The degradation of aquatic ecosystems is one of the most pressing environmental challenges of the 21st century. Pollution, biodiversity loss, and ineffective governance all threaten the sustainability of these vital ecosystems. This study adopts a holistic approach to the study of aquatic ecosystem protection. This approach integrates citizen engagement, technological innovations, governance frameworks, and sustainable practices within the theoretical context of knowledge dynamics. The study extends Bratianu’s knowledge dynamics framework by introducing the concept of holistic evolutionary knowledge. The study thus examines the interplay between rational, emotional, and spiritual dimensions of knowledge in driving ecological stewardship. Data were collected from respondents from Romania and Greece. These two countries were chosen for their distinct ecological and socioeconomic contexts to provide a comparative perspective on citizen engagement and sustainable practices. A structured questionnaire was distributed to gather data from 374 participants. Analysis of these data was conducted using three methods. The XGBoost machine learning algorithm enabled predictive modeling. Kruskal–Wallis tests were used to assess statistically significant differences between countries. Finally, correlation analysis was performed to identify linear relationships between variables. The findings reveal citizen engagement and emotional knowledge as the strongest predictors of sustainable behavior, with multidimensional knowledge positively linked to ecological practices, thereby advancing SDG 6 and informing citizen science, policy, and environmental action.

Accurate estimation of tree biomass and volume is essential for sustainable forest management, climate change mitigation, and ecosystem service assessment. Recent advances in unmanned aerial vehicle (UAV) technology enable the acquisition of ultra-high-resolution optical and three-dimensional data, providing a resource-efficient alternative to traditional field-based inventories. This review synthesizes 181 peer-reviewed studies on UAV-based estimation of tree biomass and volume across forestry, agricultural, and urban ecosystems, integrating bibliometric analysis with qualitative literature review. The results reveal a clear methodological shift from early structure-from-motion photogrammetry toward integrated frameworks combining three-dimensional canopy metrics, multispectral or LiDAR data, and machine learning or deep learning models. Across applications, tree height, crown geometry, and canopy volume consistently emerge as the most robust predictors of biomass and volume, enabling accurate individual-tree and plot-level estimates while substantially reducing field effort and ecological disturbance. UAV-based approaches demonstrate particularly strong performance in orchards, plantation forests, and urban environments, and increasing applicability in complex systems such as mangroves and mixed forests. Despite significant progress, key challenges remain, including limited methodological standardization, insufficient uncertainty quantification, scaling constraints beyond local extents, and the underrepresentation of biodiversity-rich and structurally complex ecosystems. Addressing these gaps is critical for the operational integration of UAV-derived biomass and volume estimates into sustainable land management, carbon accounting, and climate-resilient monitoring frameworks.

The increasing complexity and importance of medical data in improving patient care, advancing research, and optimizing healthcare systems led to the proposal of this study, which presents a novel methodology by evaluating the sensitivity of artificial intelligence (AI) algorithms when provided with real data, synthetic data, a mix of both, and synthetic features. Two medical datasets, the Pima Indians Diabetes Database (PIDD) and the Breast Cancer Wisconsin Dataset (BCWD), were used, employing the Gaussian Copula Synthesizer (GCS) and the Synthetic Minority Oversampling Technique (SMOTE) to generate synthetic data. We classified the new datasets using fourteen machine learning (ML) models incorporated into PyCaret AutoML (Automated Machine Learning) and two deep neural networks, evaluating performance using accuracy (ACC), F1-score, Area Under the Curve (AUC), Matthews Correlation Coefficient (MCC), and Kappa metrics. Local Interpretable Model-agnostic Explanations (LIME) provided the explanation and justification for classification results. The quality and content of the medical data are very important; thus, when the classification of original data is unsatisfactory, a good recommendation is to create synthetic data with the SMOTE technique, where an accuracy of 0.924 is obtained, and supply the AI algorithms with a combination of original and synthetic data.

