Data-Driven ESG KPIs Optimization: A Framework for Smart Buildings and Smart Factories

Spilotro, Claudia; Secundo, Giustina; Magaletti, Nicola; Zini, Ettore; Tognon, Chiara; Zerega, Angelo

doi:10.3390/su172310837

Open AccessReview

Data-Driven ESG KPIs Optimization: A Framework for Smart Buildings and Smart Factories

by

Claudia Spilotro

^1,*

,

Giustina Secundo

²

,

Nicola Magaletti

³

,

Ettore Zini

⁴,

Chiara Tognon

⁴ and

Angelo Zerega

⁴

¹

Department of Management, Finance and Technology, University LUM Giuseppe Degennaro, 70010 Casamassima, Italy

²

Department of Engineering, University LUM Giuseppe Degennaro, 70010 Casamassima, Italy

³

LUM Enterprise S.r.l, 70010 Casamassima, Italy

⁴

Deloitte Consulting S.r.l, 20122 Milan, Italy

^*

Author to whom correspondence should be addressed.

Sustainability 2025, 17(23), 10837; https://doi.org/10.3390/su172310837

Submission received: 24 October 2025 / Revised: 26 November 2025 / Accepted: 30 November 2025 / Published: 3 December 2025

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Abstract

The growing importance of Environmental, Social, and Governance (ESG) performance across sectors has accelerated the adoption of digital technologies for the continuous monitoring, analysis, and optimization of key sustainability indicators. This study investigates the role of data-driven infrastructures—spanning hardware components (such as Smart Meters, IoT sensors, and Power Quality Analyzers) and software solutions (including Energy Management Systems, Business Intelligence, Digital Twins, and Artificial Intelligence)—in enabling more transparent, adaptive, and evidence-based ESG governance. Despite this trend, the literature remains fragmented, often focusing on isolated technologies and lacking a holistic framework. This study employs a critical literature review to address this gap, guided by the research question: How does the integration of advanced digital technologies contribute to the effective monitoring and optimization of ESG Key Performance Indicators (KPIs)? This approach allows for a comprehensive understanding of the strategic, technological, and operational dimensions involved in scaling such solutions. Findings identify two major application domains—Smart Buildings and Smart Factories—where the integration of digital tools enables a critical shift from static reporting to dynamic, real-time governance, demonstrating tangible benefits for sustainability and resilience. The study concludes by presenting an integrated framework and outlining key implications for practice and theory.

Keywords:

digital technologies; artificial intelligence; ESG; KPIs; smart building; smart factory; internet of things; business intelligence; digital twin; energy management system

1. Introduction

Global challenges related to environmental, social, and governance (ESG) sustainability demand a thorough reassessment of the strategic role that Smart Buildings and Smart Factories can play in optimizing energy performance and achieving ambitious ESG objectives [1]. In recent years, the concept of ESG has become central to how companies and investors consider long-term value and responsibility [2,3]. It represents a way of looking beyond traditional financial metrics to consider how organizations interact with the environment, society, and their own systems of governance—addressing issues from climate change [4] and human rights [3] to board independence and ethics [5,6]. Together, these three pillars provide a framework for assessing an organization’s true sustainability and resilience.

The building sector accounts for a substantial share of global energy consumption—approximately 40%—and contributes significantly to greenhouse gas emissions, around 33% [7]. This highlights an urgent managerial and political need: decision-making must be informed by timely and reliable indicators that link daily operations to ESG outcomes. Smart Buildings have emerged as a key response, leveraging technologies such as the Internet of Things (IoT), automated control systems, and integrated software platforms to dynamically monitor and manage energy performance [1,8]. Within this context, monitoring energy-related Key Performance Indicators (KPIs) is critical for continuously evaluating and improving building performance [9]. This digital approach is critical not only for environmental gains (like CO₂ reduction) [10,11] but also for the social (“S”) dimension—enhancing occupant well-being and inclusivity [12,13]—and governance (“G”), by improving transparency and compliance with standards like ISO 50001 [14].

This study examines a wide range of hardware and software solutions that can be combined into an integrated ecosystem. Key hardware includes smart meters, IoT environmental sensors, and power quality analyzers (PQA) [15,16]. These are complemented by software tools such as Energy Management Systems (EMS), Business Intelligence (BI) platforms, Digital Twin (DTs) models, and Artificial Intelligence (AI) applications [17,18,19]. A similar paradigm shift is taking place in the industrial domain through the rise of Smart Factories (Industry 4.0), which also rely on IoT, cyber-physical systems, and AI-driven analytics to enhance energy efficiency and sustainability [20,21]. However, despite the availability of this rich technological toolkit, a critical practical challenge persists: how to translate these disparate tools into coherent ESG KPIs and concrete decision levers that improve performance and auditability. ESG KPIs are measurable and quantifiable metrics used by organizations to evaluate and report their performance in managing environmental impact, social responsibility and corporate governance practices. These indicators enable businesses to measure and monitor over time the progresses towards sustainability goals, manages relationships with employees and their satisfactions and communicate their commitment to ethical and responsible operations to stakeholders.

Despite the growing body of research, our analysis reveals significant gaps. Most studies address sustainability in a general sense or focus on isolated technologies (e.g., IoT or digital twins) [20,21]. There is a distinct lack of integration between these technological perspectives and the systematic monitoring of ESG-related KPIs. Critically, few contributions provide a comprehensive framework that combines hardware solutions (e.g., smart meters, sensors) with software platforms (e.g., EMS, BI, AI) to generate actionable insights aligned across all three ESG pillars [22,23]. This fragmentation creates a clear research opportunity to build a systematic, integrated model.

The methodology adopted in this study is based on a critical literature review (drawing from databases like Elsevier Scopus and Clarivate Web of Science) to address the central research question: How does the integration of advanced digital technologies contribute to the effective monitoring and optimization of ESG KPIs? Answering this question is useful for both practice and theory. First, it delivers an integrative framework that explicitly links advanced digital technologies to measurable ESG KPIs and to the operational decisions they inform in Smart Buildings and Smart Factories. This helps organizations move from static, ex-post reporting to real-time, data-driven governance of energy and sustainability performance. Second, it provides a technology-to-KPIs alignment map, thereby clarifying expected benefits for practitioners. Finally, the analysis advances literature by identifying research gaps and by outlining policy and governance implications to enhance transparency and stakeholder trust in ESG reporting.

The remainder of this paper is structured as follows. Section 2 provides the literature background on digital technologies and data-driven ESG KPIs management. Section 3 details the research methodology used for the critical review. Section 4 presents the results, including the framework. Section 5 discusses the implications of these findings, and Section 6 concludes with limitations and directions for future research.

2. Literature Background: Digital Technologies and Data-Driven Approaches to ESG KPIs Management

In recent years, the convergence of digital technologies and data-driven methodologies has fundamentally transformed how organizations monitor and manage ESG KPIs. These innovations provide real-time insights into energy consumption, resource utilization, and occupant comfort, thereby enabling more transparent reporting, improved performance, and alignment with global sustainability objectives. This section synthesizes the current state of key enabling technologies and explores their application in ESG KPIs management.

2.1. The Hardware Foundation: Data Acquisition

The digital ESG ecosystem is foundationally built upon hardware for acquiring granular data. Smart meters and distributed IoT sensors collect high-resolution data on electricity use, temperature, humidity, air quality, and occupancy in buildings and industrial settings. As demonstrated by Cano-Suñén et al. [24], IoT deployments in learning factory environments can capture both energy use and indoor environmental parameters, thereby enriching ESG datasets for analysis and benchmarking. Alongside these tools, PQAs have gained increasing attention as complementary instruments for monitoring energy performance. Rather than only measuring the amount of electricity consumed, PQAs provide information on the quality and stability of the electrical supply, which is directly linked to efficiency, reliability, and equipment lifespan [15,16,25]. When integrated into IoT-based monitoring frameworks, PQAs enhance ESG datasets with an additional layer of information.

2.2. The Software Ecosystem: From Data to Actionable Intelligence

Data collected by hardware must be processed by a sophisticated software layer to generate value. This layer includes platforms for management, business intelligence, simulation, and advanced analytics.

Software solutions such as EMS aggregate IoT-derived data into unified dashboards, offering real-time visualization, alerting mechanisms, and automated control strategies. Modern EMS platforms exploit cloud computing to achieve scalable data storage, interoperability with Building Management Systems (BMS), and remote access. Many now incorporate predictive analytics to forecast demand peaks and recommend demand-response strategies, including the evolution towards AI-enabled optimization loops that automatically adjust Heating, Ventilation, and Air Conditioning (HVAC) and lighting setpoints based on occupancy forecasts [26,27].

BI transforms raw data into actionable insights. BI platforms process heterogeneous datasets (e.g., sensor feeds, utility bills, and occupant feedback) using data mining, statistical analysis, and machine learning techniques to reveal trends, detect anomalies, and identify performance gaps. Briones et al. illustrate how BI supports smart grid operations by linking energy supply patterns to consumer behavior [28]. In ESG reporting, BI enables automated KPIs dashboard generation (e.g., energy intensity per m², CO₂e per occupant) and benchmarking against sectoral standards, helping ensure KPIs are “Relevant” and “Time-bound” according to SMART criteria (i.e., goals that are Specific, Measurable, Achievable, Relevant, and Time-bound).

