Urban canyons, integral components of the built environment, significantly influence microclimatic conditions and thermal comfort. This review investigates their combined effects with green infrastructure on thermal comfort, offering a comprehensive framework for optimizing urban design and greening strategies. Urban canyon orientation determines solar exposure and its interaction with prevailing wind patterns, affecting ventilation and heat dissipation. The urban canyon aspect ratio influences shading and airflow regulation, while their sky view factor moderates radiative cooling and daylight availability. Urban greening—encompassing street trees, green roofs, and vertical green walls—complements urban geometry by reducing air temperatures, enhancing evapotranspiration, and modifying local wind dynamics. Tree shading can reduce the physiological equivalent temperature in urban canyons, mitigating extreme heat stress. Key vegetative parameters, such as leaf area index and canopy density, are critical for quantifying cooling contributions. Key findings underscore the role of higher aspect ratios in enhancing shading and ventilation while they emphasize the critical influence of street orientation and sky view factor on microclimatic regulation. Vegetation emerges as a vital component, with tree shading contributing substantially to cooling effects and reducing physiological equivalent temperature. The beneficial synergistic interaction between urban geometry and vegetation optimizes thermal comfort. Tailored strategies based on urban canyon typologies balance urban development with environmental sustainability. The proposed framework provides actionable strategies for designing resilient and thermally optimized urban spaces, promoting climate-adaptive urban planning by addressing the dual challenges of the urban heat island and thermal discomfort in cities.

Improving the energy efficiency of buildings is a critical pathway in mitigating greenhouse gas emissions and fostering sustainable urban development. This study introduces a simulation-based multi-objective optimization framework designed to enhance both the thermal and economic performance of residential buildings. A representative single-family dwelling located in Patras, Greece, served as a case study to demonstrate the application and scalability of the proposed methodology. The optimization simultaneously minimized two conflicting objectives: the building’s annual thermal energy demand and the cost of construction materials. The computational process was implemented using MATLAB’s Multi-Objective Genetic Algorithm, supported by a modular Excel interface that enables the dynamic customization of design parameters and climatic inputs. A parametric analysis across four optimization scenarios was conducted by systematically varying the key algorithmic hyperparameters—population size, mutation rate, and number of generations—to assess their impact on convergence behavior, Pareto front resolution, and solution diversity. The results confirmed the algorithm’s robustness in producing technically feasible and non-dominated solutions, while also highlighting the sensitivity of optimization outcomes to hyperparameter tuning. The proposed framework is a flexible, reproducible, and computationally tractable approach to supporting early-stage, performance-driven building design under realistic constraints.

Indoor Air Quality (IAQ) in educational environments is a critical determinant of students’ health, well-being, and learning performance, with inadequate ventilation and pollutant accumulation consistently associated with respiratory symptoms, fatigue, and impaired cognitive outcomes. Conventional monitoring approaches—based on periodic inspections or subjective perception—provide only fragmented insights and often underestimate exposure risks. Artificial intelligence (AI) offers a transformative framework to overcome these limitations through sensor calibration, anomaly detection, pollutant forecasting, and the adaptive control of ventilation systems. This review critically synthesizes the state of AI applications for IAQ management in educational environments, drawing on twenty real-world case studies from North America, Europe, Asia, and Oceania. The evidence highlights methodological innovations ranging from decision tree models integrated into large-scale sensor networks in Boston to hybrid deep learning architectures in New Zealand, and regression-based calibration techniques applied in Greece. Collectively, these studies demonstrate that AI can substantially improve predictive accuracy, reduce pollutant exposure, and enable proactive, data-driven ventilation management. At the same time, cross-case comparisons reveal systemic challenges—including sensor reliability and calibration drift, high installation and maintenance costs, limited interoperability with legacy building management systems, and enduring concerns over privacy and trust. Addressing these barriers will be essential for moving beyond localized pilots. The review concludes that AI holds transformative potential to shift school IAQ management from reactive practices toward continuous, adaptive, and health-oriented strategies. Realizing this potential will require transparent, equitable, and cost-effective deployment, positioning AI not only as a technological solution but also as a public health and educational priority.