Heavy metal contamination of aquatic systems represents a critical environmental and public health concern due to the persistence, toxicity, and bioaccumulative potential of these elements. Geographic information systems (GISs) have emerged as indispensable tools for the spatial assessment and management of heavy metals (HMs) in water resources. This review systematically synthesizes current research on GIS applications in detecting, monitoring, and modeling heavy metal pollution in surface and groundwater. A bibliometric analysis highlights five principal research directions: (i) global research trends on GISs and heavy metals in water, (ii) occurrence of HMs in relation to World Health Organization (WHO) permissible limits, (iii) GIS-based modeling frameworks for contamination assessment, (iv) identification of pollution sources, and (v) health risk evaluations through geospatial analyses. Case studies demonstrate the adaptability of GISs across multiple spatial scales, ranging from localized aquifers and river basins to regional hydrological systems, with frequent integration of advanced statistical techniques, remote sensing data, and machine learning approaches. Evidence indicates that concentrations of some HMs often surpass WHO thresholds, posing substantial risks to human health and aquatic ecosystems. Furthermore, GIS-supported analyses increasingly function as decision support systems, providing actionable insights for policymakers, environmental managers, and public health authorities. The synthesis presented herein confirms that the GIS is evolving beyond a descriptive mapping tool into a predictive, integrative framework for environmental governance. Future research directions should focus on coupling GISs with real-time monitoring networks, artificial intelligence, and transdisciplinary collaborations to enhance the precision, accessibility, and policy relevance of heavy metal risk assessments in water resources.

Principal component analysis (PCA) is a widely applied multivariate statistical technique across scientific disciplines, with forestry being one of its most dynamic areas of use. Its primary strength lies in reducing data dimensionality and classifying parameters within complex ecological datasets. This study provides the first comprehensive bibliometric and literature review focused exclusively on PCA applications in forestry. A total of 96 articles published between 1993 and 2024 were analyzed using the Web of Science database and visualized using VOSviewer software, version 1.6.20. The bibliometric analysis revealed that the most active scientific fields were environmental sciences, forestry, and engineering, and the most frequently published journals were Forests and Sustainability. Contributions came from 198 authors across 44 countries, with China, Spain, and Brazil identified as leading contributors. PCA has been employed in a wide range of forestry applications, including species classification, biomass modeling, environmental impact assessment, and forest structure analysis. It is increasingly used to support decision-making in forest management, biodiversity conservation, and habitat evaluation. In recent years, emerging research has demonstrated innovative integrations of PCA with advanced technologies such as hyperspectral imaging, LiDAR, unmanned aerial vehicles (UAVs), and remote sensing platforms. These integrations have led to substantial improvements in forest fire detection, disease monitoring, and species discrimination. Furthermore, PCA has been combined with other analytical methods and machine learning models—including Lasso regression, support vector machines, and deep learning algorithms—resulting in enhanced data classification, feature extraction, and ecological modeling accuracy. These hybrid approaches underscore PCA’s adaptability and relevance in addressing contemporary challenges in forestry research. By systematically mapping the evolution, distribution, and methodological innovations associated with PCA, this study fills a critical gap in the literature. It offers a foundational reference for researchers and practitioners, highlighting both current trends and future directions for leveraging PCA in forest science and environmental monitoring.

The present review summarizes the existing knowledge regarding the afforestation of sand dunes. Our main focus was on the role of trees in stabilizing and rehabilitating these complex ecosystems. We analyzed 937 publications through a systematic bibliometric review and then proceeded to select 422 articles that met our criteria. This methodological approach—combining a comprehensive bibliometric analysis with an in-depth traditional literature review—represents a novel contribution to the field and allows for both quantitative trends and qualitative insights to be captured. This was then complemented by an in-depth literature review. Our results sustain the global importance of this subject, as they include studies from more than 80 countries, with a focus on the USA, China, Australia, and Japan. We have also identified a series of main tree species that are usually used in the afforestation of sand dunes (Pinus, Acacia, Juniperus) and then proceeded to analyze their ecologic and socio-economic impact. As such, we have analyzed case studies from all continents, showcasing a variety of strategies that were successful and adapted to local conditions. This did not exclude challenges, mainly invasive species, low survival rates, and effects on biodiversity and stabilization. The main factors that impact the success of afforestation are represented by topography, soil structure, water dynamics, and climate. Unlike previous reviews, this study offers a global synthesis of both the scientific output and the applied outcomes of sand dune afforestation, bridging the gap between research and practice. As such, afforestation has a positive impact on soil fertility and carbon sequestration but can also present a major risk to native ecosystems. In this context, the present review highlights the need to adopt strategies that are unique for that site, and that must integrate all aspects (ecological, social, economic) to ensure good results. Our ISI-indexed literature review helped us to address the link between the current knowledge, research trends, and future topics that must be addressed.

