Smart Cities

Urban green spaces play a critical role in mitigating heat stress and enhancing urban livability, in line with the objectives and expectations of the United Nations Sustainable Development Goals 10 (Reduced Inequalities) and 11 (Sustainable Cities and Communities). This study employs Physarealm (Grasshopper), a lightweight agent-based simulation (ABS) model, to dynamically simulate pedestrian behaviors for different mobility groups. Together with Space Syntax, the results—time-extended movement and interaction patterns—are conceptualized as a relational configuration of green space provision (supply), pedestrian activity intensity (demand), and thermal exposure (environmental resistance). Three contrasting urban areas in northern Italy (Lambrate, Bolognina, and Ispra) are selected as case studies. The results demonstrate that urban inequality cannot be sufficiently explained by the inadequacy of single components, but emerges from imbalanced relational configurations of supply, demand, and environmental resistance. In May, 100% and 95% of traversed cells in Lambrate and Bolognina fall within the high-heat-stress range (>32 °C), compared with 59% in Ispra. Correspondingly, average green provision within the 5 min walking range is 5.4% in Lambrate, 7.2% in Bolognina, and 37% in Ispra. By uncovering relational mismatch patterns that are often overlooked in conventional urban analyses, this study enables a multi-dimensional diagnosis of imbalances. By positioning ABS as a front-end process generator and Space Syntax as a structural interpretation step, it demonstrates how dynamic behavioral processes can be reorganized into network-scale diagnostic representations. The study supports a climate-sensitive and human-centered diagnosis of walkability and green space accessibility, while contributing a transferable analytical approach for identifying relational inequality patterns within open urban data science contexts. Full article

Smart cities increasingly depend on electrical grid infrastructures capable of operating under high levels of digitalization, decentralization, and intelligence while maintaining reliability, security, and governance at the city scale. However, conventional power systems, historically designed for centralized generation and passive operation, are poorly aligned with the operational complexity, multi-actor coordination, and cross-sector integration characteristic of urban energy systems. This review develops an architecture-first perspective on smart-city electrical grids, tracing their evolution from digitalized power networks to decentralized and AI-enabled urban energy ecosystems. Rather than focusing on individual technologies, the study evaluates grid architectures using a multi-layer framework that integrates physical grid infrastructure, distributed energy resources and microgrids, communication and data platforms, intelligence placement, cybersecurity exposure, and governance accountability. Smart-city grid architectures are assessed using deployability beyond pilot projects, auditability, and regulatory alignment as primary evaluation criteria alongside conventional technical considerations. Through this perspective, the review explains a recurring pattern observed in the literature: many technically mature smart-grid solutions fail to scale in real urban deployments due to architectural fragmentation and governance constraints. By synthesizing insights from power systems engineering, information and communication technologies, and smart-city research, the paper highlights architectural trade-offs related to decentralization, interoperability, resilience under compound threats, and assisted autonomy. The resulting framework supports researchers, system designers, and policymakers in the coordinated development of resilient, secure, and governable electrical grids for future smart-city energy systems. Full article

While Low Emission Zone (LEZ) enforcement cameras provide a constant stream of traffic data, such resources remain significantly underexploited for urban mobility planning, as their current application is restricted to enforcing vehicle access regulations and issuing fines. This paper presents MobiCugat, a framework demonstrating that Automatic Number Plate Recognition (ANPR) camera data from a municipal LEZ network can serve as the calibration backbone for high-fidelity, city-scale traffic simulations for a policy-testing Digital Twin. The case study is Sant Cugat del Vallès (Barcelona), where the local council sought to evaluate new scenarios for the area using an evidence-based, data-driven approach. Vehicle detection records from 102 LEZ ANPR cameras were processed into 15-min traffic intensity time series through a General Data Protection Regulation (GDPR)-compliant pipeline. The Realistic Urban Traffic Generator (RUTGe), a Deep Reinforcement Learning-based tool, was used to generate SUMO-compatible traffic demand whose simulated detector counts reproduce the observed camera-based intensities. The resulting simulations reproduced the observed detector-level traffic intensities with ${\mathrm{MARE}}_{\%}$ values between $2.29\%$ and $2.90\%$ across representative morning peak, midday off-peak, and evening peak traffic conditions. Additionally, camera analysis of over $470,000$ vehicle records revealed that resident traffic ($37.4\%$) dominates over through-traffic ($3.8\%$), significantly refining prior survey-based estimates. Our high-fidelity simulation tool based on SUMO, features realistic traffic patterns calibrated through AI-driven techniques, enabling the evaluation of diverse ’what-if’ scenarios—such as road closures, pedestrianization, changes in traffic direction, or relocation of bus stops. By quantifying the impact of these interventions, our tool facilitates informed decision-making prior to physical implementation. The proposed pipeline is cost-effective, privacy-preserving, and directly replicable for any municipality operating an LEZ camera network, offering a scalable template for evidence-based urban mobility planning, aligned with the European Strategy for Data and the EU Green Deal goals for sustainable mobility. Full article