DTs, as virtual representations of physical systems, combine historical, real-time, and predictive data to simulate operational scenarios. In ESG applications, DTs offer a comprehensive view of energy flows, occupant comfort, and emissions, supporting “what-if” analyzes to evaluate retrofits or behavioral interventions before implementation. While Grieves and Vickers [29] formalized the DT concept, its practical application is advancing rapidly. You et al. [30] demonstrated how DT-based energy system scheduling under renewable energy uncertainty can reduce carbon emissions by up to 15% in simulations. Similarly, Khan et al. [31] integrated DTs with surrogate modeling to manage hybrid energy systems, underscoring DTs’ value in optimizing energy flexibility and grid interaction KPIs.

AI and Machine Learning (ML) form the analytical backbone of data-driven ESG management. ML techniques, including regression, decision trees, and clustering, automate pattern recognition, anomaly detection (e.g., energy spikes), and predictive maintenance [32]. Deep learning models extend these capabilities to multivariate problems such as forecasting heating and cooling loads based on weather and occupancy trends [33,34]. Generative AI can simulate occupant behavior to test social KPIs like indoor air quality under varying ventilation strategies [35,36], while Natural Language Processing (NLP) further supports Governance KPIs by extracting insights from unstructured text [37]. Recent frameworks, such as those proposed by Rane et al. [38], emphasize using AI not just for optimization but to actively enhance the transparency and reliability of ESG performance management, creating a clearer link between data-driven insights and verifiable reporting.

2.3. The Key Challenges

The true value of ESG KPIs management lies in integrating these technologies into unified ecosystems: IoT for data acquisition, cloud EMS for centralized control, BI for KPIs visualization, DTs for scenario testing, and AI for predictive and prescriptive analytics. Billanes et al. [39] recently conducted a scoping review of such integrated, data-driven architectures in Smart Buildings, highlighting enhanced KPIs accuracy and increased stakeholder trust. This technological synergy supports all ESG pillars: minimizing environmental impact, enhancing occupant well-being, and fostering transparent governance through data-backed reporting.

Nonetheless, several critical challenges must be addressed. Interoperability remains a key issue: standard communication protocols such as Open Platform Communications Unified Architecture (OPC-UA) and Message Queuing Telemetry Transport (MQTT) are essential to ensure smooth data exchange [40]. Cybersecurity is equally crucial. Data integrity and privacy must be safeguarded [41], particularly for social KPIs that involve sensitive occupant information. Bridging the skills gap is also vital, as professionals need to be trained to effectively operate and maintain AI- and DTs-based systems [42].

3. Research Methodology

This research employs a critical literature review [43] to investigate how digital technologies facilitate the monitoring and optimization of ESG-related KPIs in built and industrial environments. This approach offers a comprehensive overview and nuanced examination of the current technological landscape and its applications. Table 1 presents the methodological flow of this critical review.

The critical literature review serves as the core analytical framework and was conducted to identify not only conceptual models and emerging technologies, but also documented practical implementations of ESG-oriented digital systems. In contrast to systematic reviews, which prioritize methodological exhaustiveness, the critical review focuses on interpretative synthesis, evaluation of high-impact contributions, and the integration of perspectives across disciplines [43]. To ensure breadth and scholarly quality, we searched Elsevier Scopus and Clarivate Web of Science in the Title/Abstract/Keyword fields using a two-part Boolean string that combines domain and performance terms: (“Smart Building*” OR “Intelligent Building*” OR “Cognitive Building*” OR “Smart Factory”) AND (“KPI*” OR “Key Performance Indicator*” OR “Building Performance”). The search covered 2000–2024 and included works in English and Italian. We considered journal articles, conference proceedings, technical reports, and book chapters with substantive relevance to KPIs and enabling technologies. Records were screened on title and abstract and then full text. Inclusion required explicit focus on Smart Buildings and/or Smart Factories, use or discussion of ESG-related KPIs and their monitoring, and coverage of digital technologies enabling KPIs acquisition/analysis/control (IoT, Smart Meters/PQA, EMS, BI, DTs, AI/ML). Exclusion applied to items outside scope or time window, lacking an abstract, or not in the specified languages. To complement the database results, we applied snowballing technique (also known as citation chasing) to ensure inclusion of relevant papers potentially missed by the initial search [44,45]. A key outcome of the critical review was the identification of real-world applications. To better understand these concepts, this research also presents case studies, based on secondary data collected through a desk-based analysis, as illustrative examples. After reviewing the scope, technological depth, and methodological transparency of each case, the selected examples were grouped thematically to reflect two distinct clusters:

Smart Buildings, referring to commercial, residential, and institutional facilities equipped with digital systems for energy and comfort optimization;
Smart Factories, involving industrial or manufacturing environments characterized by cyber-physical systems and data-driven operational control.

This two-cluster structure not only reflects the primary domains of application for ESG-related digital technologies but also allows for comparative analysis between the built environment and the industrial sector. Within each cluster, the selected case studies were analyzed based on several criteria, including the type of digital technologies employed (e.g., IoT platforms, AI/ML models, open-source systems), the specific ESG KPIs addressed (e.g., energy consumption, carbon footprint, system efficiency, equipment lifecycle), and the reported benefits and limitations,

The integration of critical literature review with case-based analysis offers a methodological bridge between theoretical inquiry and applied knowledge. On the one hand, the review frames the conceptual boundaries and technological trends relevant to ESG performance in intelligent infrastructures. On the other, the analysis of real-world implementations provides grounded evidence of how these technologies function in context, highlighting both opportunities and constraints.

In this way, the study addresses the central research question: How does the integration of advanced digital technologies contribute to the effective monitoring and optimization of ESG KPIs? by aligning conceptual insights with concrete, observable applications in the field. The resulting classification into Smart Building and Smart Factory clusters serves not only as an analytical device, but also as a framework for future comparative research and policy development.

4. Results

ESG energy KPIs provide a structured lens for our analysis, linking energy efficiency with environmental, social, and governance dimensions across systems and organizations (Appendix A). While translating complex operational data into a concise set of comparable indicators supports transparency, accountability, and decision-making, their effectiveness depends critically on the integration of advanced digital technologies. However, the mere adoption of these tools does not guarantee results; their efficacy is subordinate to their proper integration and the capacity to manage data complexity. The convergence of hardware components (e.g., IoT sensors, Smart Meters, PQA) with sophisticated software systems (e.g., EMS, BI, DTs, AI) forms the necessary, but often fragmented, digital backbone that is increasingly central to achieving energy efficiency and sustainability objectives.

The hardware data acquisition layer is the foundation of this ecosystem. IoT enables the deployment of sensors (temperature, humidity, occupancy, consumption), offering a granularity that, while powerful, introduces significant challenges in managing data volume and variety [46]. These devices allow for the calculation of contextual KPIs, such as energy use per occupant [47]. Concurrently, Smart Meters and PQA provide high-frequency aggregated data (often 15-min intervals) and power quality insights (e.g., Power Factor, Total Harmonic Distortion) [48]. The primary challenge, however, lies not in collecting individual data points, but in ensuring the interoperability, synchronization, and validation of these heterogeneous sources. To attempt to consolidate and leverage these data streams, organizations deploy EMS [26,27,49]. These platforms integrate multiple data streams from diverse sources (e.g., sensors, meters, weather services, and production systems) and present them through interactive dashboards. Although EMS are effective for KPI visualization, basic forecasting, and alerting [47], they often lack the advanced analytical capabilities needed for strategic modeling and complex historical analysis. This gap is often filled by BI software, which is intended to transform raw data into actionable insights [50,51]. BI tools support historical analyses, benchmarking, and predictive models, supporting executive-level decision-making [52]. However, the distinction between EMS and BI is frequently blurred, leading to functional redundancies or, worse, “analytical silos” where operational data (EMS) remains disconnected from strategic decisions (BI).

The integration of AI further extends the capabilities of digital systems by enabling intelligent, data-driven control [53,54]. Machine learning (ML) techniques are used to predict demand, detect anomalies, and optimize systems (e.g., HVAC) [55,56]. However, AI implementation is non-trivial: it requires large, clean datasets, specialized expertise, and continuous model validation to avoid performance degradation over time.

In parallel, the rise of DTs technology offers a virtualized approach to energy management [57,58]. A DTs is a dynamic, data-driven model of a physical system, such as a building or factory, that reflects its real-time behavior and performance. This virtual replica allows for the simulation of energy scenarios, evaluation of what-if conditions, and validation of energy-saving strategies before their physical implementation [29,59]. By integrating DTs with AI, organizations can achieve predictive and prescriptive analytics, ensuring that energy KPIs are not only monitored but actively optimized.

Finally, in industrial environments, Supervisory Control and Data Acquisition (SCADA) systems remain foundational for real-time process monitoring [60], but they often represent “legacy” systems that are difficult to integrate with modern IoT and cloud platforms. These systems collect and process data from programmable logic controllers (PLCs) and remote terminal units (RTUs), supporting the continuous tracking of production-related energy KPIs [61]. The combination of SCADA with BI and EMS enhances both operational transparency and decision accuracy.