As the environmental crisis intensifies, the demand for energy-efficient systems has never been greater. Vortex generators have emerged as an effective method for enhancing heat transfer within tubes. While extensive research has been conducted on their application in straight tubes, studies focusing on their performance in curved tubes remain limited. This simulation study examined three different arrangements of triangular vortex generators in a common flow-down configuration within a U-turn tube to optimize heat transfer. The analysis conducted under constant wall temperature conditions across a range of Reynolds numbers spans both laminar and turbulent flow regimes to evaluate the broader impacts of vortex generators on flow and thermal fields. The efficiency of each arrangement was evaluated based on the Nusselt number and friction factor. Results show a remarkable increase in the Nusselt number, reaching up to 115% for the configuration with the highest number of vortex generators. However, this enhancement was accompanied by a significant increase in the friction factor, rising by up to 383% at higher Reynolds numbers. Overall, vortex generators demonstrated their highest effectiveness in curved tubes during the laminar-to-turbulent flow transition. In fully turbulent flow, the friction factor increased disproportionately to the modest gains in heat transfer. Despite these limitations, the use of vortex generators in curved tubes offers promising efficiency improvements and merits further exploration.

Buildings are responsible for nearly 40% of primary energy consumption. Over the recent decades, numerous methods have been proposed to model, predict, and optimize the heating and cooling energy consumption in buildings, prioritizing efficiency, accuracy, simplicity, and speed. From basic deterministic formulations to advanced machine learning techniques, various methods have been proposed to improve the thermal performance of existing structures and optimize the design of new ones. This manuscript reviews statistical and machine learning approaches in building energy performance simulation, presenting and discussing theoretical considerations and a review of published research studies, covering input, output, distinctive modeling features, and main results. Statistical learning techniques include linear prediction models, generalized linear models, linear mixed-effects models, Bayesian approaches, and time series analysis. Machine learning techniques include deep learning approaches, such as deep feed-forward, recurrent, and convolutional artificial neural networks. Support-vector machines and ensemble machine learning are also discussed, each with a review of relevant research studies, respectively. The application of machine learning approaches in building design and control include both model predictive and reinforcement learning-based control, and building retrofit. The goal is to provide a detailed overview of historical and contemporary developments in data-driven methodologies, encompassing various scientific approaches and algorithms shedding light on the complexities and trends in the dynamic field of energy-efficient building design and operation.

Buildings constitute approximately 40% of global energy consumption and contribute to around one-third of total greenhouse gas emissions. Buried, earth-sheltered, and slope buildings leverage the ground’s thermal mass to conserve heating and cooling energy. This review examines the modelling of thermal performance in earth-sheltered buildings, focusing on the temperature profile of the ground and summarizing published mathematical formulations and solutions for modeling the ground temperature distribution. It encompasses considerations of both earth surface temperature measurements and the energy balance at the earth’s surface. Representative ground surface boundary conditions and their parameters, including thermal conductivity, heat transfer, radiation intensity, temperature, and humidity metrics, are summarized. Efforts to model the thermal behavior and performance of earth-sheltered buildings, as well as the soil temperature at various depths, using statistical and machine learning methods, including artificial neural networks, are also documented. The review further explores energy conservation in earth-sheltered buildings, summarizing the location of studies, methodology, construction and description of buildings, and achieved energy efficiency, confirming the significant role of the ground as a thermal mass and the consequent reduction in energy requirements. Subsequently, the daylighting of earth-sheltered buildings is examined, focusing on the role of windows, the impact of daylighting on the circadian rhythm, and architectural solutions such as skylights and courtyards. A review of noise aspects considers shielding from airborne and ground-borne exterior noise sources, transmission losses, as well as ground and structure-borne noise and vibrations. Indoor acoustic environments, with measures of acoustic quality like reverberation time and speech transmission, are also discussed. Sound absorption is examined in various indoor acoustic environments, including caves, rock-cut structures, and underground buildings. Finally, health and safety issues of earth-sheltered buildings refer to ventilation, lighting, and structural safety. The review concludes with a synthesis of findings.

