Search Results (80)

Decarbonization of transport is a key element of the energy transition and of achieving the Sustainable Development Goals. Integration of renewable energy into transport systems is assessed together with the potential of electric, hybrid, hydrogen, and biofuel-based propulsion to enable low emission mobility. Literature published from 2019 to 2025 is synthesized using structured searches in Scopus, Web of Science, and Elsevier and evidence is integrated through a thematic comparative approach focused on energy efficiency, life cycle greenhouse gas emissions, and technology readiness. Quantitative findings indicate that battery electric vehicles typically require about 18 to 20 kWh per 100 km, compared with about 60 to 70 kWh per 100 km in energy equivalent terms for internal combustion cars. With higher renewable shares in electricity generation, life cycle CO₂ equivalent emissions are reduced by about 60 to 70 percent under average European grid conditions and up to about 80 percent when renewables exceed 50 percent. Energy storage and smart grid management, including vehicle to grid operation, are identified as enabling measures and are associated with peak demand reductions of about 5 to 10 percent. Hydrogen and advanced biofuels remain important for heavy duty, maritime, and aviation segments where full electrification is constrained. Full article

Intelligent Transportation Systems (ITSs) aim to improve mobility and reduce congestion, yet current solutions still struggle with scalability, sensing bottlenecks, and inefficient computational resource usage. These limitations impede the shift towards environmentally responsible mobility. This work introduces ORQCIAM (Orchestrated Reasoning based on Quantum Computing and Intelligence for Advanced Mobility), a modular framework that combines Quantum Computing (QC) and Large Language Models (LLMs) to enable real-time, energy-aware decision-making in ITSs. Unlike conventional ITS or AI-based approaches that focus primarily on traffic performance, ORQCIAM explicitly incorporates sustainability as a design objective, targeting reductions in travel time, fuel or energy consumption, and CO₂ emissions. The framework unifies cognitive, virtual, and federated sensing to enhance data reliability, while a hybrid decision layer dynamically orchestrates QC–LLM interactions to minimize computational overhead. Scenario-based evaluation demonstrates faster incident screening, more efficient routing, and measurable sustainability benefits. Across tested scenarios, ORQCIAM achieved 9–18% reductions in travel time, 6–14% lower estimated CO₂ emissions, and around a 50–75% decrease in quantum-optimization calls by concealing QC activation during non-critical events. These results confirm that dynamic QC–LLM coordination effectively decreases computational overhead while supporting greener and more adaptive mobility patterns. Overall, ORQCIAM illustrates how hybrid QC–LLM architectures can serve as catalysts for efficient, low-carbon, and resilient transportation systems aligned with sustainable smart-city goals. Full article

The Green Internet of Things (GIoTs) has emerged as a transformative paradigm for environmental conservation, enabling autonomous, self-sustaining sensor networks that operate without batteries and with minimal ecological footprint. This approach is especially critical for long-term mangrove monitoring in smart coastal cities, where conventional battery-powered systems are impractical due to frequent, costly, and environmentally disruptive replacements that hinder continuous data collection. This paper presents a self-sustaining GIoT sensing system for mangrove monitoring powered by sedimentary microbial fuel cells (SMFCs), enabling perpetual, battery-less, and zero-emission operation. A spatial dynamic energy management (DPM) strategy is implemented for the efficient integration of a microcontroller unit with a LoRa wireless communication transceiver and the SMFC harvested energy, ensuring a balanced self-sustained approach into a GIoT sensing network. Experimental results demonstrate an average power consumption of 190.45 μW per 14-byte data packet transmission, with each packet containing pH, electrical conductivity and ambient temperature measurements from the mangrove environment. Under the spatial DPM strategy, the network of four sensing nodes exhibited an energy consumption of 1.14 mWh. Given a harvested power density of 15.1 mW/m² from the SMFC, and utilizing a 0.1 F supercapacitor as an energy buffer, the system can support at least six consecutive data transmissions. These findings validate the feasibility of sustainable, low-power GIoT architectures for ecological monitoring. Full article

