Search Results (6,585)

With global warming and the increasing frequency of extreme heat events, heat health risk assessment (HHRA) has become a critical topic in climate change studies. However, the study themes, methods, and governance orientation of HHRA vary significantly across spatial scales, limiting the comparability and practical integration of assessment outcomes. This study conducts a review of the HHRA literature from 2007 to 2025, analyzing publication trends and evolving research paradigms. The results indicate the following: (1) rapid growth in the field with a notable shift from identifying static vulnerabilities to adopting “Hazard–Exposure–Vulnerability–Adaptability” (HEVA) frameworks, particularly at the micro-scale; (2) a clear scale-dependent hierarchy in assessment focus, where macro-scale studies identify regional trends, meso-scale research targets urban spatial heterogeneity and green–blue infrastructure, and micro-scale assessments emphasize housing conditions and individual perceptions; and (3) machine learning has been widely applied to capture complex nonlinear mechanisms and threshold effects. Finally, this study further emphasizes the importance of establishing a full-process feedback mechanism from macro-level early warning to meso-scale planning and micro-scale intervention, bridging the gap between regional policy and community-level action and providing a theoretical foundation for building climate-resilient cities. Full article

The rapid growth of electric vehicle (EV) adoption requires the scalable and cost-effective deployment of publicly accessible charging infrastructure, where cost-effectiveness is understood in terms of infrastructure reuse rather than explicit economic optimisation. Integrating slow AC charging units into existing public lighting networks represents a promising infrastructure reuse strategy, though spatial feasibility, electrical constraints, and regulatory requirements must be addressed. This study proposes an integrated GIS–MCDA–MILP framework for the optimal allocation of EV charging stations within public lighting systems. GIS-based spatial analysis identifies feasible poles based on parking accessibility and demand indicators, while MCDA ranks candidate locations and a MILP model determines optimal deployment under capacity constraints and phased rollout scenarios. The framework also incorporates AFIR-based policy benchmarking to assess compliance under current and future EV adoption levels. A real-world case study identifies 1223 feasible poles with a structural hosting capacity of 368 chargers. The results demonstrate that such integration is viable at the spatial and cabinet-capacity planning level but structurally limited, with a critical fleet growth multiplier of approximately 3.4 identified as the threshold beyond which lighting-integrated deployment alone becomes insufficient for AFIR compliance. The proposed framework advances the state of practice by coupling spatial, electrical, and regulatory analysis within a single reproducible methodology, offering a transferable decision-support tool for sustainable urban EV charging planning. Full article

The Gulf Cooperation Council (GCC) countries consistently rank among the highest per capita CO₂ emitters globally, yet rigorous empirical analysis of the structural drivers of these emissions in the post-Paris Agreement era remains scarce. This study investigates the determinants of CO₂ emissions per capita across six GCC economies—Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates—over the period 2015–2022, using pooled ordinary least squares (OLSs) and country fixed effects (FEs) panel regression models with country-clustered standard errors. The focal explanatory variable is energy use per capita, complemented by GDP per capita, trade openness, urbanization, foreign direct investment (FDI), and industry value added as controls. A quadratic income term explicitly tests the environmental Kuznets curve (EKC) hypothesis. Results consistently show that energy use is the dominant driver of emissions. The EKC hypothesis is supported in the FE framework. The implied turning point of approximately USD 85,500 per capita (constant 2015 USD) is already exceeded by Qatar (panel mean: USD 114,835) and approached by the UAE (USD 71,434), while Bahrain (USD 55,681), Kuwait (USD 51,531), Saudi Arabia (USD 61,232), and Oman (USD 38,591) remain on the EKC’s rising slope, consistent with their continued emissions’ growth trajectories. Urbanization exerts a significant positive within-country effect on emissions. Trade openness reduces emissions in cross-sectional specifications, while FDI is systematically insignificant. These findings support energy efficiency reforms, renewable energy expansion, and low-carbon urban planning as the most effective policy levers for GCC decarbonization. Full article

