Urban Population Research Articles

Physiology evolves convergently but lags behind warming in cities.

Cities, through the generation of urban heat islands, provide a venue for exploring contemporary convergent evolution to climatic warming. We quantified how repeatable the evolution of heat tolerance, cold tolerance, and body size were among diverse lineages in response to urban heat islands. Our study revealed significant shifts towards higher heat tolerance and diminished cold tolerance among urban populations. We further found that the magnitude of trait divergence was significantly and positively associated with the magnitude of the urban heat island, suggesting that temperature played a major role in the observed divergence in thermal tolerance. Despite these trends, the magnitude of trait responses lagged behind environmental warming. Heat tolerance responses exhibited a deficit of 0.84°C for every 1°C increase in warming, suggesting limits on adaptive evolution and consequent adaptational lags. Other moderators were predictive of greater divergence in heat tolerance, including lower baseline tolerance and greater divergence in body size. Although terrestrial species did not exhibit systematic shifts towards larger or smaller body size, aquatic species exhibited significant shifts towards smaller body size in urban habitats. Our study demonstrates how cities can be used to address long-standing questions in evolutionary biology regarding the repeatability of evolution. Importantly, this work also shows how cities can be used as forecasting tools by quantifying adaptational lags and by developing trait-based associations with responses to contemporary warming.

Blueprints for Progress: Unveiling the Pillars of Urban Evolution in Contemporary American Metropolises

This paper presents a comprehensive analysis of urban development dynamics within the United States, using a dataset provided by the International City/County Management Association (ICMA). The study aims to identify and understand the factors that significantly affect urban growth and sustainability. Through rigorous preprocessing and validation processes, including factor analysis and comparative studies, the study examined variables such as infrastructure development, forms of local government, regional influences, and service provisions. The results highlighted the critical role of infrastructure development in urban growth, with a strong correlation between well-developed infrastructure and sustainable urban development. The form of government and regional characteristics also emerged as influential factors, affecting policy formulation and resource allocation. Notably, services provided by local governments were found to be crucial in supporting urban development. This study sheds light on the complexities of urban development dynamics and offers insights into how policy, governance, and infrastructure impact urban growth and sustainability. It underscores the necessity for tailored approaches that consider local contexts and the specific needs of urban populations. The paper concludes with recommendations for future research, emphasizing the need for longitudinal studies, qualitative insights, and policy impact assessments to build upon the findings and support the development of effective urban development strategies.

Representing density: the politics of fear in Zurich city planning

ABSTRACT Density dominates current urban discussions, especially in light of still-developing understandings of Covid-19’s impact on urban populations. This paper argues that critical scrutiny of visual representations of urban density is both missing from academic focus and critically important for understanding how perceptions of density are both created and reproduced. The political rhetoric of density shapes public perceptions as much as it responds to them, and scrutiny of visual density politics adds a new lens to critical density debates. This research takes the speculative imagery of imagined future density created for a public ballot referendum in Zurich, Switzerland as the main focus, and compares the verbal and written campaign rhetoric to the visual images produced as tools of public persuasion. By assessing the gap between what the campaigns claim and what the campaign imagery represents, the research finds embedded in conventional rhetoric a deep-seated and self-defeating assumption that the electorate unilaterally fears density and the changes it might bring. As a winning electoral strategy assuming fear of density worked; as a tool of persuasion it failed, and risks amplifying or creating the perception it assumes.

A qualitative risk assessment model for water reuse: Risks related to agricultural irrigation in Brazil

Urbanization and industrialization are increasing extreme weather events, causing water quantity and quality reduction. Global water scarcity impacts 32.5 % of the urban population and is growing. Brazil has also witnessed water scarcity, notably in the southeast (2014–2015) and south (2019–2020), with reservoirs dropping below 20 % capacity. Water reuse is vital for mitigating scarcity, though it presents risks due to contaminants. Risk analysis studies are crucial for evaluating contamination sources, pathways, and exposure scenarios in water reuse practices. Various methodologies, including quantitative, semi-quantitative, and qualitative analyses, can be employed. Given the uncertainty and diverse factors, qualitative methods are recommended for non-potable water reuse risk analysis. This work presents a qualitative risk analysis methodology that allows to evaluate non-potable water reuse categories. It assesses factors affecting human health and the environment, considering exposure scenarios, characteristics of the receptors, and sources of reused water. The risk analysis of water reuse was carried out focusing on agricultural reuse, considering as alternatives the irrigation of soybean and sugarcane crops. By reviewing literature, the probability of occurrence and the magnitude of impact of the risk factors were identified and rated, using an increasing relative numeric scale. This process resulted in an overall risk value for comparing agricultural irrigation alternatives. The obtained results indicate a promising risk analysis model that can be adjusted and applied to various water reuse modalities and key factors. This adaptable risk analysis model is mainly related to water treatment methods, prompting the proposal of risk control measures.