Background/Objectives: Distortion-product otoacoustic emissions (DPOAEs) are suited to longitudinal cochlear assessment, but inter-system differences may confound interpretation across clinics. This study compared DPOAE outcomes across four commercial systems and evaluated their within-session repeatability. Methods: Adults with normal hearing (84 ears) were tested using the HearID DP (Mimosa Acoustics), SmartDPOAE (Intelligent Hearing Systems), Eclipse DPOAE20 (Interacoustics), and Echoport ILO 292 USB I (Otodynamics). DPOAEs were recorded at 1, 1.5, 2, 3, 4, 6 and 8 kHz using a criterion of ≥6 dB signal-to-noise ratio. Two measurements per ear were obtained, with the probe repositioned between sessions. Results: All systems showed similar frequency response profiles but substantially different absolute values. Between-system amplitude differences were smallest at 1.5–4 kHz and largest at 6 kHz. Noise floors varied considerably: HearID DP and SmartDPOAE were best (lowest) while Echoport ILO 292 USB I and Eclipse DPOAE20 were worst (highest), with inter-system differences most prominent between 1.5 and 4 kHz. HearID DP achieved the highest detection rates (84/84 ears at key frequencies). Test–retest reliability was good across all systems. The standard error of measurement varied from 0.99 to 2.88 dB, the smallest being the HearID DP. Within-session differences were typically ≤2 dB, with the best repeatability between 1.5 and 6 kHz. Conclusions: Despite similar frequency responses, clinically significant differences exist between DPOAE systems in terms of noise floors, signal-to-noise ratios, and response amplitudes. Inter-device variations frequently exceeded minimum detectable change values, meaning that DPOAE devices cannot be considered clinically interchangeable. These findings underscore the need for industry-wide standards to enable reliable cross-clinic comparisons. Full article

Buildings account for a major share of global energy demand and emissions, prioritizing lighting for efficiency improvements. This study evaluates a daylight-assisted lighting system’s energy and environmental performance through a fully measurement-based approach. Monitored illuminance data were processed within a transparent workflow linking lighting demand to power use, electricity consumption, and life-c ycle greenhouse gas emissions. Energy demand was derived from luminaire efficacy and an illuminated area, while environmental impacts were quantified using an attributional life cycle assessment (LCA) framework consistent with ISO 14040/14044 standards. Use-phase carbon footprints were calculated with regional grid emission factors, and manufacturing, transport, and end-of-life stages were included as background conditions. The results demonstrate that the daylight-aware control strategy achieved an average electricity reduction of 17% (95% CI: 15.7–18.3%) compared to the constant baseline, with the greatest savings occurring in daylight-rich months. When translated into environmental terms, these operational reductions yielded a corresponding ~17% decrease in use-phase CO₂ emissions under a regional grid factor of 0.40 kg CO₂/kWh. Importantly, the system’s embodied impacts were outweighed within an operational payback period of approximately 18–20 months, underscoring both environmental and economic viability. Sensitivity analyses across illuminance thresholds, luminaire efficacy, and grid emission factors confirmed the robustness of these outcomes. Overall, the study provides a reproducible methodology that directly integrates empirical daylight measurements with life-cycle assessment, clarifying the contribution of smart lighting control to sustainable building design. Full article

This study evaluated soil–atmosphere greenhouse gas (GHG) fluxes—including carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N₂O)—in cacao agroecosystems on São Tomé Island, Central Africa. The field campaign was conducted between April and May 2025, coinciding with the transition from the short rainy season to the onset of the dry period. The sampling design comprised two system types (biodiverse and conventional), two crop development stages (growing and productive), and two climatic zones (wet and dry). Gas fluxes were measured using the static chamber method and analyzed in relation to climatic, topographic, and edaphic variables. CO₂ fluxes were the dominant contributor to total emissions, accounting for approximately 97.4% of the global warming potential (GWP), while CH₄ and N₂O together contributed less than 3%. The highest CO₂ emissions occurred in conventional systems during the growing stage in the wet zone (125.5 ± 11.41 mg C m⁻² h⁻¹). CH₄ generally acted as a sink, particularly in conventional systems in the dry zone (−12.58 ± 2.35 μg C m⁻² h⁻¹), although net emissions were detected in biodiverse systems in the wet zone (5.08 ± 1.50 μg C m⁻² h⁻¹). The highest N₂O fluxes were observed in conventional growing systems (32.28 ± 5.76 μg N m⁻² h⁻¹). GHG dynamics were mainly regulated by climatic factors—such as air temperature, relative humidity, and precipitation—and by key edaphic properties, including soil pH, soil organic carbon, soil temperature, and clay content. Projected GWP values ranged from 9.05 ± 2.77 to 40.9 ± 6.23 Mg CO₂-eq ha⁻¹ year⁻¹, with the highest values recorded in conventional systems in the growing stage. Overall, our findings underscore the potential of biodiversity-based agroforestry as a climate-smart practice to mitigate net GHG emissions in tropical cacao landscapes. Full article