Altogether, the interplay of these technologies—supported by a holistic integration strategy—enables a robust and adaptive framework for managing energy KPIs (Table 2). As buildings and factories become increasingly intelligent, the digital tools that support energy management evolve from passive monitoring instruments into proactive systems that drive continuous improvement and sustainability. In conclusion, the convergent evidence supports the central argument of this review: the integration of digital technologies shifts ESG KPIs governance from static reporting to near-real-time monitoring and optimization, with direct implications for efficiency, reliability, and transparency.

4.1. Smart Building

The role of digital technologies in enabling data-driven decision-making and optimizing ESG-related KPIs in Smart Buildings is increasingly substantiated by practical implementations across various domains. This section presents a synthesis of selected case studies that illustrate the deployment of such technologies and their associated benefits and challenges.

A particularly innovative approach is the implementation of Decentralized Autonomous Organizations (DAO) for energy management, as explored by Ding et al. [62]. In this model, building subsystems operate as autonomous agents, interacting through blockchain-enabled smart contracts. This decentralized framework facilitates peer-to-peer (P2P) energy exchanges between buildings, promoting a prosumer-oriented paradigm. DAO-based systems allow real-time optimization of energy flows without central authority, resulting in cost savings (up to 33.7%) and operational flexibility. However, this model poses significant barriers, including technological complexity, the need for continuous connectivity, and high infrastructure costs related to blockchain and smart metering technologies.

In contrast, the deployment of low-cost wireless protocols such as Zigbee demonstrates the feasibility of accessible IoT-based solutions for energy monitoring and automation. As reported by Madsen et al. [63], the use of Zigbee2MQTT within containerized environments (e.g., Docker) offers a modular, energy-efficient, and scalable architecture. With CO₂ sensors priced significantly lower than their 6LoWPAN counterparts, the system supports broad implementation in retrofitted buildings. The resulting 18.7% reduction in energy consumption [64] underscores the system’s efficiency. Nevertheless, compatibility issues among different manufacturers limit interoperability, hindering broader integration within heterogeneous building systems.

AI also plays a pivotal role in transforming energy optimization in Smart Buildings. Iluyomade and Okwandu [65] provide evidence of the integration of ML and IoT technologies for real-time energy forecasting and control. AI algorithms, leveraging historical and contextual data (e.g., occupancy, weather), can predict energy demand and adjust HVAC operations accordingly. These systems support predictive maintenance and increase energy efficiency while lowering operational costs. Despite these benefits, critical concerns persist around data privacy, cybersecurity, and the interpretability of black-box models.

Complementing the aforementioned approaches, Ghosh et al. [66] introduce a DSM-based Smart Home Energy Management System (EMS), designed to coordinate domestic energy consumption with onsite solar energy generation. By leveraging IoT, smart meters, and cloud-based analytics, the EMS optimizes appliance scheduling during solar production peaks, thereby reducing reliance on the grid. This integration of renewable energy within the household EMS structure enhances environmental sustainability and cost efficiency. Key limitations include managing user uncertainty, variability in solar generation, and user engagement levels.

Further advancements are exemplified by BrainBox AI, a commercial AI-based system for HVAC optimization [67]. Deployed in various corporate environments, BrainBox demonstrated substantial energy savings (e.g., 11% reduction in energy use within five months) and improved operational efficiency (BrainBox AI, 2025). Its ability to learn and adapt to internal and external variables, such as weather forecasts and occupancy trends, illustrates the potential of reinforcement learning in autonomous building management. Similar outcomes were observed in a pharmaceutical setting, where AI integration led to a 16% energy saving and 95 tCO₂eq emission reduction, reinforcing its ESG impact.

Finally, the case of Netix Controls showcases the value of integrating AI, IoT, and cloud computing into a centralized platform. As described by Pomè et al. [68], this hybrid system connects traditional Building Management Systems (BMS) to a flexible AI-based infrastructure. Key benefits include real-time fault detection, predictive maintenance, operational optimization, and significant ROI, such as a 75% reduction in annual labor hours and 15% decrease in energy costs. By supporting seamless integration without system replacement, this approach underlines the economic and operational viability of digital retrofitting in Smart Buildings.

These diverse implementations demonstrate how digital technologies enable the continuous monitoring and optimization of ESG KPIs in Smart Buildings. While each case exhibits unique advantages, common challenges persist, including system complexity, interoperability issues, and privacy concerns. Nonetheless, the transformative potential of these technologies in promoting sustainable, efficient, and intelligent building operations is evident.

4.2. Smart Factory

The increasing complexity of industrial energy systems, combined with the pressing need to reduce environmental impact, has led to the proliferation of Smart Factory models as key strategic implementation environment for the integration of digital technologies supporting ESG-related KPIs. Across diverse contexts, from large-scale manufacturing plants to pilot-scale testbeds, these factories leverage the capabilities of Industry 4.0, such as IoT, AI, DTs, and cloud-edge architectures, to optimize energy consumption, minimize downtime, and enable sustainable production. Notably, the case of Rittal’s Smart Factory in Germany illustrates how an advanced Industrial Internet of Things (IIoT) infrastructure can lead to real-time optimization of industrial processes [69]. Through the integration of Manufacturing Execution Systems (MES), private 5G networks, and edge computing, Rittal achieved seamless communication among production units, predictive maintenance, and improved machine utilization, ultimately aligning operational efficiency with sustainability goals. Similarly, MuRata Manufacturing exemplifies the digital transformation of a traditional Japanese electronics plant, where real-time sensor data, automation systems, and MES integration enabled a reduction in unplanned downtime and energy waste [70]. The deployment of autonomous robots and AGVs, combined with visual management systems and predictive analytics, enhanced both transparency and resource efficiency, demonstrating a data-driven path toward continuous improvement.

Beyond these corporate implementations, the case study by Fortoul-Diaz et al. [71] offers insight into the flexibility of Smart Factory systems built on open-source software architectures. Their experimental pick-and-place assembly line, equipped with real-time dashboards and modular control components, showcased how cyber-physical systems and cloud platforms could be applied to enable adaptable production models even in low-cost environments [71]. Despite its experimental scale, this implementation highlighted the potential for cost-effective Smart Factory solutions using technologies such as edge computing, AI, and cybersecurity frameworks. Collectively, these cases underscore the transformative potential of Smart Factories in supporting environmental KPIs, particularly those related to total energy consumption (TEC), energy intensity, carbon emissions per kWh, and efficiency per unit produced [72,73].

However, as emphasized in the literature, the realization of Smart Factory benefits requires addressing several challenges, including the complexity of system integration, the high initial capital investment, and the need for skilled personnel to manage digital infrastructures [74,75]. The application of Digital Twin technologies further enhances this ecosystem, enabling real-time simulation, predictive fault detection, and optimization of workflows and energy flows, thereby increasing operational resilience and reducing environmental footprint [31,76]. Additionally, studies suggest that the adoption of Industry 4.0 principles, such as modularity, decentralization, and interoperability, not only enhances production adaptability but also aligns with long-term sustainability strategies [77,78]. When designed with ESG objectives in mind, Smart Factories have the capacity to deliver measurable improvements in energy efficiency, emissions reduction, and operational cost savings. Indeed, quantitative analyses indicate potential returns on investment (ROI) of up to 10–20 times over five years, particularly through reduced operational costs and optimized energy use [79,80]. These findings reinforce the critical role of Smart Factory systems as both technological and strategic enablers for advancing ESG performance in the industrial sector.

5. Discussion

The growing diffusion of digital technologies is profoundly changing how organizations define, monitor, and optimize ESG-related KPIs. Our analysis confirms that the “real value” of ESG emerges only when its indicators are transformed from static, non-financial reporting metrics into dynamic, operational tools embedded in everyday processes. Digital infrastructures are the enablers of this transformation, providing the mechanism to turn raw data into actionable insights [8,16]. As summarized in Table 2, this transformation is enabled only when data-acquisition technologies are coherently integrated with EMS/BI platforms and advanced analytics, confirming the findings’ central claim that KPI effectiveness depends on end-to-end digital integration rather than on isolated tool adoption.

A primary output of this research is the identification of a fundamental shift in governance models. Traditionally, ESG reporting has been backward-looking, documenting performance post-operationally. Our findings show this model is entirely inadequate for fast-changing, energy-intensive environments. The integration of IoT, EMS, and analytics enables real-time KPI updates, shifting the decision-making horizon from months to minutes [24]. This shift is grounded in the findings showing how IoT and smart meters provide high-frequency operational data, while EMS dashboards support near-real-time visualization and alerting; however, the review also found that EMS alone rarely deliver strategic modeling, which is why BI, AI, and DTs are required to move from descriptive to predictive and prescriptive ESG governance. This represents a turning point: instead of recording what has already happened, organizations can forecast consumption peaks, simulate intervention scenarios, and automatically adjust systems [29,30]. However, our analysis suggests that many organizations stop at the descriptive stage, failing to capitalize on the prescriptive potential, thus limiting their ROI and impact.

In the building sector, which accounts for ~40% of global energy consumption and ~33% of CO₂ emissions [7], digital technologies provide significant leverage for sustainability goals. Practical implementations show that AI-based HVAC optimization can cut energy use by 10–20% in commercial buildings, with parallel reductions in emissions [62]. The Smart Building cases analyzed in Section 4.1 provide concrete evidence of this mechanism. DTs enhance this process by simulating building performance under different weather or occupancy scenarios, helping managers choose interventions with the highest ROI and lowest risk [58]. More importantly, a key innovative finding of this paper is the concrete, technology-driven link between the “E” and the “S” of ESG. Digital monitoring aimed at the “E” (energy) inherently captures data on indoor air quality, lighting, and thermal comfort, which are direct inputs for the “S” (occupant health, productivity, and inclusivity) [12,13]. Digitalization, therefore, acts as a synergistic bridge, proving that E and S are not mutually exclusive but deeply interconnected.