Enormous land exploitation is triggering strong urban growth, and this phenomenon is exacerbating the already existing problem of rising land surface temperatures. This leads to increased human activities and a disruption of the balance of natural ecosystems. The application of thermal remote sensing techniques is, in this context, helpful in learning about the condition of the earth’s surface and monitoring how it changes over time. This paper utilizes thermal data from 2000, 2010 and 2020, with supplementary data from 2024, to assess current trends in two different seasonal conditions (rainy period and low rainy period). Two different areas (urban and rural) of the central Vietnamese Province of Thua Thien-Hue have been analyzed to compare them. Processing Landsat-5 TM, Landsat-7 ETM+, Landsat-8 OLI/TIRS, and Sentinel-2 satellite images, a heat map of the study area was defined, considering hot spots and cold spots. As support for this analysis, spectral indexes have been developed for a better comprehension of the land cover change over the years and to provide a validation of the thermal analysis. This paper aims to assess the threat posed by the intensification of the urban heat island effect on cultural heritage sites. The case studies are represented by areas where there are urban growing and cultural heritage sites to be preserved, such as the UNESCO-listed Hue Citadel. Full article

Under the dual pressures of global climate change and rapid urbanization, the spatial contradiction between urban expansion and flash flood disasters in mountainous dam areas is increasingly evident. However, the mechanisms by which the multi-dimensional characteristics of urban expansion affect regional flash flood susceptibility (FFS) remain unclear, limiting scientific guidance for source-level disaster prevention. This study uses Zhaotong City, a flash flood-prone area in the lower Jinsha River basin of southwestern China, as a case study. Using land use and multi-source remote sensing data from 2000 and 2025, we identify urban expansion patterns and morphological characteristics, apply the XGBoost-SHAP model to evaluate flash flood susceptibility and determine dominant factors, and employ the generalized additive model (GAM) to quantify the nonlinear responses of expansion dimensions to FFS. Results show the following: (1) Urban expansion in Zhaotong City is primarily edge (51%) and leapfrog (46%), clustering along river valleys, dam areas, and transportation corridors. (2) The XGBoost model performs well (AUC = 0.877). Elevation, slope, normalized difference vegetation index (NDVI), and precipitation are the primary natural factors influencing FFS. About 15.66% of the city falls within the high/very high FFS zones, mainly in the Zhaolu Dam area, riverbanks of main and tributary streams, and the urban built-up area. (3) Urban expansion-related indicators explain 28.6% of the spatial variation in FFS, with leapfrog expansion as the primary driver (contribution rate 32.75%). Disorderly urban growth and morphological imbalance significantly increase flash flood susceptibility. This study provides a scientific basis for spatial planning, flash flood prevention and control, and climate-adaptive urban development in similar mountainous dam areas in Southwest China and Asia, supporting regional sustainable development goals. Full article

Quantifying the distribution and spatiotemporal variation of water vapor and liquid water is of great significance for understanding the atmospheric thermodynamic processes during extreme meteorological events. The water vapor and liquid water data obtained from ground-based measurements by three MP-3000A microwave radiometers (MWRs) over the middle reaches of the Yangtze River Basin were analyzed. Firstly, a comparison between MWRs and radiosonde was conducted, and the co-located observation results indicated that MWRs used in this study feature high detection accuracy and favorable consistency. The integrated water vapor (IWV) measured by one of MWRs (Serial No. 3115) was with the best performance for IWV observation, and the bias and RMSE were 0.22 cm and 0.18 cm. In addition, the detection biases of integrated liquid water (ILW) between three MWRs in pre-precipitation were smaller than those in non-precipitation. All three instruments captured the diurnal variation characteristics of vapor density (VD) and liquid water content (LWC) profiles. The variation in ILW and IWV in different stations showed that ILW maintained low values before precipitation and increased sharply during the pre-precipitation stage, indicating strong indicative significance for rainfall occurrence. The ILW increment was more remarkable in Wuhan station, where mostly covered with urban and water body underlying surfaces. However, the magnitude of IWV variation before precipitation was smaller than that of ILW, especially in Jingzhou station. Under non-precipitation condition, VD and LWC vertical profiles at the three stations were relatively stable. Before precipitation, they exhibited substantial increases with obvious spatial discrepancies: sharp growth in Wuhan, moderate enhancement in Xianning, and slight increment in Jingzhou. Overall, atmospheric water vapor and liquid water increase significantly before precipitation, and their distribution spatiotemporal differences are closely related to local underlying surfaces and precipitation characteristics, which can provide meaningful references for short-term precipitation forecasting. Full article