This paper aims to explore the role artificial intelligence (AI) technologies play in optimizing energy consumption levels in urban logistical systems, including the strategic implications of such technologies on smart supply chain management (SCM) in Dubai. The mixed-methods study was adopted and applied, in which quantitative measures of the performance of 16 public–private organizations were merged with qualitative evidence provided through semi-structured interviews and document analysis. AI solutions that were assessed in the research included the use of predictive routing, dynamic fleet scheduling, IoT-base monitoring, and smart warehousing. Results indicate an overall decrease of 13.9% in fuel consumption, 17.3% in energy and 259.4 kg in monthly CO₂ emissions by the organization on average by adopting AI. These findings were proven by the simulation model, which estimated that the delivery efficiency would increase within an AI-driven scenario and be scalable in the future. Other important impediments were also outlined in the study, such as constraint of legacy systems, skills gap, and interoperability of data. Implications point to the necessity of the incorporation of digital governance, data protocol standardization, and AI-compatible city planning to improve the urban SCM of Dubai, through the terms of sustainability and resilience. In this study, a transferable structure is provided that can be utilized by cities that are interested in matching AI innovation and energy and logistics goals, in terms of policy objectives. Full article

Technical losses (TLs) in power systems are an inevitable outcome of energy dissipation in components such as conductors, transformers, and transmission lines. These losses arise from the combined effects of material properties, operational conditions, and environmental factors, creating ongoing challenges for energy efficiency and grid sustainability. Their reduction requires a coordinated approach that integrates material improvements, smart grid technologies, and optimized operational practices. Reducing TLs not only improves economic efficiency but also contributes significantly to global sustainability efforts by enabling more efficient energy use and reducing carbon emissions associated with power generation. A review of recent publications shows that the literature on network losses is heavily skewed toward non-technical losses (NTLs), with TL-focused studies being fewer, often dated, and lacking comprehensive scope. This paper addresses the existing research gap by presenting a comprehensive, section-oriented taxonomy of TL mechanisms in power systems, accompanied by precise definitions for each category and a direct linkage between these categories and applicable loss mitigation measures. In addition, selected real-world projects and global initiatives aimed at reducing TLs, together with current regulatory approaches, emerging trends in this domain, and an assessment of the maturity level of technologies employed for TL reduction, are analyzed. This study aims to serve as a scientific reference to support future research and to guide policymakers, regulators, and utilities in developing more effective strategies for minimizing TLs. Full article

The transition towards a low-carbon energy system remains a critical challenge for regions heavily dependent on fossil fuels, such as Andalusia. This study proposes an energy planning framework based on the Low Emissions Analysis Platform (LEAP) to model alternative scenarios and assess the feasibility of meeting the 2030 and 2050 decarbonisation targets. Three scenarios are evaluated, the Tendential Scenario (TS01), the Efficient Scenario (ES01), and the Efficient UJA (EEUJA) Scenario, with this last being specifically designed to ensure full compliance with regional energy goals. The results indicate that, while the Tendential Scenario falls short in reducing primary energy consumption and greenhouse gas (GHG) emissions, the Efficient Scenario achieves significant progress, though it is still insufficient to meet renewable energy integration targets. The proposed EEUJA Scenario introduces more ambitious measures, including large-scale electrification, smart grids, energy storage, and green hydrogen deployment, resulting in a 39.5% reduction in primary energy demand by 2030 and 97% renewable energy penetration by 2050. Furthermore, by implementing sector-specific decarbonisation strategies for the industry, transport, residential, and services sectors, Andalusia could position itself as a frontrunner in the energy transition while minimising economic and environmental risks. These findings underscore the importance of policy enforcement, technological innovation, and financial incentives in securing a sustainable energy future. The methodology developed in this study is replicable for other regions aiming for carbon neutrality and energy resilience through strategic planning and scenario analysis. Full article