This study explores the application of machine learning (ML) techniques on the Sloan Digital Sky Survey (SDSS) Data Release 17 dataset, for the classification, regression and clustering of celestial objects, including stars, galaxies and quasars, using spectral features, redshift and position coordinates. Using Python and libraries such as scikit-learn, LightGBM and XGBoost, the research applies the classification algorithm (Logistic Regression, k-Nearest Neighbors, Random Forest), regression (Multiple Linear Regression, Polynomial Regression, LightGBM) and clustering (K-means, DBSCAN) on this data. The results indicate a maximum accuracy of 0.9796 in the classification with Random Forest, obtained by integrating all features, and a coefficient of determination of 0.4328 in the regression with k-NN, highlighting the combined contribution of the data. In clustering, K-means achieved a silhouette score of 0.6692 per redshift, reflecting the ability of distance-based separation. The study highlights the potential of SDSS17 as an "open science" resource for the practical application of artificial intelligence in astronomy. The conclusions confirm the effectiveness of nonlinear methods in cosmic data analysis and the essential role of open access to data in advancing research.

This paper presents the experience and lessons learned in a pilot project aimed at integrating artificial intelligence (AI) technologies in sturgeon aquaculture. The project used convolutional neural networks and visual intelligence for the evaluation of fish biomass and the optimisation of sturgeon rearing technologies in integrated multitrophic production systems. Similar solutions have been used before to determine the biomass of other fish species, but this is the first documentation of the application of such a solution for sturgeons. The application challenges were significant, which was determined by the special morphological peculiarities of the sturgeons (shape, way of swimming, their dimensions). Both YOLACT technology and a computer vision context were tested using LAB and HSV colour spaces to estimate fish biomass based on imaging data. It was found that the LAB colour space provided superior results in terms of precision and efficiency, but maximum accuracy was achieved using convolutional neural networks (YOLACT). The analysis of the project results confirms the significant advantages of using the AI system for biomass monitoring, advantages consisting of the reduction of unit costs with labour and feed, improvement of water quality, active optimisation of sturgeon growing conditions. In this way, conditions are created for the sustainable growth of sturgeon production, both for consumption and for the restocking of various aquatic ecosystems with brood. It also proves that the large-scale implementation of AI-based technologies in the fisheries industry can make an important contribution to the achievement of Romania’s National Multiannual Aquaculture Strategic Plan 2022-2030, as well as to the implementation of the European Union’s strategies on food security and biodiversity

In the realm of contemporary education, the integration of advanced technology is not just a trend but a necessity. ”EDSense” emerges as a cutting-edge platform at the intersection of educational needs and technological innovation. This paper focuses on the development of a social educational platform, leveraging artificial intelligence (AI) uniquely designed to support the official educational curriculum, catering to a wide range of users from students and teachers in grades I-XII in Romania, to adult learners. Central to EDSense is the use of AI for personalized learning experiences. This includes the implementation of sophisticated recommending systems that adapt to individual learning styles and progress. Personalized quizzes, crafted using AI algorithms, offer a tailored approach to assess and reinforce learning. Furthermore, EDSense employs sentiment analysis to gauge and respond to the emotional and cognitive states of learners, thereby enhancing the overall learning experience. The platform stands as a testament to the potential of AI in revolutionizing education. By combining educational content with state-of-the-art technology, EDSense aims to provide an engaging, effective, and highly personalized learning journey, setting a new standard in educational technology.

People with Alzheimer's disease are at risk of malnutrition, overeating, and dehydration because short-term memory loss can lead to confusion. They need a caregiver to ensure they adhere to the main meals of the day and are properly hydrated. The purpose of this paper is to present an artificial intelligence system prototype based on deep learning algorithms aiming to help Alzheimer's disease patients regain part of the normal individual comfort and independence. The proposed system uses artificial intelligence to recognize human activity in video, being able to identify the times when the monitored person is feeding or hydrating, reminding them using audio messages that they forgot to eat or drink or that they ate too much. It also allows for the remote supervision and management of the nutrition program by a caregiver. The paper includes the study, search, training, and use of models and algorithms specific to the field of deep learning applied to computer vision to classify images, detect objects in images, and recognize human activity video streams. This research shows that, even using standard computational hardware, neural networks' training provided good predictive capabilities for the models (image classification 96%, object detection 74%, and activity analysis 78%), with the training performed in less than 48 h, while the resulting model deployed on the portable development board offered fast response times-that is, two seconds. Thus, the current study emphasizes the importance of artificial intelligence used in helping both people with Alzheimer's disease and their caregivers, filling an empty slot in the smart assistance software domain.