Complex patterns of morphological diversity across multiple populations of an urban bird species.

Urbanization presents a natural evolutionary experiment because selection pressures in cities can be strongly mismatched with those found in species' historic habitats. However, some species have managed to adapt and even thrive in these novel conditions. When a species persists across multiple cities, a fundamental question arises: do we see similar traits evolve under similar novel environments? By testing if and how similar phenotypes emerge across multiple urban populations, we can begin to assess the predictability of population response to anthropogenic change. Here, we examine variation within and across multiple populations of a songbird, the dark-eyed junco (Junco hyemalis). We measured morphological variations in juncos across urban and non-urban populations in Southern California. We investigated whether the variations we observed are due to differences in environmental conditions across cities. Bill shape differed across urban populations; Los Angeles and Santa Barbara juncos had shorter, deeper bills than non-urban juncos, but San Diego juncos did not. On the other hand, wing length decreased with the built environment, regardless of the population. Southern Californian urban juncos exhibit both similarities and differences in morphological traits. Studying multiple urban populations can help us determine the predictability of phenotypic evolutionary response to novel environments.

Racist dogs and classist rats?

In cities across the world, animals reflect, reproduce and transform urban inequalities – yet their role in mediating social hierarchies remains undertheorized. Urban scholars have begun to highlight the importance of infrastructures and technologies in configuring access to essential goods and services. While this research provides key insights into how non-human entities mediate unequal relations, it has largely overlooked how certain animals – „political animals“ – also co-produce inequalities. This article focuses on two critical urban domains, security and public health, that are often characterized by stark inequalities, and takes the role of key animals within these domains – dogs and rats, respectively – as a new analytical entry-point. Security dogs are socialized to identify threatening individuals on the basis of classed and raced markers. Rats thrive in upscale neighborhoods with historical architecture and abundant green space – yet the public health risks and the stigma associated with these rodents may disproportionately affect low-income residents. Drawing on research on security dogs in Kingston, Jamaica and rats in Amsterdam, this talk discusses the role of animals in the formation of sociospatial boundaries, and the distribution of resources and risks across urban spaces and populations. Focusing on the interactions these two types of „political animals“ have with both humans and infrastructure, it sets out a research agenda for studying how animals’ everyday encounters with their cultural and material environments combine to result in (in-)equitable social outcomes.

Context-aware multi-modal route selection service for urban computing scenarios

The rapid urban population growth introduces novel challenges to urban mobility scenarios, requiring innovative and connected solutions to relieve traffic congestion and enhance transportation efficiency, using unusual roads and public transportation modals. For instance, multi-modal routes allow more flexible modal combinations to offer smart multi-modal mobility. In this context, the route selection service must rely on different context information, such as criminality, accidents, air quality, and others, where Internet of Things technologies introduce active and ubiquitous sensing of many contexts along the urban scenario, providing data for statistical analysis to offer a safer and healthier urban reality. However, designing an efficient multi-modal route selection service that considers different context information to offer personalized routes is important. In this article, we describe a context-aware route selection service that considers adequate contextual information to provide routes according to the user’s preference. The multi-modal route selection service applies a multi-criteria method to give different degrees of importance to each criterion based on the user profile (i.e., Worker, Green, Safe, and Tourist). We present extensive evaluation results from applying our multi-modal route selection approach to a London dataset. The approach successfully enables user-preferred transportation choices using four balanced selection profiles and ten route features. The proposed multi-modal route selection service demonstrates better performance in terms of economic, ecological, and time-saving metrics for each user profile compared to a greedy selection manner.

Long-term, medium-term and acute stress response of urban populations of Eurasian red squirrels affected by different levels of human disturbance.