This study conducts a literature review coupled with case-study calculations comparing the thermal contribution of semi-intensive and intensive (deeper) green roofs to non-insulated and insulated building roofs, and enhancing comprehension by validating applied scenarios with published literature-based data. Mitigation of the urban heat island is crucial for reducing energy consumption and enhancing urban sustainability, especially through natural solutions such as green (i.e., planted) roofs. The energy and environmental benefits of green roofs include energy conservation, thermal comfort, noise reduction, and aesthetic improvement. Legal mandates, innovative business models, financial subsidies and incentives, regulations, etc. are all components of green roof policies. Conflicts between private property owners and the public, regulatory gaps, and high installation costs are among the challenges. Green roofs are layered and incorporate interacting thermal processes. Green roof models are either based on the calculation of thermal transmittance (U-values), an experimental energy balance, or data-driven (primarily neural network) approaches. U-values were calculated for eight hypothetical scenarios consisting of four non-insulated and four insulated roofs, with or without semi-intensive and intensive green roofs of various materials and layer thicknesses. While the non-planted, non-insulated roof had the highest U-value, planted roofs were particularly effective for non-insulated roofs. Three of these scenarios were in reasonable accord with experimental and theoretical thermal transmittance literature values. Finally, a non-insulated planted roof, particularly one with rockwool, was found to provide a certain degree of thermal insulation in comparison to a non-planted insulated roof.

Green roofs are artificial ecosystems that provide a nature-based solution to environmental challenges such as climate change and the urban heat island. Green roofs aid in the conservation of both cooling and heating energy; deposition of particulates and mitigation of air pollution; control of runoff and water pollution; promotion of biodiversity; and provision of aesthetic and health benefits. This research is a holistic review of the green roof literature and provides a global perspective of the subject with a classification of modelling studies; and an extensive review of contributions to energy conservation, carbon sequestration, mitigation of air pollutants, runoff control; and urban noise reduction. The review covers the system's thermal performance modelling through several methodologies; experimental studies; parametric studies to assess the impact of various pa-rameters on the system's energy efficiency using several configuration parameters such as leaf area, foliage height and density, plant coverage, roof insulation, soil thickness, and irrigation; energy benefits; and envi-ronmental benefits including air pollutants mitigation, carbon sequestration, runoff control and urban noise reduction. Finally, review was complemented with a life cycle assessment study of green roofs, which examined the extraction of raw materials, manufacturing and construction, transportation, and disposal.Green roofs can reduce the cooling load by up to 70%, decrease the indoor temperature achieving an indoor air temperature reduction up to 15 degrees C, and provide a significant improvement of thermal comfort conditions. The environmental benefits of green roofs were focused on decreasing pollutants concentrations (e.g. PM2.5, PM10, O3, NO2), sequestering carbon and reducing urban noise.

This chapter examines the socioeconomic, environmental, ethical, policy-related, and geopolitical aspects of biofuels, comparing conventional (first- and second-generation) biofuels to the third (and fourth) generation of biofuels that are produced by algae. This chapter commences with a review of the basic characteristics of different biofuels, including definitions, production, economics, and challenges. Socioeconomic aspects include land; employment and income; costs and prices; gender and culture; and public acceptance. Issues that algal biofuels largely avoid (or affect differently) include competition for agricultural land; debate between food and fuel; rising food (and commodity) prices; pressures for deforestation; and reduction of biodiversity. Public attitudes are largely governed by emulation of social norms, but the public is not acquainted with algal biofuels well enough to be engaged in such behaviors and research on algal biofuels is lacking. Environmental aspects are related to the atmosphere; greenhouse gas emissions; land and soil; deforestation; ecosystems and biodiversity; water quantity and quality; wastes and recycling; energy; health and safety; aesthetics; and sustainability. Conventional biofuels affect water quality and quantity and have a debatable contribution toward fighting climate change, while algal biofuels can reuse wastewater, but may be associated with safety issues. As regards the strenuous relationship of the Global North to the Global South, conventional biofuels have been associated with environmental injustice and affect socioeconomic, ecological, food, transportation, energy, and even national security. Policies in the European Union, the US, China, India, and Latin America are presented and discussed. This chapter is rounded up with a summary and conclusions.