People with Alzheimer's disease are at risk for malnutrition, overeating and dehydration because short-term memory loss can be confusing. They need a caregiver to make sure they adhere to the main meals of the day and are properly hydrated. The purpose of this paper is to present the need for a developed surveillance system that has the role of regaining their independence through artificial intelligence. The system is based on a unique concept that involves the use of artificial intelligence to recognize human activity in video. The system identifies the times when the monitored person is feeding or hydrating and reminds them by sound messages that they forgot to eat, drink or eat too much. It also allows for the remote supervision and management of the nutrition program by a caregiver. The paper includes the study, search, training and use of models and algorithms specific to the field of computer vision in order to classify images, detect objects in images and recognize human activity in video. The study shows that artificial intelligence used to help people with Alzheimer's is a new concept, but a real help for both them and their caregivers.

The development of artificial intelligence (AI) technologies is proceeding fast across many fields. Based on a deep learning approach, we propose a prototype of an on-site customer profiling and hyper-personalization system (OSCPHPS) targeted at marketing professionals. We propose an AI platform to create customer profiles during their physical presence in stores. The idea of the OSCPHPS prototype is to automatically detect and gather customer data directly from the store, essentially completing customer profiles containing gender, age, personality, emotions, and products they interacted with or bought, irrespective of where they are in the store. Each buying operation could generate an anonymous customer profile. Therefore, for every product sold, the system will track multiple customer-generated profiles of the people who bought that product. These kinds of data offer endless further possibilities for the business. Through a configurational study conducted via fsQCA methodology, we assessed the interest in the OSCPHPS prototype on the part of marketing managers of clothes & fashion stores located in different European countries. Based on these live generated profiles, we could further enhance the OSCPHPS system by adding support for customer segmentation, strategic product campaigns, live product recommendation, analysis of emotions toward a product or a category of products, sales forecasts, and personalized store space enhancements based on augmented reality, customer exploratory statistics and customer purchasing patterns.

The paper addresses the detection of floating and underwater marine mines from images recorded from cameras (taken from drones, submarines, ships, boats). Due to the lack of image datasets, images were taken from the Internet and by using the technique of augmentation and synthetic image generation (by overlapping images with different types of mines over water backgrounds) 2 data sets were built (one for floating mines and one for underwater mines). The networks were trained and compared using 3 types of Deep Learning models Yolov5, SSD and EfficientDet (Yolov5, SSD for floating mines and Yolov5 and EfficientDet for underwater mines). The networks were also tested in the context of an IoT device (RaspberryPi 4, RPi camera).

This study determines the differences in opinion of U-15 (20 boys) and U-16 (29 boys) rugby players from Romanian national teams, regarding motivational support (MS) and the effects/benefits (EB) of the sport. The evaluation questionnaire (based on 21 items with closed answers and 7 items with free answers) was applied between 29 November 2019 and 13 December 2019. The statistical calculation indicates the absence of significant differences between the groups for most items, with the exception of financial motivation (where the U-16 group has a higher score, p < 0.05) and the usefulness of rugby for the population as a variant of active leisure (where the U-15 group has a higher score). However, U-15 athletes are more motivated by the examples of elite players, have increased involvement in terms of passion in training and competitions, and assign high scores to their relationships with the coach and teammates, while U-16 players are more optimistic about self-perceived skills as the basis of success in rugby. The U-15 team is more confident regarding most of the benefits of a rugby game, and those in the U-16 team have superior values in the context of favorable effects on attitude, as well as a better ability to concentrate at the levels of academics and sports. Masculine characteristics, the uniqueness, and physical contact are the main factors of attraction for rugby. Over 56% of the players practiced or practice other sports and sports games, with contact sports being at the top. A total of 96% of players suffered injuries, with the legs and arms being the most affected, but 25% of the U-15 group also suffered injuries to the head, with the main causes being physical contact with opponents and the superficiality of the warm-up. Workouts associated with physical training are the most difficult to bear, and the U-15 group is more bored with routine and monotony. A higher level of physical training/self-perceived fitness is the main strength of players, followed by technical and tactical knowledge.

