’s decarbonisation and competitiveness objectives.

A unified EM strategy across the EU must allow for differentiated implementation. In MSs with mature institutional infrastructures and advanced digital capacity, policy should support the rapid deployment of fully digital EMS, integrated with EU carbon pricing and reporting mechanisms. In contrast, countries with limited administrative capacity may require a phased approach, supported by technical assistance, targeted funding, and structured capacity-building programs. A modular EM policy model—comprising a core set of mandatory elements and adaptable national-level extensions—would promote harmonisation while accommodating institutional diversity. Additionally, establishing a pan-European Energy Management Observatory could facilitate transparency, benchmarking, and policy learning across sectors and jurisdictions.

Ultimately, the success of the novel EM framework depends on continued policy support, regulatory alignment, and active industry engagement. By fostering collaboration, innovation, and strategic planning, this comprehensive approach equips industries with the tools needed to navigate the complex energy landscape while achieving environmental, economic, and societal goals. The redefined EM framework serves as a dynamic, impactful instrument for advancing industrial sustainability and driving global decarbonisation efforts, ensuring that energy management remains at the forefront of the transition to a more sustainable future.