The building sector is responsible for 40% of primary energy consumption, with heating/cooling covering the most significant portion. Thus, passive heating/cooling applications have gained significant ground during the last three decades, with many research activities on the subject. Among passive cooling/heating applications, ground cooling (especially earth-to-air heat exchangers) has been highlighted as a remarkably attractive technological research subjects because of its significant contribution to the reduction of heating/cooling energy loads; the improvement of indoor thermal comfort conditions; and the amelioration of the urban environment. This paper presents a holistic review of state-of-the-art research, methodologies, and technologies of earth-to-air heat exchangers that help achieve energy conservation and thermal comfort in the built environment. The review covers the critical subject of the thermal performance of earth-to-air heat exchanger systems; experimental studies and applications; parametric studies for investigating the impact of their main characteristics on thermal efficiency; and recent advances and trends including hybrid technologies and systems. The models describing the thermal performance of earth-to-air heat exchangers systems were classified in numerical, analytical, and data-driven; their main theoretical principles were presented; and experimental validation was mentioned when carried out. System parameters were grouped into three categories: system design, soil types, and soil surface coverage. System design parameters, especially length and burial depth, bore the most important influence on the thermal efficiency of the system. The paper was rounded up with an economic assessment of system application, and the conclusions highlighted the need for more experimental work including laboratory simulators.

With growing urban populations, methods of reducing the urban heat island effect have become increasingly important. Cool pavements altering the heat storage of materials used in pavements can lead to lower surface temperatures and reduce the thermal radiation emitted to the atmosphere. Cool pavement technologies utilize various strategies to reduce the temperature of new and existing pavements, including increased albedo, evaporative cooling, and reduced heat conduction. This process of negative radiation forces helps offset the impacts of increasing atmospheric temperatures. This paper presents an extensive analysis of the state of the art of cool pavements. The properties and principles of cool pavements are reviewed, including reflectivity, thermal emittance, heat transfer, thermal capacity, and permeability. The different types, research directions, and applications of reflective pavements are outlined and discussed. Maintenance and restoration technologies of cool pavements are reviewed, including permeable pavements. Results show that cool pavements have significant temperature reduction potential in the urban environment. This research is important for policy actions of the European Union, noting that European and international business stakeholders have recently expressed their interest in new ways of reducing energy consumption through technologically advanced pavements.

This chapter reviews the research literature that has aimed to formulate and estimate energy security quantitatively. Energy security is defined as an entity containing dimensions and components represented by metrics. Adopting a geopolitical perspective that is lacking, selected quantitative approaches used for the computation of energy security indexes are reviewed. Research into the role of energy markets is reviewed. Many simple (disaggregate) indicators, composite (aggregate) indicators, and complex indexes of energy security are mentioned. Complementary qualitative techniques such as interviews are discussed. Reviewed works were found to analyze energy security metrics using: numerical (scoring/ranking, weighting, organizing into a matrix); statistical (z-score approaches, correlation analysis, consumer surveys); multivariate statistical (multiple regression, Principal Component Analysis, Factor Analysis, Cluster Analysis on static of time-series data); econometric (time series approaches); multi-criteria decision making (Analytic Hierarchy Process, Fuzzy Analytical Hierarchical Process, Preference Ranking Organization Method for Enrichment Evaluations); (accident) risk assessment; complexity (time series analysis coupled with path dependency and lock-in concepts, Agent-Based Models); risk assessment (covering energy, social, institutional and political factors); game theoretic; and qualitative (e.g., interviews, expert panels) methods and techniques. Public perceptions were occasionally taken into consideration. Regarding the scope of the reviewed research, several works examine case studies of a single or a few countries; others analyze more countries, usually located in a region (such as Europe or Asia); some studies concentrate on regions or provinces. The chapter is concluded with a roundup of main points.