In the context of new geopolitical tensions due to the current armed conflicts, safety in terms of navigation has been threatened due to the large number of sea mines placed, in particular, within the sea conflict areas. Additionally, since a large number of mines have recently been reported to have drifted into the territories of the Black Sea countries such as Romania, Bulgaria Georgia and Turkey, which have intense commercial and tourism activities in their coastal areas, the safety of those economic activities is threatened by possible accidents that may occur due to the above-mentioned situation. The use of deep learning in a military operation is widespread, especially for combating drones and other killer robots. Therefore, the present research addresses the detection of floating and underwater sea mines using images recorded from cameras (taken from drones, submarines, ships and boats). Due to the low number of sea mine images, the current research used both an augmentation technique and synthetic image generation (by overlapping images with different types of mines over water backgrounds), and two datasets were built (for floating mines and for underwater mines). Three deep learning models, respectively, YOLOv5, SSD and EfficientDet (YOLOv5 and SSD for floating mines and YOLOv5 and EfficientDet for underwater mines), were trained and compared. In the context of using three algorithm models, YOLO, SSD and EfficientDet, the new generated system revealed high accuracy in object recognition, namely the detection of floating and anchored mines. Moreover, tests carried out on portable computing equipment, such as Raspberry Pi, illustrated the possibility of including such an application for real-time scenarios, with the time of 2 s per frame being improved if devices use high-performance cameras.

The present research uses machine learning, panel data and time series prediction and forecasting techniques to establish a framework between a series of renewable energy and environmental pollution parameters, considering data for BRICS, G7, and EU countries, which can serve as a tool for optimizing the policy strategy in the sustainable energy production sector. The results indicates that XGBoost model for predicting the renewable energy production capacity reveals the highest feature importance among independent variables is associated with the gas consumption parameter in the case of G7, oil consumption for EU block and GHG emissions for BRICS, respectively. Furthermore, the generalized additive model (GAM) predictions for the EU block reveal the scenario of relatively constant renewable energy capacity if gas consumption increases, while oil consumption increases determine an increase in renewable energy capacity until a kick point, followed by a decrease. The GAM models for G7 revealed the scenario of an upward trend of renewable energy production capacity, as gas consumption increases and renewable energy production capacity decreases while oil consumption increases. In the case of the BRICS geopolitical block, the prediction scenario reveals that, in time, an increase in gas consumption generates an increase in renewable energy production capacity. The PCA emphasizes that renewable energy production capacity and GHG, respectively CO2 emissions, are highly correlated and are integrated into the first component, which explains more than 60% of the variance. The resulting models represent a good prediction capacity and reveal specific peculiarities for each analyzed geopolitical block. The prediction models conclude that the EU economic growth scenario is based on fossil fuel energy sources during the first development stage, followed by a shift to renewable energy sources once it reaches a kick point, during the second development stage. The decrease in renewable energy production capacity when oil consumption increases indicates that fossil fuels are in trend within the G7 economy. In the case of BRICS, it is assumed that gas consumption appears because of increasing the industrial capacity, followed by the increase of economic sustainability, respectively. In addition, the generalized additive models emphasize evolution scenarios with different peculiarities, specific for each analyzed geopolitical block.

The inventory and evaluation of growth rates for afforested surfaces is extremely important in estimating production levels and in determining the wood quantities that can be harvested. The present research was realized in southeast Romania, on a surface that contains 375h of afforested fields. The monitored surfaces are situated in Hanu-Conachi Independenta Forest, at a relatively low altitude. The study took into account only the surfaces afforested with willow (Salix alba) and extended between 2010 and 2015. The afforested surfaces’ consistency and age were evaluated based on direct observations and measurements. The used numerical analysis on different optimization methods was selected from amongst the most used series from the specialty literature. Our results have shown that evaluations of estimated production growth rates can vary significantly when different statistical analyses and numeric methods are used. By using numerical optimizing models, computer simulations can offer precise estimations regarding growth rates, and consequently, for the efficiency of a given forest inventory. Common numerical interpolation methods or the usage of neuronal networks do not always lead to consistent results. Specific numeric methods are preferable for a better evaluation of growth rates and current inventory. In addition, investments in computer simulation methods and software should be encouraged in order to reach a permanent inventory, improve the efficiency of exploitation operations, and sustain environmental protection.