Animals in urban areas often encounter novel and potentially stressful conditions. It is important to understand how wildlife cope with anthropogenic disturbance. To investigate this specific adaptation we live-trapped squirrels in two study sites in Warsaw: a forest reserve and an urban park and we estimated stress responses at three levels: long-term and medium-term stress (the level of stress hormones, i.e. cortisol and cortisone concentrations, in hair and feces) and acute reaction to human-induced stress (measured during handling with the aid of the three indices: breath rate, struggle rate, and vocalization). According to GLMM models no difference in the stress hormones level was found between the two populations. The only differences in cortisol concentrations clearly depended on the season, i.e. being higher in autumn and winter comparying to other seasons. There was no influence of sex, or reproductive status on stress hormones. Forest squirrels had significantly higher breath rates, suggesting they were more stressed by handling. There was no difference in the struggle rate between study areas, this index was mostly affected by season (i.e. being highest in winter). First-trapped squirrels vocalized less than during the subsequent trappings. Assumingly, during the first, and more stressful trapping, squirrels used 'freezing' and/or little vocalization, while during next captures they used alarm calls to warn conspecifics. Overall, we showed that the two squirrel populations differed only in terms of their breath rate. This suggests that they did not differ in medium-term and long-term stress in general, but they can differ in acute response to handling. This also suggests that both populations were similarly affected by environmental factors. The lack of clear effects may also be due to population heterogeneity. Thus, in order to assess the effects of anthropogenic stressors a broader range of indicators and diverse analytical methods, including behavioral analyses, should be employed.

Impact of human capital and natural resources on environmental quality in South Asia

AbstractThis study examines the importance of human capital and natural resources on environmental quality in South Asia by using panel data that ranges from 1975 to 2021. Advanced cointegration approaches establish the long-term linkages between human capital, natural resources, economic growth, economic growth squared, urban population, industrial value added, and ecological footprint. To explore the long and short-run association among these variables, this study applied the panel autoregressive distributed lags (ARDL) model. The outcome designates that human capital has a negative and significant alliance with environmental quality, whereas economic growth and economic growth square affect environmental quality positively and negatively, confirming the existence of the environmental Kuznets curve (EKC) hypothesis. Urban population growth and natural resources have significant negative associations with environmental pollution, whereas industrial value added increases the pollution levels in South Asia. Lastly, the outcome of the interaction between human capital and natural resource rent on the ecological footprint has a significant positive effect. This study recommends that South Asian nations improve their human capital to promote industrial modernization, expansion of green technology, and energy-efficient habits. Utilizing natural resources also helps South Asian nations maintain a clean ecosystem. Additionally, South Asian nations ought to vigilantly track the negative consequences of industrialization on the atmosphere. Thus, ecological sustainability requires human capital and natural resources.

Socio-Cultural Factors and Components of the Digital Economy in Ethnic Minority Regions

We define the digital economy as all economic activities conducted through the use of information technologies, including activities to create new markets, expand old ones, and produce digital goods and services. The digital economy has become a major driver of sustainable development and the transition towards a greener economy. However, studies show an unequal level of progress among cities, regions, and countries. Consequently, this paper explores the associations between socio-cultural factors (such as attitudes towards marriage/raising a family) and components of the digital economy (such as expenses/investment in ICT and the proportion of personal computers and the Internet in households). This study specifically examines twenty-two ethnic minority regions in Russia with data covering a five-year period. Using the Pearson correlation coefficient, the results show that socio-cultural factors including a large urban population and high divorce rates were positively associated with the digital economy, whereas a large rural population, higher birth rates, and higher natural population growth were negatively correlated with the digital economy.

The contrasting trend of global urbanization-induced impacts on day and night land surface temperature from a time-series perspective

Increasing impervious surface area (ISA) is a major contributor to the rising land surface temperature (LST). Understanding the warming effect of ISA quantitively is crucial to alleviate the urban heat island effect. However, the quantitative analyses of urbanization-induced LST change globally remain to be determined over past decades. Here, we first characterized the response of LST to ISA (i.e., δLST) in 369 global cities using Terra MODIS LST product (1km) and the global artificial impervious area dataset (30m). We then investigated the spatiotemporal changes of δLST and its associated driving factors, including enhanced vegetation index (1km), albedo (500m), population (100m), air temperature (0.1°), and precipitation (0.1°). The analysis was conducted using linear regression models from 2000 to 2010 at the pixel level. The results illustrate that (1) the δLST in urban areas shows a negative trend during daytime (–0.2971°C/% and –0.0870°C/%) and a negative trend at night (0.0938°C/% and 0.0048°C/%) in arid and temperate regions, respectively. While in tropical regions, the δLST remains positive throughout the day, but it exhibits an opposite trend in snow regions. (2) The magnitude of nighttime δLST is stronger compared to daytime globally, which is more significant in arid regions with the largest ΔδLST values (i.e., diurnal variations) in urban (0.3909°C/%) and rural (0.3262°C/%) areas. Besides, the impervious surface has a distinct impact on LST in summer and winter, particularly at night. (3) In temperate regions, δALB (negative) and δAT (positive) are dominant factors in regulating δLST during daytime and nighttime. However, in arid regions, the daytime and nighttime δLSTs are negatively correlated by urban population and vegetation, respectively. These findings provide a quantitative understanding on long-term LST variations and driving factors behind at the pixel level, suggesting that urban greening and increasing surface albedo are effective strategies to mitigate the urbanization-induced surface warming.