Urbanization and climate change are two major issues that humanity faces in the 21st century. Megacities are large urban agglomerations with more than 10 million inhabitants that emerged in the 20th century. The world’s top 100 economies include many North and South American megacities, such as New York, Los Angeles, Mexico City, Sao Paulo and Buenos Aires; European cities such as London and Paris; and Asian cities such as Tokyo, Osaka, Seoul, Beijing and Mumbai. This paper addresses a dearth of megacity energy metabolism models in the literature. Cross-sectional data for 36 global megacities were collected from many literature and Internet sources. Variables included megacity name, country and region; population; area; population density; (per capita) GDP; income inequality measures; (per capita) energy consumption; household electricity prices; (per capita) carbon and ecological footprint; degree days; average urban heat island intensity; and temperature and precipitation. A descriptive comparison of the characteristics of megacities was followed by ordinary least squares with heteroskedasticity-robust standard errors that were used to estimate four alternative multiple regression models. The per-capita carbon footprint of megacities was positively associated with the megacity GDP per capita, and the megacity ecological footprint; and negatively associated with country income inequality, a low-income country dummy, the country household electricity price, and the megacity annual precipitation. Targeted policies are needed, but more policy autonomy should be left to megacities. Collecting longitudinal data for megacities is very challenging but should be a next step to overcome misspecification and bias issues that plague cross-sectional approaches.

This chapter examines the role of renewable energy in shaping energy security against the backdrop of global geopolitical, socioeconomic, and technological uncertainties. The evolving definition of energy security during the twentieth and early twenty-first centuries is discussed initially. The dimensions, components, and metrics of energy security are reviewed, including the 4A definition of energy security that comprises physical availability; economic affordability; accessibility from a sociopolitical standpoint; and environmental acceptability. A novel energy security index is proposed, with the following components: physical availability; technology development; economic affordability; social accessibility; governance; unconventional threats; and natural environment. Of these, physical availability followed by technology development, economic affordability, and governance was rated as the most important, and the environment was rated as the least important by a small panel of experts. The roles of wind and solar energy are highlighted, with an emphasis on the social acceptance of renewable energy in an energy security context. Other energy security indexes are discussed, focusing on sustainability and renewable energy. Denmark, Germany, China, Russia, and the United States are examined as case studies that help understand the transition to renewable energy in the context of coopetition among states. As these countries face different political concerns, geopolitical realities, and energy security issues, they consider different policy approaches to address them.

Army personnel perceives character and personality traits as playing an important role in promoting high ranking officers to leaders. Whether the different categories of army personnel perceive the relative importance of such traits similarly is a question of significance for the morale, cohesiveness and efficiency of the armed forces. This paper addresses the documented scarcity of empirical research on such perceptions. The research literature on leadership traits was reviewed with emphasis on the armed and security forces. An online questionnaire was designed and 2702 responses of Greek armed and security forces personnel were collected, including 947 officers, 534 non-commissioned officers, 531 veterans, 81 security forces personnel, and 609 civil personnel. An 86.3% of respondents felt that leadership promotions failed to consider appropriate character and personality traits such as crisis management, integrity, perception, meritocracy, strategic proficiency, bluntness, and impartiality. Cooperation with the political leadership was considered unduly important. Cluster Analysis confirmed by Discriminant Analysis rendered four clusters: politically non-aligned; religious; authority-oriented conformist; and dissatisfied nonconformist. To maintain the morale and cohesiveness of military personnel, it was advised that the promotion to leaders of the armed and security forces be enriched with distilled elements of wisdom from the perceptions of all personnel clusters. Keywords: Leadership, armed forces, personality traits, Cluster Analysis DOI: 10.7176/JRDM/63-04 Publication date: March 31 st 2020

Size, technological scale and monetary investment have kept energy connected to geopolitical contention. With the shale revolution putting peak oil concerns aside and the global rearrangement of energy consumer and producer roles, the emergence of China and India have focused attention on energy security. This paper reviews tens of dimensions, components and metrics that may be used to define energy security, and the methods that may be used to calculate an energy security index. The 4As (availability, affordability, accessibility and acceptability), the five Ss (surety, survivability, supply, sufficiency and sustainability) and other similar approaches are discussed with corresponding metrics. A concise and inclusive novel energy security index is synthesized, with seven no overlapping dimensions: physical availability, technology development, economic affordability, social accessibility, governance, unconventional threats, and natural environment. Since the dimensions of an energy security index are not perceived as having equal importance by different economic actors or the research literature, a small group of energy, economics, technology, geopolitics and environment experts was asked to rate their importance of the seven dimensions of the proposed index. Physical availability was rated as the most important, and natural environment as the least important dimension. In forthcoming research, this energy security index will be combined with the appropriate qualitative methods to reframe geopolitical problems. JEL Classification: Q34, Q35