The rate of urbanization in developing nations is on the rise; however, expedited urbanization concurrently precipitates the degradation of the urban water environment. Given the comparatively circumscribed data monitoring capabilities of developing nations, to check into internal factors that impact urban water quality and their interaction with limited data in a time of the growth of urbanization and assist developing countries in reconciling urbanization with the urban environment, this paper takes the vicennial development of two industrialized cities in China as a case study by constructing an index model based on a nonlinear improved analytic hierarchy process. The model parameters were calibrated and verified with data from Zunyi and Harbin cities in China. The results display that the root mean square errors of Zunyi’s and Harbin’s data were 0.093 and 0.096, respectively, manifesting satisfactory accuracy and the universality of the model. The dominating factors impacting urban water quality were further revealed by dissecting the model’s elements, which can provide accurate and scientific assistance in urban water quality management. Overall, these findings could help urban water quality management in developing countries, especially in regions where data monitoring is lacking. Full article

Riparian ecosystems provide numerous services that are critical to integrated, sustainable water management. Their ecological functions face various threats, however, including the construction of impervious surfaces that alter watershed hydrology. The understanding of risks and the design of adequate solutions to the threats posed by impervious cover requires assessment throughout entire watersheds. Yet few assessments have considered parcel-scale changes over larger extents, particularly using readily available public data. Seeking to better characterize recent patterns and to understand how characterizations differ with alternative spatial resolutions and assumptions, we assessed statewide change in impervious land cover within riparian areas in Washington State, USA. Leveraging open data from a public decision-support application, we generated estimates based on high-resolution (1 m) change detections for 2011 to 2017, intersected with riparian areas defined from the current management guidance. As an illustrative contrast, we constructed estimates based on the 2011 to 2016 change in a national dataset of 30 m resolution land cover within a fixed buffer on a coarser stream network. Complementing these depictions of change, we also estimated the 2021 standing impervious area using an independent 1 m land cover layer within the management-based riparian extent for the western portion of the state. The “best available” high-resolution estimate of change indicated that riparian and floodplain impervious cover increased by hundreds of hectares a year statewide during the early and middle 2010s. New impervious cover was more prevalent within reaches associated with urban growth areas (UGAs) and in portions of the assessed extent used by highly valued Pacific salmon. The coarser contrasting approach yielded a similar overall magnitude of change, but this served to clarify methodological sources of uncertainty rather than to confirm accuracy. Notably, in addition to capturing larger blocks of impervious increase, high-resolution data revealed many individual changes that were smaller than a single 30 m × 30 m pixel. In 2021, standing impervious cover was also concentrated in UGA-associated reaches, which contained 43.5% of the impervious area despite being 5.2% of the assessed extent. Much of the observed change within the assessed extent was likely outside of the local riparian regulatory jurisdiction at the time, but the patterns revealed by high-resolution monitoring data underscore the importance of continuing to strengthen riparian protections to maintain ecosystem function. Full article

Okun’s Law has been predominantly estimated at the aggregate level and for advanced economies, leaving its heterogeneity insufficiently explored in developing countries. This paper examines such heterogeneity for Ecuador, articulating for the first time in a developing and dollarised economy the multilevel estimation of the coefficient, the assessment of its temporal stability and the test for cyclical asymmetry within a single analytical framework. The relationship between economic activity and unemployment is estimated at the national level and for 19 disaggregations by area, sex, age, ethnicity and educational attainment, using monthly series from 2021 to 2025. The results suggest that the aggregate coefficient conceals a profound heterogeneity: Okun’s Law operates with intensity in the urban, female, youth, Afro-Ecuadorian and university-educated segments, yet is non-existent in the rural, male, older-age, indigenous and lower-education strata. This configuration is temporally robust and predominantly symmetrical between phases of the cycle, with specific exceptions in the Montubio and postgraduate segments. Economic growth reduces unemployment only in certain groups, whilst in the remainder the cyclical adjustment is channelled through margins that conventional statistics do not capture, suggesting that economic growth may be a necessary but not sufficient condition for improving labour market outcomes as a whole. Full article