Groundwater: Sinking Cities, Urbanisation, Global Drying, Population Growth

Groundwater: Sinking Cities, Urbanisation, Global Drying, Population Growth

Nasal filter reveal exposure risks of inhalable particulates and heavy metals in urban women

Urban populations, especially women, are vunerable to exposure to airborne pollution, particularly inhalable particulates (PM10). Thus, more accurate measurement of PM10 levels and evaluating their health effects is critical for guiding policy to improve human health. Previous studies obtained personal PM10 with time-weighted average by air filter-based sampling (AFS), which ignores individual differences and behavioral patterns. Here, we used nasal filters instead of AFS to obtain actual inhaled PM10 under short-term exposure for urban dwelling women during a severe haze event in Beijing in 2016. The levels of six heavy metals such as As, Cd, Ni, Cr, Pb, and Co in PM10 were investigated, and carcinogenic and non-carcinogenic risks evaluated based on an adjusted US EPA health risk assessment model. The health endpoints for urban dwelling women were further assessed through an exposure-reponse model. We found that the hourly inhaled dose of PM10 obtained through the nasal filter was about 2.5–17.6 times that obtained by AFS, which also resulted in 4.41–11.30 times more morbidity than estimated by AFS (p < 0.05). Proximity to traffic emissions resulted in greater exposure to particulate matter (>18.8 μg/kg·h) and heavy metals (>2.2 ng/kg·h), and these populations are therefore at greatest risk of developing non-cancer (HI = 4.16) and cancer (Rt = 7.8 × 10−3) related morbities.

Anthropogenic and climatic factors interact to influence reproductive timing and effort.

Reproduction, although absolutely essential to a species' persistence, is in itself challenging. As anthropogenic change increasingly affects every landscape on Earth, it is critical to understand how specific pressures impact the reproductive efforts of individuals, which directly contribute to the success or failure of populations. However, organisms rarely encounter a single burden at a time, and the interactions of environmental challenges can have compounding effects. Understanding environmental and physiological pressures is difficult because they are often context-dependent and not generalizable, but long-term monitoring across variable landscapes and weather patterns can improve our understanding of these complex interactions. We tested the effects of urbanization, climate, and individual condition on the reproductive investment of wild side-blotched lizards (Uta stansburiana) by measuring physiological/reproductive metrics from six populations in urban and rural areas over six consecutive years of variable precipitation. We observed that reproductive stage affected body condition, corticosterone concentration, and oxidative stress. We also observed that reproductive patterns differed between urban and rural populations depending on rainfall, with rural animals increasing reproductive investment during rainier years compared to urban conspecifics, and that reproductive decisions appeared to occur early in the reproductive process. These results demonstrate the plastic nature of a generalist species optimizing lifetime fitness under varying conditions.

Compound successive dry-hot and wet extremes in China with global warming and urbanization