Much of the research and proposed industrial deployment of Remote Operations (ROs) in support of automated vehicles is founded on the optimistic premise that in-vehicle standby drivers and Safety Officers (SOs) can easily be replaced with ROs, with some commercial models proposing that a single RO supervise over 30 vehicles. However, emerging evidence suggests that the RO task is fundamentally different from the in-vehicle driving task. Furthermore, communications latency and reliability constraints, coupled with fragmented attention and altered task demands, introduce distinctive human factor challenges. These include degraded situational awareness, increased cognitive workload, and reduced capacity for timely intervention. The result is a widening gap between what is commercially desirable and what may be operationally appropriate. This paper argues that the central question for remote operation in support of automated vehicles is not one of technical feasibility but of human-centred appropriateness, and debates which RO roles should continue to be developed and which should be constrained or avoided. We present a synthesis of research on remote vehicle operations, identifying recurring human-factor limitations and mapping them to proposed remote tasks. The paper concludes with targeted recommendations for designers, operators, and regulators intended to question the scaling of teleoperation models and to reframe the debate from “Can we teleoperate?” to “Under what conditions should we?” Full article

Temperature has a significant impact on the operation and performance of electronic systems. Conventional approaches focus on stabilizing electronic systems to maintain functionality under unfavorable thermal conditions, typically at the expense of increased consumption. This paper adopts a multi-objective approach to identify the Pareto-optimal (PO) trade-off across varying temperatures between functionality and consumption of low-power radio transceivers used in the Internet of Things (IoT) and wireless sensor networks. Building upon the established two-segment PO trade-off controlled by supply voltage and output power settings, between engaged and achieved transmission power, parameters directly associated with energy consumption and transmission quality, we analyze the influence of temperature on the Pareto front. We find that decreasing the temperature improves both engaged power and achieved transmission power simultaneously. Therefore, we propose a novel Pareto-optimal temperature-opportunistic wireless communication approach that exploits temperature variability by selecting favorable temperature conditions for transmission. We also identify the spatio-temporal potential of temperature variations across a four-dimensional network deployment space, particularly in temperature-dynamic urban environments of smart city infrastructure supporting massive IoT. Experiments on a modern Texas Instruments CC1200 transceiver confirm that the power savings of approx 30% and nearly 450 times increase in achieved transmission power are attainable for a temperature difference of 60 °C, corresponding to realistic conditions between the ambient air and a black-painted surface. Full article

The growing environmental crisis and rapid urbanization have made the shift to renewable energy systems even more important for smart city development. In today’s cities, such renewable energy sources as solar photovoltaics, wind energy, hybrid systems, and battery energy storage are no longer just separate assets. They are now important parts of smart grids, intelligent buildings, and urban infrastructure that work together. However, putting these systems in cities on a large scale makes it harder to monitor, control, integrate, scale, and work with them in real time. In this setting, the Internet of Things (IoT) and edge computing are technologies that make it possible to turn traditional renewable energy systems into smart, responsive, and self-sufficient urban energy systems. IoT-based monitoring and control systems let city operators, utilities, and policymakers gather real-time data, improve grid stability, optimize energy flows, and better integrate distributed renewable energy sources into smart city ecosystems. Edge computing makes these features even better by allowing for low-latency processing, more localized decision-making, and less reliance on centralized cloud infrastructures. This paper offers a thorough and methodical examination of contemporary IoT- and edge-enabled technologies used to monitor, control, and integrate renewable energy systems; specifically highlighting their significance in smart city and smart grid applications. The review combines the most recent research on hardware platforms, communication protocols, data processing architectures, and edge–cloud coordination mechanisms used in solar, wind, and hybrid energy systems. Additionally, this review synthesizes architectural design principles extracted from analyzed studies to guide the development of scalable, resilient, and cost-efficient renewable energy monitoring systems. This study offers a structured foundation for the design of scalable, resilient, and cost-effective renewable energy management systems that align with the sustainability, efficiency, and intelligence goals of future smart cities by analyzing cutting-edge solutions and pinpointing significant technological trends, challenges, and research deficiencies. This review also highlights its contribution vis-à-vis previous surveys by stressing the inter-domain comparison across solar, wind, and hybrid systems. It focuses, in particular, on edge–cloud coordination and architecture-level trade-offs pertinent to smart grid and smart city deployments. Full article