This thesis is corroborated in manufacturing, where Industry 4.0 technologies (IoT, SCADA, MES) are key enablers [61]. As shown by the Smart Factory cases synthesized in Section 4.2, Industry 4.0 infrastructures operationalize ESG energy KPIs through real-time process monitoring, predictive maintenance, and DT-enabled energy optimization. Our analysis of cases like Rittal and MuRata shows the crucial output is not just efficiency, but radical transparency—monitoring KPIs like carbon emissions per unit produced in real time [72]. Furthermore, the emergence of open-source solutions [71] suggests a potential democratization of these tools beyond large corporations. This is a significant finding, though the scalability to SMEs remains critically challenged by knowledge gaps and the lack of in-house integration expertise.

Despite this potential, our analysis highlights severe barriers that limit the full potential of digital ESG management. Interoperability remains the most fundamental socio-technical challenge. Heterogeneous devices, proprietary protocols, and legacy infrastructures prevent seamless integration. This is not merely a technical inconvenience; it is a structural failure. Without interoperability, data silos persist, undermining the accuracy and comparability of KPIs [63] and rendering a holistic ESG overview impossible. Second, this review identifies data integrity as the weakness of digital ESG. While smart meters provide granular data, the complexity of baseline definition and normalization—especially when accounting for weather, occupancy, and behavior—is a profound methodological challenge [16]. Without robust M&V protocols, reported gains are unverifiable, and the entire framework’s credibility collapses. Finally, our findings point to a “governance paradox”: we are using technology to govern ESG, but the technologies themselves often lack governance. AI-based systems raise critical concerns about transparency and explainability [53,75]. This concern is consistent with the findings highlighting that AI requires large, clean datasets and continuous validation, and that its value for ESG KPIs is limited when model opacity and cybersecurity risks are not governed (Table 2). Stakeholders cannot and should not trust “black box” optimization. Furthermore, cybersecurity and privacy [41] are not peripheral issues but are foundational to trust. A vulnerability that compromises building data or industrial controls discredits sustainability efforts and exposes the organization to severe operational and reputational risk. Alignment with standards like ISO 50001 [14] provides a necessary compliance framework, but it does not solve the underlying ethical and technical challenges of governing the complex, opaque digital systems upon which future ESG performance depends.

Implications

This critical review suggests several implications that can guide both practitioners and policymakers in implementing digital ESG strategies more effectively. A first consideration concerns the need to prioritize interventions that are both high-impact and low-risk. For instance, optimizing HVAC systems and introducing demand-side management strategies have consistently demonstrated measurable reductions in energy use and emissions [81,82]. These areas represent practical entry points where organizations can achieve immediate results and build internal support for more ambitious digital transformations [65]. At the same time, the scalability of these initiatives depends on the quality of the underlying data infrastructure. Investing in interoperable systems that avoid vendor lock-in and allow for data portability across different platforms is essential [83]. Without this foundation, organizations risk creating fragmented silos of information, which limit the reliability of ESG KPIs and undermine comparability between assets or portfolios [63]. Equally important is the alignment of digital tools with established international standards. Embedding EMS, AI-driven analytics, and other monitoring platforms within ISO 50001 energy management cycles does not only facilitate external audits but also provides a recognized governance structure that enhances accountability and consistency over time [14]. This alignment ensures that technological innovation is not detached from organizational routines but is embedded in established processes of planning, monitoring, and continuous improvement. Another key implication concerns the credibility of reported outcomes. M&V protocols should be rigorous and independent [84], so that improvements in ESG KPIs are not simply claimed but demonstrably verified. Such protocols help to prevent the risks of “greenwashing” and increase the trust of investors and other stakeholders in the reported figures [2]. Finally, the full potential of digital ESG management cannot be realized without the development of hybrid competences. Professionals must be trained not only in technical aspects, such as IoT integration or AI model management, but also in governance, ethics, and communication. This combination of skills enables organizations to bridge the gap between technical solutions and strategic decision-making, ensuring that digital tools are applied effectively and responsibly [74,85].

6. Conclusions

This study solves a critical dissonance between the technological optimism surrounding digital ESG and the fragmented reality of its implementation. Our innovative contribution is to demonstrate that integrating IoT infrastructures, EMS platforms, BI tools, DTs, and AI applications enables a transition from static reporting practices to truly dynamic and data-driven forms of governance. Within this transition, this review confirms that ESG indicators assume an operational function, shaping daily decision-making rather than serving merely as retrospective measures.

At the same time, the analysis yields three critical findings. First, while the reviewed cases highlight tangible benefits (e.g., reduced energy consumption, lower emissions, enhanced comfort, and improved industrial resilience) the broader evidence base remains fragmented. Much of the available literature continues to rely on pilot projects or vendor-led accounts, limiting generalizability and potentially overstating positive outcomes [22]. Second, this study identifies a “sustainability paradox”: the environmental costs of digital infrastructures themselves, particularly the energy demands of AI models and data centers, are rarely accounted for, which raises fundamental questions about the net sustainability gains of digitalization [75]. Third, a key output of this research concerns the relative imbalance among the three ESG pillars. The environmental dimension dominates empirical research and technological innovation, while social and governance indicators, such as inclusivity, workplace well-being, transparency, and ethical data use, remain underexplored. This asymmetry risks narrowing the scope of ESG and undermines its integrative ambition.

From a practical perspective, this finding implies that organizations may achieve rapid improvements by deploying digital tools in energy-intensive systems, but long-term success depends on investment in interoperable infrastructures, transparent governance mechanisms, and the development of hybrid competences that link technical expertise with strategic and ethical considerations [14].

From a research and policy perspective, there is a pressing need for independent, large-scale evaluations that consider not only efficiency gains but also trade-offs, including rebound effects and the environmental footprint of digital solutions.

In conclusion, digitalization should not be regarded as a technological fix but as a complex enabler whose effectiveness depends on how it is embedded within broader governance frameworks. Its value lies in strengthening ESG with evidence-based monitoring, greater transparency, and adaptive management. Only through a holistic integration that addresses the “S” and “G” pillars alongside the “E”, while actively accounting for the footprint of digitalization itself, can digital ESG KPIs evolve into credible instruments of transformation, capable of reconciling environmental imperatives with social and governance responsibilities.

Limitations and Future Research

Despite the progress observed in both literature and practice, the evidence base for digital ESG KPIs management still presents important limitations. One limitation of this study is that the search strategy, which focused on established terms related to buildings (e.g., “intelligent building,” “smart building”), may have led to the omission of potentially relevant studies that use alternative terminology. Another concern is the scale of existing studies. Much of the empirical evidence focuses on pilot projects or single-site implementations, which makes it difficult to generalize results across regions, sectors, or organizational contexts. While these pilots are useful to demonstrate feasibility, they often lack the diversity needed to capture the complexity of large-scale deployment. Another limitation relates to the nature of the available evidence. Many of the most widely cited results on energy savings or emission reductions come from vendor-led reports. These reports are valuable but tend to emphasize positive outcomes, while challenges such as interoperability issues, high upfront costs, or user resistance are often underrepresented. As a result, there is a risk of overestimating the benefits of digital ESG management without fully accounting for the organizational and technical barriers to implementation. A further gap concerns the environmental footprint of digital infrastructures themselves. Very few studies quantify the net energy balance of adopting AI, IoT, or cloud-based solutions. Yet, the energy consumed by data centers, AI training, and edge devices can be significant, potentially offsetting part of the efficiency gains achieved at the level of buildings or factories. Without such assessments, there is a risk that digitalization might inadvertently contribute to rebound effects. Finally, the current body of literature shows an imbalance in how the three ESG pillars are addressed. Environmental KPIs, such as energy intensity or emissions, are extensively studied, while the social and governance dimensions often receive less attention. Aspects such as inclusivity, workplace well-being, data privacy, and ethical AI use are mentioned but rarely analyzed in depth, creating a partial picture of the broader sustainability implications of digitalization.

To address these gaps, future research should pursue several directions. First, there is a need for large-scale, independent evaluations across portfolios of buildings and factories. Studies should apply standardized M&V protocols to ensure comparability and to validate the scalability of digital ESG solutions. Second, more work is needed on the net energy balance of digital technologies. Comparative studies should account not only for the savings generated by AI-driven optimization or IoT monitoring, but also for the computational and infrastructural costs of maintaining such systems. Only by adopting a life-cycle perspective can we fully assess whether digitalization contributes positively to climate and sustainability goals. Third, semantic interoperability should become a research priority. Developing shared taxonomies, ontologies, and data models for ESG KPIs would allow organizations to compare performance across different platforms and contexts, reducing fragmentation and increasing transparency. Fourth, data governance models deserve systematic exploration. This includes examining how optimization can be balanced with privacy, cybersecurity, and ethical AI principles, especially in contexts where sensitive occupant or employee data are collected. Such research should integrate insights from computer science, law, and organizational studies. Finally, scholars should expand their focus to include social and governance indicators. Future studies could investigate how digital tools improve inclusivity in smart buildings, enhance worker well-being in factories, or support transparent decision-making processes aligned with international standards. Broadening the scope beyond environmental metrics will allow for a more holistic understanding of the ESG impact of digitalization.