Global warming has accelerated the interregional hydrological cycle, resulting in a significant increase in the frequency and intensity of extreme events worldwide. These events often involve a combination of spatial and temporal factors, giving rise to compound events. Among them are rapid transitions from dry and hot conditions to wet (DHW) events, which can have more severe impacts on human societies and ecosystems than individual extreme events. Urbanization not only heightens the likelihood of disasters but also exacerbates the exposure of affected populations. However, there has been insufficient attention given to understanding the connections between these successive compound events and the heightened risks posed by the increasing urbanization. In this study, we used bias-corrected daily precipitation and maximum temperature data simulated by the Coupled Model Intercomparison Project Phase 6 (CMIP6) model, along with historical and future daily runoff data simulated by the Variable Infiltration Capacity (VIC) model. We systematically investigated the spatiotemporal changes (i.e., frequency, duration, intensity, and compound probability) in DHW events in China during the upper historical period (1979–2014) and under two medium and high emission scenarios (2015–2100) − Shared Socioeconomic Pathways (SSP245 and SSP585). Furthermore, we examined how extreme runoff responds to variations in maximum temperature and daily precipitation during DHW events. We also evaluated the potential population and urban exposure to DHW events using dynamic future population and urban expansion data to assess the potential risks. Our results indicate that the multi-model ensemble predicts varying spatial patterns of future DHW events under SSP245 and SSP585 scenarios. The frequency and duration of these events are expected to decrease by approximately 20–25% in the Northwest region under both scenarios. At the same time, the middle and lower plains of the Yangtze and Yellow River Basins experience increased occurrence, broader geographical impact, and higher DHW event intensity alongside urban expansion and population growth in these regions. Specifically, event intensity is anticipated to increase by a factor of approximately 7–11. Temporally, we expect short-duration, high-intensity DHW events to occur in the Yangtze and Yellow River basins around 2023, 2038, and 2058, respectively. The primary driver for the future rise in population exposure to DHW events is the expected increase in event frequency in the middle and lower plains of the Yangtze and Yellow River basins, concurrent with population growth in these regions. Under the medium emission scenario, the total exposed population to DHW events in China is predicted to be 1.61 times higher than the historical baseline. In contrast, the high-emission scenario estimates a total population of 2.41 times higher than the historical baseline period. Most of the DHW events occurred in regions that exhibit a positive dependence between high temperature and high runoff events, serving as the primary driver of DHW events. Urbanization has a positive impact on DHW events, with the effect under high emissions being approximately 30% higher than during the historical base period. Additionally, it is indicated that future DHW events will exhibit higher sensitivity to global warming, with intensity projected to increase approximately fourfold and the exposed population to rise by about 1.5 times for every 1 °C of global warming. This research enhances our understanding of forecasting future compound hydrometeorological extreme events under different scenarios and provides insight into the role of climate change and urbanization in shaping these events in China.

Медико-демографические показатели населения села старше трудоспособного возраста в Республике Татарстан.

Demographic aging of the population is especially pronounced in rural areas and has regional differences. Purpose of the study: based on the analysis of medical and demographic indicators of mortality and morbidity of the rural population over working age in the Republic of Tatarstan (RT), to develop proposals for the health of rural residents. Methods and materials: analytical, statistical, Rosstat data and statistical materials from the Federal State Budgetary Institution “TsNIIOIZ” of the Russian Ministry of Health (2019–2022). Results. During the years of pension reform (2019–2023), the number of rural population in the Republic of Tatarstan increased slightly – from 922,3 to 930,3 thousand (0,9%), while the share of the rural population over working age decreased from 28,6 up to 27,5% (in the Russian Federation the figure is lower – 25,5%). The mortality rates of the rural population in the Republic of Tatarstan were significantly higher for all years of analysis: in 2013 they were 15,5‰ for the rural population, 11,0 for the urban population. During the years of COVID‑19, mortality rates increased and amounted to 18,9‰ for the rural population in 2021, 14,4‰ for the urban population, and decreased in 2022 and amounted to 14.1 and 10,3‰, respectively. The primary morbidity rate of the rural population in the Russian Federation (2022) was 64755,2 per 100 thousand rural population, in the Republic of Tatarstan it was lower – 52598,4‰îî. There is a large difference in the indicators in the constituent entities of the Russian Federation – 13,6 times. The newly identified morbidity in the rural population over working age in the Republic of Tatarstan is lower than 48996,2‰îî than in the entire rural population 52598,4‰îî, while the older population has the highest rates of primary morbidity due to diseases of the circulatory system, diseases of the bones, muscular system and connective tissue, diseases of the genitourinary system, diseases of the nervous system, neoplasms, etc. In this age group, the incidence of COVID‑19 is higher. The results of the study should be taken into account for the development of management decisions at the regional level, as well as when implementing programs for rehabilitation and medical examination of older patients.