This paper presents a closed-loop, CPU-aware traffic-control framework for SDN in 5G/6G multi-tenant edge environments based on commodity KVM/OVS infrastructures. It couples fine-grained data-plane telemetry via eBPF with adaptive XDP rate limiting, coordinated by a PID controller in the OVS datapath. Unlike control-plane polling, it provides real-time feedback between CPU utilization and traffic regulation. Experiments in a virtualized multi-tenant OVS testbed (KVM/virtio-net) keep CPU below per-slice CPU targets (e.g., 1.02% for a 3% setpoint), with an under-target bias that avoids overshoot while preserving stable forwarding. We attribute this bias, at light load, to a supply-limited regime, conservative per-slice CPU accounting, and stability-oriented PID tuning, and introduce a low-latency profile that mitigates this bias for latency-sensitive slices. The XDP datapath achieves 1–3 $\mathsf{\mu }$s per-packet processing with 5–10% additional CPU overhead relative to an uninstrumented baseline, while using less CPU than OVS policing at comparable throughput. A 3% per-slice CPU target balances isolation and throughput, while 2% yields stricter isolation at the cost of higher packet loss. Software-based rate limiting can induce cross-slice interference; effective isolation holds below 1 Gbps aggregate load. Above this, shared Linux kernel overhead degrades isolation, causing significant loss; thus, XDP alone cannot ensure line-rate isolation, motivating SmartNICs. The design improves efficiency, predictability, and isolation, laying a foundation for intelligent traffic management in future resource-intensive applications. Full article

Medical resources are primary public goods, but the nature of their distribution across different-sized cities is unclear. Here, we examined the nonlinear scaling relationship between urban populations and medical resources in China, moving beyond the limitations of traditional linear evaluation metrics. Taking 296 Chinese cities as samples, we constructed scaling law models between population size and three medical resource indicators: the numbers of hospital beds, doctors, and hospitals. The results show that the number of doctors maintained a linear scaling relationship on the whole (scaling exponent $\beta$: 0.98–1.06), while the numbers of hospitals ($\beta$: 0.79–0.91) and hospital beds ($\beta$: 0.91–0.99) both exhibited sublinear scaling (2000–2022), confirming the existence of economies of scale in basic medical facilities. The Scale-Adjusted Metropolitan Indicator (SAMI) further reveals spatial agglomeration characteristics: the northern and southwestern regions of China perform notably better than expected in hospital availability, while provincial cites show advantages in terms of the numbers of beds and doctors. This study quantifies the nonlinear allocation of medical resources across Chinese cities and advocates for a reasonable allocation mechanism to promote medical equity. Full article

Background: Smart-city road and intersection management increasingly aims to smooth bus operations and reduce stop-and-go driving, but cities often lack auditable indicators linking routine fleet data with comparable energy and environmental KPIs. Methods: This study develops a Monitoring–Reporting–Verification (MRV) workflow for daily bus records from a 2024 Polish metropolitan fleet (diesel, compressed natural gas (CNG), hybrid, and battery-electric buses). Records were quality checked, harmonized to MJ/km, aggregated to bus-month observations, and analyzed using a linear mixed-effects model with propulsion technology, season, and activity level as fixed effects and vehicle-level random intercepts. Environmental impacts were then calculated under well-to-wheel (WTW) boundaries using Environmental Footprint 3.1 (EF 3.1) impact categories, Poland’s 2024 electricity mix, and illustrative electricity-mix scenarios through 2050. Results: Relative to diesel, BEV and HEV were associated with lower adjusted energy intensity (ratios 0.272 and 0.681, respectively), whereas the CNG–diesel contrast was directionally higher but statistically inconclusive under the available CNG sample. BEV energy intensity more than doubled in winter in descriptive terms, and vehicle-specific heterogeneity remained high (ICC ≈ 0.61). The BEV climate profile improved under electricity decarbonization, while some EF categories showed mix-dependent trade-offs. The 3–10% traffic-management variants are interpreted as screening assumptions rather than measured ITS effects. Conclusions: Routine bus records can support auditable MRV and preliminary screening of fleet and corridor interventions, but causal traffic-management evaluation requires route-level trajectory, congestion, and before–after data. Full article