It is critical to provide proper environmental conditions in poultry houses to maintain birds’ health, boost productivity, and improve the overall economic viability of the poultry industry. Among the myriad of environmental elements, indoor air quality has been a determining factor that directly affects poultry well-being. Elevated concentrations of harmful gases—in particular Carbon Dioxide ($C{O}_2$), Methane ($C{H}_4$), and Ammonia ($N{H}_3$)—decomposition products of poultry litter, feed wastage, and biological processes have draconian effects on bird health, feed efficiency, the growth rate, reproduction efficiency, and mortality rate. Despite their importance, traditional air quality monitoring systems are often operated manually, labor intensive, and cannot detect sudden environmental changes due to the lack of real-time sensing. To overcome these limitations, this paper presents an interdisciplinary approach combining cloud computing, Artificial Intelligence (AI), and Internet of Things (IoT) technologies to measure real-time poultry gas concentrations. Real-time sensor feeds are transmitted to a cloud-based platform, which stores, displays, and processes the data. Furthermore, a machine learning (ML) model was trained using historical sensory data to predict the next-day gas emission levels. A web-based platform has been developed to enable convenient user interaction and display the gas sensory readings on an interactive dashboard. Also, the developed system triggers automatic alerts when gas levels cross safe environmental thresholds. Experimental results of $C{O}_2$ concentrations showed a significant diurnal trend, peaking in the afternoon, followed by the evening, and reaching their lowest levels in the morning. In particular, $C{O}_2$ concentrations peaked at approximately 570 ppm during the afternoon, a value that was significantly elevated (p < 0.001) compared to those recorded in the evening (~560 ppm) and morning (~555 ppm). This finding indicates a distinct diurnal pattern in $C{O}_2$ accumulation, with peak concentrations occurring during the warmer afternoon hours. Full article

Urban parking around the world faces similar challenges of inadequate space, pollution, and carbon emissions. Although various smart parking technologies have been tested and implemented, they primarily aim to reduce the time spent searching for parking, without considering the impact on air quality. In this study, the air quality in three urban garages was investigated with portable instruments at the entrance and exit gates and inside the garages. Garage emissions measured include CO₂, PM_2.5, PM₁₀, NO₂, and total VOCs. The results suggested that the PM_2.5 levels in these garages tend to be higher than the ambient levels. The emissions also exhibit seasonal variations, with the highest concentrations occurring in the summer, which are 20.32 µg/m³ in Campus Green, 14.25 µg/m³ in CCM, and 15.23 µg/m³ in Washington Park garages, respectively. PM_2.5 measured from these garages is strongly correlated (with an R² of 0.64) with ambient levels. CO₂ emissions are higher than ambient levels but within the indoor air quality limit. This suggests that urban garages in Cincinnati tend to enrich ambient air concentrations, which can affect garage users and garage attendants. Portable sensors are capable of long-term emission monitoring and are compatible with other technologies in smart garage development. With portable air sensors becoming increasingly accessible and affordable, there is an opportunity to integrate these devices with smart garage management systems to enhance the sustainability of parking garages. Full article

Urban transport is a key driver of greenhouse gas emissions in Europe, making its decarbonization essential to achieving EU climate neutrality targets. This study examines how European strategies, such as the Green Deal, the Sustainable and Smart Mobility Strategy, and the Fit for 55 package, are reflected in Romania’s transport policies, with a focus on implementation challenges and urban outcomes in Bucharest. By combining policy analysis, stakeholder mapping, and comparative mobility indicators, the paper critically assesses Bucharest’s current reliance on private vehicles, underperforming public transport satisfaction, and limited progress on active mobility. The study develops a context-sensitive reform framework for the Romanian capital, grounded in transferable lessons from Western and Central European cities. It emphasizes coordinated metropolitan governance, public trust-building, phased car-restraint measures, and investment alignment as key levers. Rather than merely cataloguing policy intentions, the paper offers practical recommendations informed by systemic governance barriers and public attitudes. The findings will contribute to academic debates on urban mobility transitions in post-socialist cities and provide actionable insights for policymakers seeking to operationalize EU decarbonization goals at the metropolitan scale. Full article