Author Contributions

Conceptualization, N.M., E.Z., C.T. and A.Z.; methodology, G.S.; formal analysis, C.S.; investigation, C.S.; data curation, C.S.; writing—original draft preparation, C.S., E.Z. and C.T.; writing—review and editing, G.S. and E.Z.; supervision, G.S., N.M., E.Z., C.T. and A.Z.; project administration, E.Z., C.T. and A.Z.; funding acquisition, N.M. All authors have read and agreed to the published version of the manuscript.

Funding

The proposed work has been developed within the framework of the project: “xTech NextHub: Competence Center for Innovative Solutions Development” through the Puglia Regional Call for Proposals for Exemption Aid 17 of 30/09/2014-BURP 139 suppl. of 06/10/2014 and subsequent amendments—Title II Chapter 2 of the General Regulations “Notice for the Submission of Projects Promoted by Large Enterprises Pursuant to Article 17 of the Regulation”. Project’s grant number: VTOIFW0.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors of this article, Nicola Magaletti of LUM Enterprise S.r.l., and Ettore Zini, Chiara Tognon, and Angelo Zerega of Deloitte Consulting S.r.l., all declare no conflicts of interest related to this research.

Appendix A

Table A1. (Environmental) Energy KPIs.

(Environmental) Energy KPIs
KPI	Category	Description	Formula	UoM	Typology
Carbon Footprint (CF)	CO₂ Emission Reduction	Indicates the total amount of greenhouse gas (GHG) emissions caused by an individual, organization, or product, either directly or indirectly. The formula calculates the sum of emissions associated with different activities by multiplying the quantity of each activity by its corresponding emission factor. [86]	$C F = \sum_{i} ({A c t i v i t i e s}_{i} x {E m i s s i o n F a c t o r s}_{i})$ ${A c t i v i t i e s}_{i}$ = Quantity of a specific activity that generates greenhouse gas emissions (e.g., km, kWh, liters). ${E m i s s i o n F a c t o r s}_{i}$ = Rate of GHG emissions per unit of activity, expressed in CO₂ equivalent per unit (e.g., tCO₂e/kWh for electricity, tCO₂e/liter for fuel, etc.).	tCO₂e	Quantitative
Emission Intensity (EI)	CO₂ Emission Reduction	Evaluates the environmental impact of an energy system by measuring the amount of carbon dioxide (CO₂) emitted per unit of energy consumed or produced. A low $E I$ value indicates that the system is more environmentally efficient, emitting less CO₂ for each unit of energy consumed or produced (this can occur through the use of renewable energy sources). Conversely, high $E I$ values typically occur in systems that rely heavily on fossil fuels. [87]	$E I = \frac{\sum_{i} {{C O}_{2} E m i s s i o n s}_{i}}{\sum_{i} {E_{P r}}_{i}}$ ${{C O}_{2} E m i s s i o n s}_{i}$ = Total amount of CO₂ emitted over a given period, resulting from the consumption of fossil fuels or the use of grid electricity [tCO₂] ${E_{P r}}_{i}$ = Total amount of energy consumed or produced during the same reference period [kWh]	tCO₂/kWh	Quantitative
Load Cover Factor	Load Match Indicators	Represents the ratio between the energy actually supplied by a generation source and the energy demanded or consumed over a given time interval. If equal to 1, it indicates that the generation capacity exceeds the demand, whereas values lower than 1 indicate that generation is insufficient to meet the required load. When $γ_{l o a d}$ = 1, the entire load demand is fully satisfied. When $γ_{l o a d} <$ 1, the load is not completely met during part of the period, due to limitations in generation or available resources. Range: 0 $<$ $γ_{l o a d} <$ 1 [88,89]	$γ_{l o a d} = \frac{\int_{τ_{1}}^{τ_{2}} \min [g (t) - S (t) - ζ (t), l (t)] d t}{\int_{τ_{1}}^{τ_{2}} l (t) d t}$ $g (t)$ = On-site energy generation at a given time t [kWh] $ζ (t)$ = Storage energy losses at a given time t [kWh] $l (t)$ = Building load at a given time t [kWh] $τ_{1}$ e $τ_{2}$ = Start and end of the evaluation period [s] $S (t)$ = Storage energy balance at a given time t [kWh] $S (t) = S_{c} - S_{d c}$ $S_{c}$ = Charging energy of the storage system [kWh] $S_{d c}$ = Discharging energy of the storage system [kWh]	-	Quantitative
Supply Cover Factor	Load Match Indicators	Indicates the ability of an organization to meet its energy demand through its own on-site supply resources. When $γ_{s u p p l y}$ = 1, the amount of useful supplied resources is exactly equal to the total available amount. This implies that there are no significant losses and that all available resources are fully utilized. When $γ_{s u p p l y}$ < 1, the amount of effectively usable resources is lower than the total available amount. Part of the generated energy is not used to meet the load, likely due to overproduction, losses, or storage capacity limitations. Range: 0 $<$ $γ_{s u p p l y} <$ 1 [88,89]	$γ_{s u p p l y} = \frac{\int_{τ_{1}}^{τ_{2}} \min [g (t) - S (t) - ζ (t), l (t)] d t}{\int_{τ_{1}}^{τ_{2}} g (t) d t}$ $g (t)$ = On-site energy generation at a given time t [kWh] $ζ (t)$ = Storage energy losses at a given time t [kWh] $l (t)$ = Building load at a given time t [kWh] $τ_{1}$ e $τ_{2}$ = Start and end of the evaluation period [s] $S (t)$ = Storage energy balance at a given time t [kWh] $S (t) = S_{c} - S_{d c}$ $S_{c}$ = Charging energy of the storage system [kWh] $S_{d c}$ = Discharging energy of the storage system [kWh]	-	Quantitative
Load Matching Index	Load Match Indicators	Measures the efficiency with which on-site energy generation (whether renewable or not) matches the energy load (demand) of a system. It evaluates how well the energy production profile corresponds to the load profile over time by analyzing the synchrony between supply and demand. A higher index indicates a better match between generation and load. When $f_{l o a d, i}$ = 1, the load is fully met (i.e., generation and storage are sufficient to cover the required demand) in every considered interval. When $f_{l o a d, i}$ < 1, the load is not fully met at certain times, meaning that the generation and/or storage capacity was lower than the demand. Range: 0% ≤ f_(load,i) ≤ 100% [89]	$f_{l o a d, i} = \frac{\sum_{τ_{1}}^{τ_{2}} \min [1, \frac{g (t) - S (t) - ζ (t)}{l (t)}]}{N} \cdot 100$ i = Time intervals [hourly, daily, monthly] $g (t)$ = On-site energy generation at a given time t [kWh] $S (t)$ = Storage energy balance at a given time t [kWh] $ζ (t)$ = Energy losses at a given time t (sum of generation energy losses, storage energy losses, building technical system losses (excluding storage), and load-related energy losses such as distribution losses) [kWh] $l (t)$ = Building load at a given time t [kWh] $τ_{1}$ e $τ_{2}$ = Start and end of the evaluation period [s] $N$ = Number of samples within the evaluation period, from τ₁ to τ₂. When hourly data are used and the evaluation period covers a full year, the number of samples is 8760.	%	Quantitative
On-site Energy Ratio (OER)	Load Match Indicators	Determines the amount of energy produced on-site (e.g., from renewable sources such as solar panels or wind turbines) relative to the total energy consumption over a given period of time. If $O E R$ = 1, the on-site generated energy equals the total energy consumption. If $O E R$ < 1, the on-site produced energy is lower than total consumption, meaning that the system depends on external energy sources to meet the demand. If $O E R$ > 1, the on-site generated energy exceeds total consumption, indicating that energy production is greater than demand (and surplus energy may be exported to the grid). Range: $O E R \geq$ 0 [90]	$O E R = \frac{\int_{τ_{1}}^{τ_{2}} g (t) d t}{\int_{τ_{1}}^{τ_{2}} l (t) d t}$ $g (t)$ = On-site energy generation at a given time t [kWh] $l (t)$ = Total energy consumption (energy load) at a given time t [kWh] $τ_{1}$ e $τ_{2}$ = Start and end of the evaluation period [s]	-	Quantitative