With the rapid electrification of urban transportation and increasing penetration of renewable energy, maintaining frequency stability in smart-city multi-microgrids (MMG) systems increasingly depends on coordinated vehicle-to-grid (V2G) flexibility. However, existing load frequency control strategies typically treat electric vehicles (EVs) as homogeneous resources and overlook the impacts of charging-infrastructure diversity, user mobility constraints, and extreme weather conditions on regulation availability. To address these challenges, this study proposes a weather-adaptive intelligent load frequency control strategy for smart-city MMG considering heterogeneous charging stations and energy requirements of EV users. Fast and slow charging infrastructures are modeled separately to reflect their distinct regulation characteristics, while time-varying charging and discharging margins are derived from travel demand, parking duration, and state-of-charge preferences and further adjusted under extreme weather scenarios. Based on these dynamic constraints, an enhanced multi-agent soft actor–critic (MA-SAC) controller coordinates micro gas turbines and charging stations for distributed frequency regulation. Simulations demonstrate MA-SAC outperforms PID, Fuzzy, and MA-DDPG methods, achieving a 98.51% frequency excellent rate normally and 91.47% during extreme weather. It reduces maximum deviations by up to 80% versus PID, while preserving user travel requirements. The proposed framework provides a practical pathway for integrating electrified mobility into resilient smart-city MMG frequency regulation. Full article

Urban flexibility research is expanding across buildings, electric vehicles (EVs), distributed energy resources (DERs), storage, positive energy districts (PEDs), digital twins, and interoperability platforms. These strands are often reviewed separately, although urban distribution operators must manage their combined impacts on the same feeders. This paper presents a PRISMA 2020-aligned systematic review with evidence mapping and narrative synthesis of feeder-aware coordination in smart-city electricity systems. Searches of Scopus, Web of Science, IEEE Xplore, ScienceDirect, and citation chasing identified 312 records; 127 studies were included after screening and eligibility assessment, 101 entered the quantitative mapping sample, and 31 formed the deep-synthesis anchor core. Sparse contingency tables were analyzed with Monte-Carlo permutation chi-square tests and bootstrap confidence intervals for Cramér’s V, while ordinal variables were summarized with medians and interquartile ranges. Explicit feeder grounding was concentrated in grid-oriented and EV-oriented studies, whereas many AI/digital-twin and interoperability studies were less often validated against distribution-network operation. Economic and peak-flexibility indicators were reported far more often than interoperability, cybersecurity, or validation-maturity indicators in the anchor core. The synthesis also showed that deployment-oriented work depends on clearer treatment of standards, co-simulation workflows, regulatory instruments, and stakeholder roles. The evidence base is heterogeneous, English-only, and single-coded, so the quantitative results are descriptive rather than population-level. The review contributes a transparent three-layer corpus design (127 included/101 mapped/31 anchor), a domain-specific specialization of SGAM/IEEE 2030 for urban feeder orchestration, an operational digital-twin definition and validation ladder, a retrofittable benchmarking framework, and a practical roadmap for DSOs, municipalities, aggregators, EV operators, building managers, and ICT providers. Full article

Environmental sound classification is an important component of smart city sensing systems, supporting applications such as urban noise analysis, public safety monitoring, and real-time situational awareness. However, high-accuracy models are often difficult to deploy on low-power edge devices because of memory, computational, and latency constraints. This study aims to address this deployment gap by developing a lightweight compression pipeline for a hybrid convolutional and Kolmogorov–Arnold Network-based model. The proposed pipeline consists of three stages. First, structured channel pruning is applied to remove redundant convolutional filters while preserving hardware-efficient dense operations. Second, selective quantization-aware training is applied to the most computation-dominant layers, namely the third convolutional layer and the fully connected layer. Third, knowledge distillation is used to recover accuracy by training the compressed model under the guidance of the baseline model. Experiments were conducted on ESC-10, ESC-50, FSC22, and UrbanSound8K. The proposed pipeline reduced the average parameter count from 511,033 to 50,774 and reduced the model size while maintaining competitive accuracy across all benchmarks. The final model preserved the baseline accuracy of 96.75% on ESC-10, while accuracy decreased only from 88.25% to 86.50% on ESC-50, from 87.92% to 86.38% on FSC22, and from 85.13% to 84.52% on UrbanSound8K. These results show that the proposed compression pipeline provides an effective accuracy–efficiency trade-off for real-time audio classification on resource-constrained devices. Therefore, the resulting compressed model supports the scalable deployment of distributed acoustic sensing systems for real-time smart city monitoring and decision-making. Full article