Rapid digital transformation is reshaping global trade and raising important questions about its environmental impact, particularly in energy-intensive GCC economies. Despite growing interest, existing evidence remains inconclusive and often overlooks potential nonlinear effects. This study explores how digital trade influences environmental sustainability in Gulf Cooperation Council (GCC) countries over the period 2010–2024. Using a balanced panel dataset for the six economies, the analysis applies a fixed-effects approach with Driscoll–Kraay standard errors to account for cross-sectional dependence and other econometric concerns. To better capture the complexity of the relationship, the study also adopts an asymmetric framework that distinguishes between positive and negative changes in digital trade. The findings show that digital trade does not have a significant effect in the linear model. However, once asymmetry is considered, a clearer pattern emerges. Increases in digital trade are associated with lower CO₂ emissions, while decreases tend to raise emissions. Energy consumption remains the primary driver of emissions, while technological readiness helps reduce environmental pressure. Urbanization and political stability, on the other hand, are linked to higher emissions, reflecting ongoing structural challenges in the region. Overall, the results highlight the importance of sustaining digital trade growth and strengthening technological capabilities to support environmental sustainability in GCC economies. Full article

This study examines the long- and short-run determinants of green employment in Saudi Arabia over the period 1990–2024 using an Autoregressive Distributed Lag (ARDL) bounds testing framework within an error-correction model. Six macroeconomic and structural variables are analyzed: renewable energy capacity, GDP growth, domestic credit, urbanization, foreign direct investment, and the Vision 2030 policy regime shift. Supplementary analyses test the Environmental Kuznets Curve (EKC) hypothesis and map causal relationships using pairwise Granger causality tests. The bounds test indicates long-run cointegration among the variables (F = 8.45, exceeding the 5% I(1) critical bound of 3.61). The model explains 89% of the variation in log green employment (R² = 0.89) and passes standard diagnostic tests for serial correlation, heteroskedasticity, normality, and parameter stability. Three correlates of long-run green employment are identified. The post-2016 dummy used to capture the Vision 2030 regime shift is associated with the largest coefficient in the long-run equation (θ = 1.75, p = 0.008), although this estimate should be interpreted with caution because the dummy absorbs all post-2016 changes, including policy effects, the rapid expansion of renewable capacity, broader institutional reforms, and possibly changes in measurement practices. Renewable energy capacity is the primary continuously measurable driver (θ = 0.145, p = 0.018), with Toda–Yamamoto modified Wald tests indicating a bidirectional predictive relationship between investment and employment. Urbanization exerts a significant positive long-run effect (θ = 0.098, p = 0.001). The error correction term (δ = −0.520, p < 0.001) implies equilibrium reversion with a half-life of approximately one year. The EKC hypothesis is not supported in the Saudi context, suggesting that active decarbonization policy—rather than income-driven structural change alone—is needed for environmental improvement. The findings carry implications for Vision 2030 implementation and for other resource-dependent economies undertaking structural green transitions. Full article

This research presents a comprehensive spatial–temporal analysis of urban form and growth in Charlotte and Mecklenburg County, North Carolina, USA, from 1900 to 2017 at the land parcel level. Employing a data fusion framework, we integrate diverse datasets—including historical cadastral records, census data, remote sensing imagery, and infrastructure maps—to examine urban morphology through Euclidean and fractal geometries. Urban growth was reconstructed and visualized by decade and cumulatively, revealing dynamic patterns of expansion, densification, and fragmentation. Using scatterplot matrices and the Hausdorff box-counting algorithm, we quantified urban form across major land use types and temporal intervals. The fusion of socio-physical variables with mathematical functions enabled multi-scale modeling of urban transitions, aligning spatial, temporal, and thematic dimensions. Key findings include: (1) multidirectional spatial expansion resulting in a sprawling urban footprint at different rates over 117 years; (2) exponential growth between 1950 and 2000 with slower rates before and after manifesting a classic S-curve urban development by Northam; (3) a pivotal moment in 1993 when urbanized and rural lands reached parity, reflecting balanced urbanization in terms of population and land area for cities and rural areas for Mecklenburg; and (4) consistent quantitative relationships—linear, polynomial, exponential, logarithmic, and proportional—between urban form and growth metrics. This study’s novelty lies in its integrated spatial–temporal framework not only for combining both Euclidean and fractal geometric analyses with fused multi-source data to uncover the evolving structure of urban landscapes, but also for offering valuable insights into efficient land uses to assess equitable land and population dynamics, all aiming to achieve a good understanding of and sound policies for Charlotte, Mecklenburg and beyond. Full article