Grid Interaction Index	Grid Interaction Indicators	Measures the level of interaction and integration of a facility with the power grid, describing its average stress. If $f_{g r i d, i}$ = 100%, the energy exchanged with the grid during interval i equals the maximum possible exchange. If $f_{g r i d, i}$ = 0%, no energy exchange with the grid occurred at that moment. If $f_{g r i d, i}$ < 0%, energy was injected into the grid rather than drawn from it. [88,89]	$f_{g r i d, i} = [\frac{{n e t g r i d}_{i}}{\max \|n e t g r i d\|}] \cdot 100$ ${n e t g r i d}_{i}$ = Net energy exchanged with the power grid during interval i (can be positive or negative depending on whether energy is being drawn from or injected into the grid) [kWh] $\max \|n e t g r i d\|$ = Maximum absolute value of the net energy flow with the grid, taken over all considered time intervals [kWh] i = Time intervals [hourly, daily, monthly]	%	Quantitative
No grid interaction probability	Grid Interaction Indicators	Measures the probability that a building or facility operates autonomously from the power grid, and therefore the likelihood of no interaction with it. It also indicates the extent to which the load is covered by stored energy or renewable energy use. If $P_{(E = 0)}$ = 0, there was no moment during the considered time interval when the net energy was zero or negative. If $P_{(E = 0)}$ = 1, the net energy was zero or negative for the entire considered period. Range: 0 $\leq$ $P_{(E = 0)}$ $\leq$ 1 [88,89]	$P_{(E = 0)} = \frac{\int_{τ_{1}}^{τ_{2}} {d t}_{\|n e (t)\| < 0}}{τ_{2} - τ_{1}}$ $P_{(E = 0)}$ = Probability that the net energy $n e (t)$ is zero or negative during the time interval \| $τ_{2} - τ_{1}$ \| $n e (t)$ = Normalized variable for the net exported energy at a given time t [kWh] $τ_{1}$ e $τ_{2}$ = Start and end of the evaluation period [s]	-	Quantitative
Capacity Factor (CF_b)	Grid Interaction Indicators	Defines the ratio between the actual energy production of a system (energy exchanged between the building and the grid) and the maximum production that could be achieved if the system operated at full capacity over a given period of time. If ${C F}_{b}$ = 1, the system operated at its maximum capacity for the entire considered period. If ${C F}_{b}$ = 0, the system did not produce any energy. Range: 0 $\leq$ ${C F}_{b}$ $\leq$ 1 [89]	${C F}_{b} = \frac{\int_{τ_{1}}^{τ_{2}} \| n e (t) \| d t}{E_{d e s} \cdot T}$ $n e (t)$ = Normalized variable for the net exported energy at a given time t [kWh] $E_{d e s}$ = Maximum producible energy at full capacity (system capacity) [kWh] $T$ = $τ_{2} - τ_{1}$ = Evaluation period [s]	-	Quantitative
One Percent Peak Power (OPP)	Grid Interaction Indicators	Quantifies the maximum power that an energy system can reach by calculating the energy production corresponding to the top 1% of peak periods. A high $O P P$ value indicates that the building or system experiences moments (the top 1% of the time) with very high energy consumption. This may point to significant peak loads that place stress on the electrical grid. If $O P P$ is low, the building’s energy demand is more evenly distributed over time, with fewer or smaller peaks. [91]	$O P P = \frac{E_{1 % p e a k}}{T}$ $E_{1 % p e a k}$ = Energy associated with the top 1% of a given value, calculated during periods of maximum demand or generation [kWh] $T$ = Time period over which the energy is measured [h]	kW	Quantitative
Demand Response Percentage (DR^p)	Energy Flexibility Indicators	Refers to the percentage variation of the Demand Response relative to a baseline value. If ${D R}^{p}$ > 0, the Demand Response was successful in reducing power compared to the baseline level (load “reduction” capability). If ${D R}^{p}$ = 0, no variation occurred. If ${D R}^{p}$ < 0, it indicates an increase in power during the Demand Response implementation, which is generally undesirable (load “overload” condition). [92]	${D R}^{p} = \frac{p_{h}^{b a s e} - p_{h}^{L S}}{p_{h}^{b a s e}} \cdot 100$ $p_{h}^{b a s e}$ = Baseline hourly power, i.e., the expected or normal power level without any Demand Response measures [kWh] $p_{h}^{L S}$ = Hourly power under Load Shifting conditions, i.e., the power recorded during the Demand Response event [kWh]	%	Quantitative
Flexibility Factor (FF)	Energy Flexibility Indicators	Measures the ability of an energy system to adapt to variations in energy demand and resource availability, and to shift energy use from high-price periods to lower-price periods. It applies a daily quartile-based price classification, dividing prices into three categories: low, medium, and high. A high price is defined as one above the third quartile (price > 75% of all prices during a day). A low price corresponds to a value within the first quartile (price ≤ 25%). If $F F$ = 0, consumption is balanced between low- and high-price periods. If $F F$ = 1, consumption occurs only during low-price periods. If $F F$ < 0, most consumption occurs during high-price periods. Range: −1 $\leq$ $F F$ $\leq$ 1 [93]	$F F = \frac{\sum_{i = 1}^{n} {(E_{e l}^{i} \cdot p^{i})}_{q 1} - \sum_{i = 1}^{n} {(E_{e l}^{i} \cdot p^{i})}_{> q 3}}{\sum_{i = 1}^{n} {(E_{e l}^{i} \cdot p^{i})}_{q 1} + \sum_{i = 1}^{n} {(E_{e l}^{i} \cdot p^{i})}_{> q 3}}$ $E_{e l}^{i}$ = Electricity consumption during time interval i [kWh] $p^{i}$ = Energy price during time interval i $q_{1}$ = Low-price periods (first quartile, i.e., the lowest 25% of prices) $q_{> 3}$ = High-price periods (above the third quartile, i.e., the highest 25% of prices) $n$ = Number of considered time intervals	-	Quantitative
Flexibility Index (FI)	Energy Flexibility Indicators	Calculates the difference between the energy cost under a flexibility-controlled scenario and the energy cost under a reference scenario. The Flexibility Index is used to measure the effectiveness of flexibility strategies in reducing costs compared to a baseline case. If $F I$ < 0, the flexibility-controlled case has a higher energy cost than the reference case, meaning an undesirable cost increase. If $F I$ = 0, the total energy cost under flexible conditions is identical to that of the reference case, indicating that flexibility yields no savings. If $F I$ = 1, the total cost in the flexibility-controlled case is zero relative to the reference case—this represents an ideal but unrealistic situation. If $F I$ is positive and close to 1, it means that energy has been effectively shifted or managed, reducing costs compared to the reference scenario. Range: − $\infty$ $<$ $F I$ $\leq$ 1 [94]	$F I = 1 - \frac{\sum_{i = 1}^{n} {(E_{e l}^{i} \cdot p^{i})}_{f l e x}}{\sum_{i = 1}^{n} {(E_{e l}^{i} \cdot p^{i})}_{r e f}}$ $E_{e l}^{i}$ = Electricity consumption during time interval i [kWh] $p^{i}$ = Energy price during time interval i ${(E_{e l}^{i} \cdot p^{i})}_{f l e x}$ = Total electricity cost in a flexibility-controlled scenario ${(E_{e l}^{i} \cdot p^{i})}_{r e f}$ = Total electricity cost in a reference scenario without flexibility control $n$ = Number of considered time intervals	-	Quantitative
Flexible Energy Efficiency	Energy Flexibility Indicators	Measures how effectively a system utilizes flexible energy compared to its reference energy consumption. It refers to the system’s ability to manage energy use during Demand Response (DR) events, considering the “rebound effect” (i.e., when energy consumption increases after a reduction event to restore normal operating conditions). A higher $η_{f}$ value indicates greater flexibility efficiency, meaning the system can better optimize energy use during flexible periods. Range: 0% $\leq$ $η_{f} \leq$ 100% [95]	$η_{f} = \|\frac{E_{f}}{E_{r b}}\| \cdot 100$ $E_{f}$ = Flexible energy, i.e., the energy used during periods when the system operates in flexible mode (for example, by optimizing consumption based on renewable resource availability or variable pricing) [kWh] $E_{r b}$ = Reference or baseline energy, i.e., the energy consumed under normal or non-flexible operating conditions [kWh]	%	Quantitative