Persistent traffic congestion and the need for efficient traffic monitoring have increased the demand for automated vehicle-analysis systems based on CCTV footage. This study presents a CCTV-based vehicle monitoring system that integrates vehicle detection, tracking, counting, public/private vehicle class prediction, seven-category vehicle-type prediction, vehicle-color recognition, and traffic-state estimation using YOLOv12 and DeepSORT. To reduce manual annotation effort during the initial training stage, a semi-automated method for generating synthetic composite road scenes was developed by combining cropped vehicle images and road-background images. The detector was first trained on 10,000 synthetic images and then sequentially fine-tuned on real CCTV data. Four real-world traffic video clips from Metro Manila were used in the study. Three 5 min clips were used within the staged refinement workflow: the first two for iterative refinement and the third for final post-refinement evaluation of the adapted model. A separate fourth CCTV clip was reserved exclusively for blind evaluation without on-the-fly retraining. The final system achieved average accuracies of 97% for public/private vehicle class prediction, 90% for seven-category vehicle-type prediction, 82% for vehicle-color recognition, and 96.67% for vehicle counting on the final evaluation video. The results show that synthetic pretraining combined with limited real-world fine-tuning can improve performance in CCTV-based vehicle monitoring while reducing the amount of manually labeled real-world data required. The study also discusses the limitations of the current evaluation protocol and the need for broader multi-location testing. Full article

Precise assessment of commercial service facilities (CSFs) is a vital pillar for megacity governance. However, existing evaluations rely on static population and 2D metrics, overlooking behavioral heterogeneity and 3D spatial supply at the micro scale. This study constructs a “3D Supply–Group Demand–Matching” framework at the community level. On the supply side, a Building Coupling Entropy (BCE) model integrates 3D volume and morphology to characterize service capacity. On the demand side, a dynamic behavioral model measures multi-group needs. Mismatch patterns are identified using the Entropy-modified Spatial Disparity Ratio (ESDR). Using Guangzhou as a case, the results reveal three paradigms: (1) Core districts exhibit rigid path dependency, where first-tier sub-districts rose from 48 to 51, and elderly service shortages in old areas plummeted by nearly 80% via micro-regeneration; (2) Growth poles show spatial fragmentation, with core labor demand spilling over but infrastructure lagging, creating a fast production–slow urbanism mismatch; (3) Far-suburban areas reduced extreme-shortage sub-districts from 38 to 34, identifying resource islands besieged by residential demand. Overall, the framework elucidates the shape–flow mismatch mechanism and provides a transferable basis for precision zonation governance, supporting a shift from static quantity-based allocation to dynamic quality-oriented provision in high-density megacities. Full article

Street lighting systems are essential for ensuring nighttime road safety and visibility. The integration of solar photovoltaic (PV) systems into street lighting infrastructure improves energy efficiency and sustainability; however, the mismatch between daytime energy generation and nighttime lighting demand requires effective energy management solutions. In addition, long-distance electrical connections introduce voltage drop constraints, which are often overlooked in conventional design approaches. This study addresses the integration of lighting design, electrical constraints, and techno-economic performance in nanogrid-based LED street lighting systems for secondary roads. A unified framework is developed to evaluate lighting performance, PV–battery sizing, voltage drop behavior, and lifecycle cost under different system architectures. Optimal pole spacing and luminaire ratings are determined using DIALux, while PV–battery configurations are optimized using HOMER Pro based on site-specific solar irradiance. The analysis focuses on voltage drop as the key electrical constraint and examines its impact under decentralized and centralized nanogrid configurations (25%, 50%, and 100%) in both stand-alone and grid-connected modes. The results show that increasing centralization reduces component redundancy but significantly increases cable length, conductor sizing, and infrastructure cost. A techno-economic assessment with lifecycle cost and sensitivity analysis indicates that a 25% centralized configuration reduces total system cost by approximately 23% compared to fully decentralized systems while avoiding excessive cabling costs. These findings demonstrate that voltage drop and electrical infrastructure constraints play a decisive role in determining optimal system design, highlighting the importance of system-level integration rather than isolated optimization of lighting or energy components. Full article