With water scarcity becoming worse, and demand increasing, the urgency for the water industry to hit net-zero carbon is accelerating. Even as a multitude of utilities have pledged to reach net-zero by 2050, advancing beyond the energy–water nexus remains a heavy lift. This paper, using a systematic literature review that complies with Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA), aims to propose sustainable water resource management (SWRM) strategies that may assist water utilities in decarbonizing their value chains and achieving net-zero carbon. In total, 31 articles were included from SCOPUS, ResearchGate, ScienceDirect, and Springer. The findings show that water utilities are responsible for 3% of global greenhouse gas emissions and could reduce these emissions by more than 45% by employing a few strategies, including the electrification of transport fleets, the use of renewables, advanced oxidation processes (AOPs) and energy-efficient technologies. A broad-based case study from Scottish Water shows a 254,000-ton CO₂ reduction in the period since 2007, indicative of the potential of these measures. The review concludes that net-zero carbon is feasible through a mix of decarbonization, wastewater reuse, smart systems and policy-led innovation, especially if customized to both large and small utilities. To facilitate a wider and a more scalable transition, research needs to focus on development of low-cost and flexible strategies for underserved utilities. Full article

This study explores modernization strategies for existing district heating (DH) networks to enhance their efficiency and sustainability, focusing on achieving net-zero emissions in urban heating systems. Building upon a literature review and expert interviews, we developed a strategic decision-support framework that outlines distinct strategies for retrofitting district heating grids and includes a portfolio analysis. This framework serves as a tool to guide DH operators and stakeholders in selecting well-founded modernization pathways by considering technical, economic, and social dimensions. The review identifies several promising measures, such as reducing operational temperatures at substations, implementing optimized substations, integrating renewable and waste heat sources, implementing thermal energy storage (TES), deploying smart metering and monitoring infrastructure, and expanding networks while addressing public concerns. Additionally, the review highlights the importance of stakeholder engagement and policy support in successfully implementing these strategies. The developed strategic decision-support framework helps practitioners select a tailored modernization strategy aligned with the local context. Furthermore, the findings show the necessity of adopting a comprehensive approach that combines technical upgrades with robust stakeholder involvement and supportive policy measures to facilitate the transition to sustainable urban heating solutions. For example, the development of decision-support tools enables stakeholders to systematically evaluate and select grid modernization strategies, directly helping to reduce transmission losses and lower greenhouse gas (GHG) emissions contributing to climate goals and enhancing energy security. Indeed, as shown in the reviewed literature, retrofitting high-temperature district heating networks with low-temperature distribution and integrating renewables can lead to near-complete decarbonization of the supplied heat. Additionally, integrating advanced digital technologies, such as smart grid systems, can enhance grid efficiency and enable a greater share of variable renewable energy thus supporting national decarbonization targets. Further investigation could point to the most determining context factors for best choices to improve the sustainability and efficiency of existing DH systems. Full article

This research explores how artificial intelligence (AI) can be leveraged to optimize packaging design, reduce operational costs, and enhance sustainability in e-commerce. As packaging waste and shipping inefficiencies grow alongside global online retail demand, traditional methods for determining box size, material use, and logistics planning have become economically and environmentally inadequate. Using a three-phase framework, this study integrates data-driven diagnostics, AI modeling, and real-world validation. In the first phase, a systematic analysis of current packaging inefficiencies was conducted through secondary data, benchmarking, and cost modeling. Findings revealed significant waste caused by over-packaging, suboptimal box-sizing, and poor alignment between product characteristics and logistics strategy. In the second phase, a random forest (RF) machine learning model was developed to predict optimal packaging configurations using key product features: weight, volume, and fragility. This model was supported by AI simulation tools that enabled virtual testing of material performance, space efficiency, and damage risk. Results demonstrated measurable improvements in packaging optimization, cost reduction, and emission mitigation. The third phase validated the AI framework using practical case studies—ranging from a college textbook to a fragile kitchen dish set and a high-volume children’s bicycle. The model successfully recommended right-sized packaging for each product, resulting in reduced material usage, improved shipping density, and enhanced protection. Simulated cost-saving scenarios further confirmed that smart packaging and AI-generated configurations can drive efficiency. The research concludes that AI-based packaging systems offer substantial strategic value, including cost savings, environmental benefits, and alignment with regulatory and consumer expectations—providing scalable, data-driven solutions for e-commerce enterprises such as Amazon and others. Full article