Table A2. (Social) Energy KPIs.

(Social) Energy KPIs
KPI	Category	Description	Formula	UoM	Tipology
Relative Humidity (RH)	Comfort Indoor—IAQ	Indicates the amount of water vapor in the air relative to the maximum that can be contained at the same temperature. The optimal relative humidity (RH) range for occupant comfort and health is between 40% and 60%. [96]	$R H = \frac{e}{e_{s}} \cdot 100$ $e$ = Water vapor pressure [Pa] $e_{s}$ = Saturation vapor pressure [Pa]	%	Quantitative
PM Concentration (Particulate Matter—PM10 e PM2.5)	Comfort Indoor—IAQ	Measures the amount of suspended particles (particulate matter) in the air, typically expressed in micrograms per cubic meter (µg/m³). PM2.5 refers to particles with a diameter smaller than 2.5 μm, while PM10 refers to particles smaller than 10 μm. Recommended long-term health thresholds are PM2.5 < 20 µg/m³ and PM10 < 50 µg/m³. [97]	${P M}_{C} = \frac{{P M}_{m}}{V_{a i r}}$ ${P M}_{m}$ = Mass of particulate matter [µg] $V_{a i r}$ = Volume of air [m³]	µg/m³	Quantitative
VOC Levels (Volatile Organic Compounds)	Comfort Indoor—IAQ	Establishes the concentration of VOCs—such as benzene, formaldehyde, and other potentially harmful gases. Elevated VOC levels can cause discomfort and health issues in occupants. The indicated threshold is ${V O C}_{l e v}$ < 300 ppb. [98]	${V O C}_{l e v} = (\frac{C \cdot 10^{9}}{M}) \cdot \frac{1}{V}$ $C$ = VOC concentration [mg/m³] $M$ = Molar mass of the VOC [g/mol] $V$ = Molar volume under standard conditions, generally considered as 24.45 L/mol (at standard temperature and pressure, 0 °C and 1 atm)	ppb	Quantitative
Air Changes per Hour (ACH)	Comfort Indoor—IAQ	Indicates the number of times the air within a space is completely renewed in one hour. An air change rate between 3–5 ACH is considered adequate for residential buildings or office environments. [99]	$A C H = \frac{Q}{V}$ $Q$ = Airflow rate [m³/h] $V$ = Volume of the indoor space [m³]	1/h	Quantitative
Thermal Insulation Rate (R-Value)	Comfort Indoor—Insulation (Thermal)	Determines the thermal resistance of insulating materials, indicating how effectively they prevent heat loss. A higher R-Value indicates better insulation performance. [100]	$R - V a l u e = \frac{t}{λ}$ $t$ = Materials thickness [m] λ = Thermal conductivity of the materials [W/m·K]	m²·K/W	Quantitative
Sound Insulation Index (R)	Comfort Indoor—Insulation (Acoustic)	Evaluates the effectiveness of a building element in reducing sound transmission between two different spaces. It is defined as the difference between the incident sound pressure level on a surface and the transmitted sound pressure level through it. A higher R value indicates that walls, floors, or windows are more effective at blocking sound. [101]	$R = L_{1} - L_{2} + 10 {l o g}_{10} (\frac{A}{S})$ $L_{1}$ = Incident sound pressure level [dB] $L_{2}$ = Transmitted sound pressure level [dB] $A$ = Equivalent absorption area [m²] $S$ = Separating surface area [m²]	dB	Quantitative
Energy Efficiency Ratio (EER)	Comfort Indoor—HVAC	Measures the efficiency of an air conditioning system (air conditioners or cooling units). A higher EER indicates that the air conditioning system provides more cooling output for each unit of energy consumed, making it more efficient. If EER ≥ 12, the system is considered efficient. [102]	$E E R = \frac{\emptyset_{c}}{P_{t}}$ $\emptyset_{t c i}$ = Total cooling capacity provided by the system [kW] $P_{t}$ = Electrical power input consumed by the system [kW]	-	Quantitative
Coefficient of Performance (COP)	Comfort Indoor—HVAC	An indicator similar to the EER, it can be used to evaluate efficiency in both cooling and heating modes. It is commonly applied to heat pumps. A higher COP indicates that the system can produce a greater amount of useful energy (heating or cooling) for each unit of electrical energy consumed. If COP ≥ 3.5, the system is considered efficient. [103]	$C O P = \frac{\| \emptyset \|}{P_{t}}$ \| $\emptyset$ \| = $\emptyset_{h}$ = $\emptyset_{c}$ = Heating or cooling capacity provided by the system [kW] $P_{t}$ = Electrical input power consumed by the system [kW]	-	Quantitative
System Efficiency (η)	Comfort Indoor—HVAC	Measures how much of the energy used by the system is effectively converted into useful heating or cooling. A high system efficiency means that a large portion of the consumed energy is actually transformed into useful thermal energy, minimizing losses. If η ≥ 85%, the system is considered efficient. [104]	$η = \frac{E_{u t}}{E_{t}}$ $E_{u t}$ = Useful energy delivered (cooling or heating capacity) [kWh] $E_{t}$ = Total energy consumed (including system losses and auxiliary consumption) [kWh]	-	Quantitative
Energy Use Intensity based on people count (EUI_p)	Comfort Indoor—Lighting	Measures the energy consumption for lighting relative to the number of occupants in the building, reflecting energy efficiency in terms of per capita usage. A high EUI indicates higher energy consumption for lighting per person, suggesting a lack of optimization. Optimal values: EUI < 15 kWh/person/year. [104]	${E U I}_{p e o p l e} = \frac{E_{l i g h t}}{N_{p e o p l e} \cdot T}$ $E_{l i g h t}$ = Energy consumed for lighting [kWh] $N_{p e o p l e}$ = Number of occupants in the building $T$ = Duration of lighting usage [year]	kWh/ person/ year	Quantitative
Lighting Power Density per floor area (LPD_f)	Comfort Indoor—Lighting	Determines the power consumed by lighting per unit of floor area. It serves as an indicator of lighting efficiency in relation to the utilized space. A high LPD indicates greater power consumption per unit area, suggesting inefficient lighting design. Optimal values: LPD < 10 W/m². [104]	${L P D}_{f l o o r} = \frac{P_{l i g h t}}{A}$ $P_{l i g h t}$ = Power used for lighting [kW] $A$ = Illuminated indoor area [m²]	kW/m²	Quantitative

Table A3. (Governance) Energy KPIs.

(Governance) Energy KPIs
KPI	Category	Description	Formula	UoM	Typology
Cost of Energy Saving (CES)	Economic	Measures the cost associated with energy savings achieved through energy efficiency interventions. This parameter is particularly useful for comparing different investment options in terms of efficiency, as it estimates how much it costs to save one unit of energy (e.g., 1 kWh) through technological or operational measures. The CES formula is structured to calculate the total cost of energy savings and divide it by the amount of energy saved, accounting for system inefficiencies. A higher CES indicates a greater cost per unit of energy saved, suggesting that the intervention may be less cost-effective compared to other alternatives. Conversely, a lower CES means a lower cost per unit of energy saved, making the energy efficiency measure more economically advantageous. [105]	$C E S = \frac{(Δ I_{0} \cdot C R F) + Δ C o m - p \cdot Δ E \cdot (1 - f_{s i d e})}{Δ E \cdot (1 - f_{s i d e})}$ $Δ I_{0}$ = Change in initial investment. Represents the amount of capital required to implement the energy efficiency measure [€] $Δ C o m$ = Change in operating costs. Includes expenses related to the operation and maintenance of the energy efficiency measure [€] $p$ = Energy price. Represents the cost per unit of energy, which can influence the savings achieved by the measure [€/kWh] $Δ E$ = Change in energy consumption. Indicates the amount of energy saved as a result of the intervention [kWh] $f_{s i d e}$ = Energy loss (or efficiency) factor associated with losses that may occur during the energy use process. It may include heat losses or other system inefficiencies [-] $C R F$ = Capital Recovery Factor. Used to calculate the annualized cost of the investment and determine how much an investment must generate each year to be recovered over time [-] $C R F = \frac{i \cdot {(1 + i)}^{n}}{{(1 + i)}^{n} - 1}$ $i$ = Interest rate [-] $n$ = Amortization period [years].	[€/kWh]	Quantitative
Energy Return on Investment (EROI)	Economic	Evaluates the energy efficiency of a production source by measuring how much energy is obtained compared to how much energy is invested to produce it. It is a key indicator of energy sustainability: the higher the EROI, the more efficient the system. If EROI > 1, the energy process is sustainable, as the energy produced exceeds the energy invested. If EROI = 1, the energy produced is exactly equal to the energy invested, meaning the system is at the limit of sustainability and produces no usable net energy. If EROI < 1, the system is inefficient, since it requires more energy than it generates. Such a process is neither economically nor energetically sustainable in the long term. This indicator answers the question: “How efficient is the energy investment?” [106]	$E R O I = \frac{\sum_{i = 1}^{n} λ_{i} E_{i}^{O}}{\sum_{j = 1}^{m} λ_{j} E_{j}^{I}}$ $E_{i}^{O}$ = Total outgoing or produced energy from process i. This may include, for example, the electricity generated by a power plant or the fuel produced by a refinery [kWh]. $E_{j}^{I}$ = Total incoming or consumed energy for process j. This may include the energy required to extract, transform, or transport the energy source [kWh]. $λ_{i}$ e $λ_{j}$ = Scaling factors that can represent the quality of energy. For instance, they may be used to assign greater or lesser importance to certain forms of energy or technologies [-].	[-]	Quantitative
Energy Payback Time (EPBT)	Economic	Measures the time required for an energy system to produce the same amount of energy that was needed to build, install, and maintain it. If EPBT is high, it takes longer for the system to return the energy invested. Conversely, if EPBT is low, the energy system quickly recovers the energy used for its construction and startup. It is an indicator that answers the question: “How long does it take for the system to repay the energy invested?” [107]	$E P B T = \frac{\sum_{i = 1}^{n} E_{i}^{I}}{\sum_{j = 1}^{m} E_{j}^{P - Y}}$ $E_{i}^{I}$ = Total invested energy required to build, install, maintain, and decommission the energy system throughout its life cycle [kWh]. $E_{j}^{P - Y}$ = Amount of energy that the system is capable of producing annually once it is operational [kWh/year].	[year]	Quantitative
Cost of Peak Demand (CPD)	Economic	Measures the cost associated with the peak electricity demand over a given period. A lower CPD is desirable, as it indicates effective management and reduced exposure to energy costs. [108]	$C P D = P_{d} \cdot C_{u}$ $P_{d}$ = Represents the maximum power demand during a given period [kW]. $C_{u}$ = Represents the cost associated with each unit of power [€/kW].	[€]	Quantitative
Cumulative Cash Flow (CCF)	Economic	Measures the total cash flow generated by the project in relation to the initial investment. The CCF is useful for investors and decision-makers, as it helps assess a project’s profitability, compare different investments, and plan future financial needs and returns on investment. A CCF > 0 indicates that the project is generating more cash flow than the costs incurred, while a CCF < 0 indicates a loss. [109]	$C C F = (\sum_{k = 1}^{n} {F E S}_{k} \cdot {E C C}_{k} \cdot T L) - I C$ ${F E S}_{k}$ = Represents the Final Energy Savings in period k. This value indicates the final energy savings achieved through energy efficiency measures or other strategies [kWh]. ${E C C}_{k}$ = Energy Carrier Cost, i.e., the cost of energy per unit during period k. This may include costs for purchasing or using energy such as electricity, gas, etc. [€/kWh]. $T L$ = Technical Life, i.e., the project period during which energy savings and economic benefits are expected [years]. $I C$ = Investment Cost, i.e., the cost of the investment. It includes all expenses necessary to implement the project, such as installation, equipment, and other preliminary costs [€].	[€]	Quantitative
Share of Project Cost Subsidized (SPCS)	Economic	Indicates the proportion of the total project cost that has been financed through grants. A high SPCS means that a significant portion of the project has been funded through external aid, while a low SPCS suggests that the project has been mainly self-financed. SPCS = 0% when no grants have been received (RS = 0), meaning no part of the project costs is subsidized. SPCS = 100% when the entire project cost is covered by grants (RS = IC), meaning the entire project is subsidized. Range: 0% ≤ SPCS ≤ 100% [109]	$S P C S = \frac{R S}{I C} \cdot 100$ $R S$ = Received Subsidies, meaning the total amount of grants or funding received for the project [€]. $I C$ = Investment cost, meaning the total investment cost [€].	[%]	Quantitative
Renewable Energy Use (REU)	Sustainability	Provides a measure of the proportion of final energy savings that comes from renewable sources compared to all energy sources used. It is useful for energy policies and environmental assessments, as it helps quantify and compare the impact of different energy sources on overall sustainability and efficiency. A higher REU indicates greater use of renewable energy, while a lower REU suggests a higher dependence on fossil fuels. Range: 0% ≤ REU ≤ 100% [109]	$R E U = \frac{\sum_{k = 1}^{n} \| {F E S}_{k} \cdot {C F}_{k} \cdot {R E S f a c t o r}_{k} \|}{\sum_{k = 1}^{n} \| {F E S}_{k} \cdot {C F}_{k} \|} \cdot 100$ ${F E S}_{k}$ = Final Energy Savings for each energy source k. Indicates the final energy savings achieved from that specific source [kWh]. ${C F}_{k}$ = Conversion Factor for each energy source k. This factor is used to convert the saved energy into a common unit, allowing comparison among different sources [-]. ${R E S f a c t o r}_{k}$ = Renewable Energy Source factor for each energy source k, which accounts for the sustainability of the source. This value varies depending on the type of energy: 0 for fossil fuels, indicating they do not contribute to sustainable energy production [-] 1 for renewable sources such as biomass, wind, solar, and other renewables, as they are considered sustainable [-] A value between 0 and 1 for mixed sources, such as industrial waste or end-of-life tires, depending on the sustainability level of the source [-]	[%]	Quantitative
Energy Use per Worker-Hour (EPWH)	Human Capital	Measures the total energy used by a production system in relation to the number of human resources and working time. It calculates the energy used per working hour, taking into account the total supplied energy minus the imported one, and normalizing the result by the number of workers and the annual working hours. This indicator is useful for evaluating the energy efficiency of an organization or an entire economy, allowing comparisons over time or between different sectors or countries. A low EPWH is considered positive, as it indicates higher productivity with lower energy use, suggesting a more sustainable use of energy resources. Conversely, a high EPWH may indicate energy inefficiency, potentially linked to poorly optimized production processes, outdated machinery, or energy-intensive technologies. [110]	$E P W H = \frac{T P E S - I P E S}{N_{p o p} \cdot (\frac{h}{y})}$ $T P E S$ = Total Primary Energy Supply, i.e., the total amount of primary energy supplied, including all available energy sources [kWh]. $N_{p o p}$ = Population number, meaning the total number of individuals within the studied population. $\frac{h}{y}$ = Total number of working hours per person per year [hours/year]. $I P E S$ = Industrial Primary Energy Supply, meaning the portion of TPES specifically used in the industrial sector [kWh]. $I P E S = T P E S \cdot \frac{I F C}{T F C}$ $I F C$ = Industrial Final Consumption, referring to the final energy consumption by the industrial sector [MWh]. $T F C$ = Total Final Consumption, referring to the total final energy consumption within a given economic system, including the industrial, residential, tertiary, and transport sectors [MWh].	MJ/(ab. hour/years)	Quantitative