Australian local governments are increasingly deploying smart city technologies to manage climate-related shocks and chronic stresses, yet implementation often remains fragmented and difficult to embed in routine practice. Many initiatives stall in “pilot-forever” cycles because decision rights, equity safeguards, operational integration, and learning systems are applied inconsistently. This paper introduces the Smart Urban Resilience Framework (SURF), a phase-gated, tier-aware governance framework designed to support the institutionalisation of smart urban resilience through more transparent and evidence-based decision-making. The SURF is grounded in an integrated evidence-to-design synthesis drawing on a systematic review, a comparative analysis of Tier 1 and Tier 2 Australian local government strategies, an in-depth Sydney case study, and stakeholder interviews. Although empirically grounded in Australian local government, the SURF is designed as a governance architecture that may be adapted in comparable municipal settings elsewhere. The framework comprises a staged pathway, two evidence gates, and four concurrent action tracks, supported by enabling layers and traceable evidence tools. The SURF is presented as a practical implementation architecture intended to support more transparent and defensible decisions about funding, scaling, refining, or retiring smart resilience initiatives. In this paper, resilience is operationalised through a service continuity lens, focusing on how digital initiatives can be embedded in governance and delivery systems to support the continuity of essential local government services under stress. Full article

Artificial intelligence (AI) is increasingly embedded in how cities are governed, shaping decisions on mobility, land use, public services, and environmental management. Yet urban AI is predominantly governed through fragmented frameworks designed at national or corporate scales, offering limited guidance for municipal decision-making and overlooking place-specific social and ecological consequences. As the level of government closest to everyday urban life, cities are uniquely positioned to steer AI toward public value, but face persistent tensions between efficiency, equity, accountability, and sustainability. This paper argues that responsible urban AI cannot be governed through top-down or one-size-fits-all approaches. To address this, the study aims to conceptualise and advance a ground-up model of responsible urban AI governance that places cities and local governments at the centre of decision-making. It addresses the following research question: How can municipal authorities translate high-level ethical principles into practical, context-sensitive governance arrangements that respond to local capacities, risks, and public values? Drawing on global governance principles and illustrative city experiences, we propose a locally grounded, stage-based framework for municipal AI governance. The framework addresses institutional capacity gaps, fragmented responsibilities, and algorithmic externalities, advancing a participatory, place-sensitive, and adaptive model that aligns urban AI innovation with democratic legitimacy, social justice, and sustainable urban futures. Full article

Comparative, experimentally grounded evidence for selecting smart city IoT data-layer architectures remains limited, complicating practical design decisions. This study provides an applied architecture decision-making guide by evaluating seven serverless data-layer architectures within a clearly defined service boundary (The Things Network, Azure-managed ingestion services, and Delta Lake persistence on object storage). Using a 21-day pilot deployment with nine LoRaWAN sensors, we compare ingestion completeness, median ingestion latency (estimated from TTN receive timestamps to Delta Lake commit times), cloud costs within an explicit boundary (ingestion, compute, and storage), and implementation/operational complexity proxies. Under the observed workload, TTN Storage Integration offers the lowest-cost archival ingestion via batching, Event Grid provides the most cost-effective near-real-time option among reliable pipelines, and Event Hubs demonstrates the highest ingestion completeness. The results are synthesized into practical guidance that maps common smart city application requirements to appropriate serverless ingestion patterns. Full article

Buildings represent 40% of the European Union’s energy consumption and 36% of its greenhouse gas emissions. Nursing homes are among the buildings that consume the most energy. The objective of this study was to make predictive models of Energy Consumption, Energy Costs, and CO₂ Emissions in nursing homes using different variables. To do this, data from 20 public nursing homes located in Extremadura (Spain) during the 2019–2023 period were analyzed. All the buildings were built or renovated between 1995 and 2009; the useful area and the number of residents were in the range of 1332–10,880 m² and 24–254 residents. A statistical analysis was performed using multivariable linear regression. During the research, equations that allow for the estimation of the annual Energy Consumption, Energy Costs and CO₂ Emissions of nursing homes, according to the useful area and number of residents, were found. The R_adj² was 0.9710, 0.9744 and 0.9742, respectively. The quality of the models obtained was contrasted using the mean absolute error (MAE), the relative error (RE) and the root mean square error (RMSE), together with the assessment of multicollinearity through the Variance Inflation Factor (VIF). The findings of this study may prove beneficial for stakeholders within the elder care sector. Full article