Based on the characteristics of cities as organic entities, this paper constructs a five-dimensional evaluation framework encompassing economy, industry, society, population, and space. A three-stage process of “fuzzy comprehensive evaluation—bi-level K-means clustering—state stability correction” is adopted to identify the development stages and spatiotemporal evolution of 286 Chinese cities from 2008 to 2023. The study finds that China’s urban development has shifted from “universal growth” to “divergent evolution,” exhibiting multiple characteristics such as the decline in the initial-stage cities and differentiation in the growth stage. Significant regional spatial differentiation is observed, with notable development gaps among the eastern, central, western, and northeastern regions, as well as between the northern and southern regions. Furthermore, most urban agglomerations exhibit a “mature center–lagging periphery” structure. Full article

This study critically examines residential tourism in Isla Cortés within the context of the real estate boom and the growing sustainability challenges facing coastal regions. Driven by global mobility, investment flows, and lifestyle migration, residential tourism is reshaping coastlines through intensive urban expansion. The analysis highlights the socio-environmental consequences of this model, including habitat fragmentation, mangrove loss, increasing pressure on water resources, and the gradual privatization of coastal areas. Using a qualitative research design that combines literature review, comparative case analysis, and territorial assessment, the study identifies structural similarities between Isla Cortés and other coastal tourism enclaves while emphasizing locally specific processes shaped by Mexico’s political economy and regulatory context. Findings suggest the structurally unsustainable character of this development pathway. Although residential tourism has stimulated short-term economic growth, it has also intensified socio-spatial segregation, commodified coastal commons, and generated long-term ecological and social vulnerabilities. The study challenges dominant narratives that portray residential tourism as inherently sustainable and instead draws on ecological reflexivity and socio-ecological systems perspectives to outline alternative planning pathways. It underscores the need for stronger regulatory frameworks, nature-based solutions, participatory governance, and regenerative planning strategies capable of aligning economic activity with ecological integrity and social inclusion in coastal territories. Full article

Nepal has undergone significant land-use and land-cover (LULC) changes from 2000 to 2020, driven by urbanization, agricultural shifts, and broader socioeconomic dynamics. This study analyzes historical changes and projects LULC dynamics for 2030, 2040, and 2050 across four scenarios: Business-as-Usual (BAU), Rapid Urban Development (RUD), Forest Degradation and Terai Contraction (FDTC), and Agricultural Land Abandonment and Ecological Recovery (ALER). A CA–Markov modeling framework in TerrSet was used to simulate future land-use patterns, utilizing scenario-specific transition probability matrices and spatial constraints to reflect different socio-economic and policy assumptions. Under the BAU scenario, land-use change remains moderate, characterized by gradual urban expansion and limited forest decline. On the contrary, the RUD scenario predicts a drastic expansion of built-up areas by about 1.44 million ha, along with significant losses of cropland, bare soil, grassland, and forest, reflecting intensified development pressure. The FDTC scenario emphasizes agricultural expansion at the expense of forests, while urban growth remains limited. Conversely, the ALER scenario demonstrates strong ecological recovery driven by cropland abandonment and secondary vegetation regeneration, resulting in notable expansion of forest and other woody land. Overall, these four scenarios reveal sharply divergent land-use trajectories, ranging from rapid urban transformation to ecosystem restoration. These contrasting land-use pathways highlight the critical importance of integrated land-use policies that can proactively manage urban expansion, safeguard high-value agricultural and forest landscapes, and promote ecological restoration through incentives for agricultural land abandonment and secondary vegetation recovery, thereby ensuring long-term sustainability and climate resilience in Nepal. Full article