Revealing the Urban Heat Island: Investigating Spatiotemporal Surface Temperature Dynamics, Modeling, and Interactions with Controllable and Non-controllable Factors

The unchecked growth of urban populations has spurred rapid and unsustainable urban expansion, exacerbating environmental degradation both locally and globally. This research offers a comprehensive analysis of the Urban Heat Island (UHI) phenomenon in Bangalore and Hyderabad, India, focusing on its spatial and temporal distribution and its correlation with air pollutants. Conducted over the course of summer and winter seasons from 2001 to 2021, this study advances our understanding of UHI dynamics in rapidly urbanizing regions, shedding light on the complex interplay between urbanization, climate, and air quality. The primary objective of this study is to assess and compare the predictive performance of baseline Random Forest (RF) and XGBoost models for forecasting Land Surface Temperature (LST) in Hyderabad, India, while examining the impact of integrating spatial information into these models. Incorporating an extensive array of input variables, ranging from spectral indices to terrain features, land use/land cover (LULC) data, atmospheric parameters, and spatial data, the analysis reveals significant relationships between LST and various factors. Notably, Aerosol Optical Depth (AOD) and Normalized Difference Built-up Index (NDBI) exhibit strong positive correlations with LST, while Enhanced Vegetation Index (EVI) demonstrates a pronounced negative correlation. Similarly, Modified Normalized Difference Water Index (MNDWI) displays the strongest positive correlation with LST (r = 0.49), whereas EVI exhibits the most notable negative correlation (r = -0.67). Additionally, Modified Built-up Index (MBI) demonstrates a noteworthy negative correlation (r = -0.48) with LST. Atmospheric components such as CO, NO2, and O3 also show significant positive correlations with LST, with NO2 displaying the highest correlation during winter in urban areas. XGBoost emerges as the superior model, achieving high R2 values of 0.84 for Bangalore and 0.88 for Hyderabad. Integration of spatial information consistently enhances the performance of both RF and XGBoost models, resulting in reduced Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) values. However, predictions over water bodies tend to be underestimated. The study underscores the importance of incorporating spatial data for accurate LST prediction, with implications for environmental monitoring and management. Despite the advancements made, further research is needed to elucidate the specific mechanisms through which spatial information improves model performance, suggesting avenues for future investigation and refinement of modeling methodologies. At the intersection of rapid urban growth and environmental conservation, these findings are poised to inform policymakers, urban planners, and researchers, guiding them towards innovative and sustainable urban development strategies aimed at mitigating the adverse impacts of rapid urban expansion while promoting ecosystem well-being. The study contributes to spatial modeling strategies and refines LST prediction methodologies, offering valuable insights for addressing the challenges posed by urbanization and climate change in rapidly urbanizing regions.

Assessing the Relationship Between Land Surface Temperature and Air Temperature for Thermal Comfort Analysis in Can Tho City: Implications for Sustainable Spatial Planning

This study aims to assess the relationship between land surface temperature (LST) and air temperature (Tair) and thermal comfort analysis in Can Tho City. LST is derived from the Landsat thermal band, while Tair is measured using 15 monitoring stations. A Temperature Humidity Index (THI) is employed as a preferred metric for analyzing the thermal comfort level, calculated based on a combination of Tair and relative humidity (RH). Besides quantitative data collection, the study also survey residents in high heat risk areas to acquire insights into their current experiences and perceptions heat. The results reveal that individuals in high SUHI areas experience discomfort across varying temperature ranges, influenced by particular working conditions and personal characteristics. Moreover, the findings highlight a significant correlation between LST and Tair, reaching a coefficient of 0.73, indicating the applicability of land surface temperature in large-scale temperature calculations. These results underscore the need for further research on the extent of heat-related health effects and the development of strategies for heat stress mitigation. Understanding of thermal comfort and the impacts of SUHI is crucial for addressing climate and societal challenges and enhance the well-being of urban populations.

Securing Smart Cities: A Cybersecurity Perspective on Integrating IoT, AI, and Machine Learning for Digital Twin Creation