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Table 1. Research process.

Phase	Purpose	Inputs	Procedure
1. Research objective	Define the guiding question and scope	ESG KPIs context; Smart Buildings/Smart Factories domains	Formulate RQ and specific aims; align with review scope
2. Database search	Identify candidate studies	Scopus & Web of Science	Title/Abstract/Keyword query using domain + KPI terms; 2000–2024; EN/IT; eligible types
3. Screening	Ensure relevance and fit	Retrieved records	Deduplicate; title/abstract screening; full-text eligibility using inclusion/exclusion criteria
4. Manual content analysis	Extract and code evidence	Included documents	open/thematic coding by two reviewers; consensus on key themes (domain, technologies, ESG KPIs, methods, evidence type, outcomes)
5. Snowballing	Capture additional relevant items	Seed papers	Backward (references) and forward (citations); reapply same criteria
6. Integration with secondary data	Enrich with real-world applications	Desk-based case materials	Select illustrative cases consistent with scope; document data sources/limits
7. Clusterization	Compare across application domains	Coded studies and cases	Group evidence into Smart Buildings and Smart Factories; synthesize per cluster

Table 2. Digital Technologies for ESG KPIs: A Theoretical–Operational Framework.

Technology	Main Role in ESG KPIs Management	Related ESG KPIs	Benefits	Limitations	References
IoT sensors	High-granularity, real-time monitoring of environmental/operational variables (temp., humidity, light, occupancy, voltage, power)	Energy intensity; energy use per occupant/zone; IEQ (thermal, lighting, air quality)	Enables context-specific KPIs; rapid detection of inefficiencies/deviations	Device heterogeneity; data quality; privacy	[46,47]
Smart Meters	Accurate metering (load profiles, peaks, time-stamped data)	Total/peak consumption; demand patterns	Supports load management and benchmarking	Interval data management; integration with other systems	[26,27,48]
PQA	Electrical supply quality assessment (harmonics, voltage fluctuations, phase imbalance)	Power Factor; THD; equipment efficiency/reliability KPIs	Diagnoses inefficiencies; reduces failures	Added complexity; specialized expertise	[48]
EMS	Data integration, dashboards, alerts; forecasting; (advanced) optimization/control	Portfolio/asset KPIs; threshold compliance	Centralized governance; automated responses	Vendor lock-in; interoperability	[26,27,49]
BI	Historical analysis, benchmarking, predictive analytics for decision support	Energy/CO₂ per area/occupant; cross-asset comparisons; disclosure-ready metrics	Executive visibility; supports planning and reporting	Data harmonization; audit trails	[50,51,52]
AI	Forecasting, anomaly detection, predictive maintenance; autonomous control (HVAC, lighting)	Energy use/emissions; comfort/IEQ; reliability/uptime	Efficiency gains; continuous optimization; compliance support	Explainability; cybersecurity; data needs	[53,54,55,56,57]
DTs	Virtual replica for what-if analysis, validation, and prescriptive control	Scenario-based energy/CO₂; flexibility/resilience KPIs	De-risking interventions; predictive/prescriptive ops	Model upkeep; integration effort	[29,58,59]

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Spilotro, C.; Secundo, G.; Magaletti, N.; Zini, E.; Tognon, C.; Zerega, A. Data-Driven ESG KPIs Optimization: A Framework for Smart Buildings and Smart Factories. Sustainability 2025, 17, 10837. https://doi.org/10.3390/su172310837

AMA Style

Spilotro C, Secundo G, Magaletti N, Zini E, Tognon C, Zerega A. Data-Driven ESG KPIs Optimization: A Framework for Smart Buildings and Smart Factories. Sustainability. 2025; 17(23):10837. https://doi.org/10.3390/su172310837

Chicago/Turabian Style

Spilotro, Claudia, Giustina Secundo, Nicola Magaletti, Ettore Zini, Chiara Tognon, and Angelo Zerega. 2025. "Data-Driven ESG KPIs Optimization: A Framework for Smart Buildings and Smart Factories" Sustainability 17, no. 23: 10837. https://doi.org/10.3390/su172310837

APA Style

Spilotro, C., Secundo, G., Magaletti, N., Zini, E., Tognon, C., & Zerega, A. (2025). Data-Driven ESG KPIs Optimization: A Framework for Smart Buildings and Smart Factories. Sustainability, 17(23), 10837. https://doi.org/10.3390/su172310837

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Data-Driven ESG KPIs Optimization: A Framework for Smart Buildings and Smart Factories

Abstract

1. Introduction

2. Literature Background: Digital Technologies and Data-Driven Approaches to ESG KPIs Management

2.1. The Hardware Foundation: Data Acquisition

2.2. The Software Ecosystem: From Data to Actionable Intelligence

2.3. The Key Challenges

3. Research Methodology

4. Results

4.1. Smart Building

4.2. Smart Factory

5. Discussion

Implications

6. Conclusions

Limitations and Future Research

Author Contributions

Funding

Informed Consent Statement

Data Availability Statement

Conflicts of Interest

Appendix A

References

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