The study investigates the influences of gender, area of origin and age stage variables and also of the interaction between them, on the free time behavior of the students at the Faculty of Physical Education and Sports from Galati. The questionnaire applied in the academic year 2019–2020 had 85 items and was structured on 4 factors: leisure budget, leisure limiting factors, preferred leisure activities, and leisure sports activities. The multivariate/MANOVA analysis showed statistically significant data for some of the analyzed items, with values of F associated with thresholds p < 0.05. The results support longer screen time for urban areas and for those <25 years and time limitation for the favorite activities of students >25 years, while reading had higher stress scores for men and students <25 years. Men tended to limit their free time working overtime and women limited their free time due to housework. Students from rural areas and men >25 years were more stressed by socializing on the internet and shopping. Financial limitations for preferred activities were higher for women and students <25 years—women read more and visited their friends more often while men had higher scores in relation to involvement in physical activities throughout the week, an aspect also reported for those <25 years. Students >25 years spent more time with their family, while those <25 years socialized more on the internet and had better scores when going out with friends. Those in urban areas did more jogging, men had better scores in relation to playing sports games, higher indicators for the satisfaction generated by sports activity, and women preferred jogging and cycling/rollerblading. Sports games and different types of fitness were the most common variants practiced at the level of the studied group. Conclusion: There was no dominant orientation of the investigated group towards forms of passive leisure and there were no cases of sedentariness, even if the use of technologies (video games, socializing on the Internet and TV) were forms of leisure often used by students.

The purpose of this study, which contains historical data recorded over a period of 40 years, was to identify the main factors that influence and control the level of wood mass production. The main reason was to optimize the management of forest areas and was driven by the necessity to identify factors that can influence most of the volume produced by coniferous forests located in southeast Europe. The data was collected between1980 and 2005 at the National Institute for Research and Development in Forestry, for forests located in the Southern Carpathians, Romania. The studied data refer to the parameters that model forest structure for spruce, fir, pine, and larch. These are the main resinous species found in the Southern Carpathians. The total area covered by these forests is 143,431 ha. At the forest species level, the analysis consists of 16,162 records (corresponding to the elements of the trees), covering an area of 45,008 ha for fir, 4711 ha for larch, 81,995 ha for spruce, and 11,717 ha for pine. The aim of this research has been to investigate and to assess the impact and magnitude of abiotic factors such as altitude and field aspect on forest structures from the main resinous stands located in the Southern Carpathians. Taking into account the size of the database as well as the duration for collecting data, a complete statistical and systematic approach was considered optimum. This resulted from our wish to emphasize and evaluate the influence of each analysed factor on the wood mass production level. The relationship between abiotic factors and forest structure has been analysed by using a systematic statistical approach in order to provide a useful theoretical reference for the improvement of forest management practices in the context of multiple climatic, environmental, and socio-economic challenges. These common characteristics have been found by applying ANOVA and multivariate statistical methods such as PCA and FA methods. A series of parameters were considered in this investigation, namely altitude (ALT), forest site type (TS), forest type (TP), consistency (CONS) etc. In order to obtain a complete image, we have also applied multivariate analysis methods that emphasize the effect size for each database parameter. At such a level of recorded data, the statistical approach ensures a factor level of p <0.001 while the accuracy in evaluating effect size is increased. As such, they influence the spreading and structure of the studied resinous stands to a higher degree, regardless of species.

The work at hand assesses several driving factors of carbon emissions in terms of urbanization and energy-related parameters on a panel of emerging European economies, between 1990 and 2015. The use of machine learning algorithms and panel data analysis offered the possibility to determine the importance of the input variables by applying three algorithms (Random forest, XGBoost, and AdaBoost) and then by modeling the urbanization and the impact of energy intensity on the carbon emissions. The empirical results confirm the relationship between urbanization and energy intensity on CO2 emissions. The findings emphasize that separate components of energy consumption affect carbon emissions and, therefore, a transition toward renewable sources for energy needs is desirable. The models from the current study confirm previous studies' observations made for other countries and regions. Urbanization, as a process, has an influence on the carbon emissions more than the actual urban regions do, confirming that all the activities carried out as urbanization efforts are more harmful than the resulted urban area. It is proper to say that the urban areas tend to embrace modern, more green technologies but the road to achieve environmentally friendly urban areas is accompanied by less environmentally friendly industries (such as the cement industry) and a high consumption of nonrenewable energy.