The burgeoning evolution of smart cities, characterized by the integration of the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML), heralds a transformative era in urban management and citizen engagement. These technological advancements promise enhanced efficiency in city operations, improved public services, and a sustainable urban environment. However, the complexity and interconnectedness inherent in these systems introduce significant cybersecurity challenges, necessitating innovative approaches to safeguard the digital infrastructure of smart cities. This paper aims to explore the cybersecurity landscape of smart cities from the perspective of integrating IoT, AI, and ML for the creation of digital twins, offering a comprehensive analysis of the opportunities and threats within this domain. Smart cities leverage IoT to connect various components of the urban infrastructure, including transportation systems, utilities, and public services, creating an integrated network of devices that communicate and share data. The incorporation of AI and ML into this framework facilitates intelligent decision-making, enabling the automation of services and the optimization of resources. This synergy enhances the quality of life for residents, promotes economic development, and supports sustainable environmental practices. However, the dependence on digital technologies also exposes smart cities to a range of cybersecurity risks, from data breaches and privacy violations to the disruption of critical infrastructure. The integration of IoT, AI, and ML in smart cities, while offering unprecedented opportunities for urban innovation, also amplifies the complexity of the cybersecurity landscape. IoT devices, often designed with minimal security features, become potential entry points for cyber attacks. The vast amount of data generated and processed by these devices, if compromised, could lead to significant privacy and security breaches. AI and ML models, for their part, are susceptible to manipulation and bias, which can undermine the integrity of decision-making processes. The interconnectivity of systems means that a breach in one sector could have cascading effects throughout the city's infrastructure. Against this backdrop, the paper investigates the role of digital twins in mitigating cybersecurity risks in smart cities. Digital twins, digital replicas of physical entities or systems, offer a powerful tool for simulating and analyzing smart city operations, including cybersecurity scenarios. By mirroring the city's infrastructure in a virtual environment, digital twins allow for the identification of vulnerabilities, the simulation of cyber attacks, and the evaluation of potential impacts. This proactive approach to cybersecurity enables city administrators to anticipate threats and implement protective measures before real-world systems are compromised. The research questions guiding this inquiry include: How can the integration of IoT, AI, and ML enhance the resilience of smart cities against cyber threats? What are the specific cybersecurity challenges presented by these technologies, and how can they be addressed? And, most crucially, what role can digital twins play in fortifying the cybersecurity defenses of smart cities? To address these questions, the paper begins with a review of the current state of smart city technology, focusing on the integration of IoT, AI, and ML. It then delves into the cybersecurity challenges unique to this technological landscape, drawing on recent examples of cyber incidents in smart cities. The analysis highlights the vulnerabilities introduced by the widespread use of IoT devices and the complexities of securing AI and ML systems. Following this, the discussion turns to the potential of digital twins as a cybersecurity tool, examining how they can be employed to detect vulnerabilities, simulate attacks, and plan responses. The paper argues that while the integration of IoT, AI, and ML in smart cities presents significant cybersecurity challenges, it also offers opportunities for innovative solutions. Digital twins emerge as a promising approach to enhancing the cybersecurity posture of smart cities, enabling a dynamic and proactive defense mechanism. By facilitating the simulation of cyber threats in a controlled environment, digital twins allow city administrators to identify weaknesses, test the efficacy of protective measures, and develop more resilient urban infrastructures. In conclusion, the integration of IoT, AI, and ML in smart cities represents a double-edged sword, offering both remarkable opportunities for urban innovation and formidable cybersecurity challenges. This paper underscores the critical importance of adopting a cybersecurity perspective in the development and management of smart cities, highlighting the potential of digital twins as a strategic tool in mitigating these risks. As smart cities continue to evolve, embracing these technologies in a secure and responsible manner will be paramount in realizing their full potential while safeguarding the digital and physical well-being of urban populations.

What implications do primary energy use, urban population agglomeration, and economic development rendered to Romania's environmental sustainability?

The energy sector plays a significant role in Romania's expanding economic and security agenda. The energy sector utilizes a diverse range of resources, such as renewable energy, nuclear power, coal, and natural gas. Traditionally, the primary sources of the country's energy have been coal and natural gas, while hydroelectricity and nuclear power have played supplementary roles. The present study's major purpose is to establish how primary energy consumption, fossil fuel consumption, urban population agglomeration, and economic growth affect CO2 emissions in Romania. The time series data covering 1990 to 2021 was analyzed using the symmetric Autoregressive Distributed Lag (ARDL) approach in conjunction with robust least squares technique. The study's findings demonstrated that in both the short and long run, variables such as fossil fuel consumption, urban population agglomeration, and economic growth had a positive influence on CO2 emissions. Primary energy consumption has a detrimental influence on environmental quality in Romania. Similarly, the results of the robust least squares technique show that distinct variables have specific effects on environmental quality. It particularly shows that fossil fuel consumption, urban population agglomeration, and economic growth have a positive effect on CO2 emissions. However, empirical evidence indicates that primary energy consumption has a negative impact on CO2 emissions. It is essential for the Romanian government to take more steps to successfully address the problem of environmental degradation inside the country, with the ultimate goal of increasing economic growth.