Energy is an important geopolitical driver, and energy security is an emerging field with growing interest in its measurement. This Chapter is a guide to energy security research that aims to estimate a quantitative energy security index with a geopolitical focus, by providing an in-depth dynamic geopolitical look into the history, evolution, dimensions, data, estimation, taxonomy, and forecasts of energy security. Discussion is complemented with examples from the area of the Southeastern Mediterranean and the Middle East. The Chapter commences with documenting the state of the art of the research literature on energy security. In particular, the definitions, the dimensions, the associated geopolitical issues, the policies, and the quantitative simple indicators and complex indexes of energy security are examined. An alternative to a full panel data approach for research that aims to calculate a quantitative energy security index would be to focus on specific milestone time periods such as the first oil crisis of the 1970s, the first Gulf War of 1990–91, the Russian-Ukrainian disputes (2005–09), and the present time. The Chapter discusses how empirical geographical, energy, socioeconomic, environmental and geopolitical data could be collected, and a novel geopolitical energy security index could be developed with reference to appropriate statistical techniques. A Cluster Analysis of the data and security index values of each milestone time period could provide a dynamic taxonomy of countries, based on their energy security profile, which could be depicted on geopolitical maps. Of particular interest would be to see how countries shifted from one cluster to another over time. Following Cluster Analysis, it is suggested that a case study analysis of the energy security profile of key countries (such as Germany, Russia, Iran, Iraq, Saudi Arabia, the United States, Canada, Venezuela, China, India, and Japan) could complement the quantitative approach, and provide an in-depth appreciation of countries, the energy security issues they face, the policies they adopt to address them, and how well the obtained clustering and energy security values capture this knowledge. It is also proposed that any energy security index research include a small number of interviews with energy experts from the government, the academia, and the professional arena. It is advised that these interviews include open-ended questions on the concept of energy security, its evolution through the milestone time periods, the energy security index, the clusters, the geopolitical maps, and energy security policies. Finally, it is proposed that any energy security index research be complemented with forecasts that take into account socioeconomic and geopolitical data, the energy security index values, the dynamic taxonomy, as well as information gleaned from the interviews.

Energy is an economic, ill-distributed and expensive good, subject to price fluctuations. Energy security arose as a problem of the oil crises of the 1970s, and it has become a matter of national security, although its definition depends on geographical location, natural resource endowment, international relations, political system, economic disposition, and ideological perceptions. Although there is no universally accepted definition of energy security, a succinct way to approach it is through the four As: availability, affordability, accessibility (to all), and acceptability (from a sustainability standpoint). Energy security may be measured by numerous indicators, with no single accepted methodology being ideal for all historical and geopolitical circumstances. Dimensions of the concept of energy security include: the environment, technology, demand side management, sociocultural and political factors, human security, geopolitical considerations, and the formulation of energy security policy. Alternatively, energy security may be considered to have five dimensions that may be broken down into 20 components, as follows: (a) availability, i.e. security of supply and production, dependency, and diversification; (b) affordability, i.e. price stability, access and equity, decentralization, and low prices; (c) technology development, i.e. innovation and research, safety and reliability, resilience, energy efficiency, and investment; (d) sustainability (environmental component), i.e. land use, water, climate change, and air pollution; and (e) regulation, i.e. governance, trade, competition, and knowledge of sound regulation. Energy security contains multiple components, including: geography, nuclear energy, economy, society, environment policy and political institutions. There are many energy security indicators and indexes that include different dimensions and attributes, such as the Herfindahl-Hirschmann Index, the Oil Vulnerability Index, the Vulnerability Index, the Aggregated Energy Security Performance Indicator, the US Energy Security Risk Index, the Energy Architecture Performance Index, and the Energy Trilemma (or Sustainability) Index. The paper is rounded up with a short discussion of the geopolitical role of energy security.