ABSTRACT Over the past few decades, the intensification of global warming has brought increased attention to urban thermal dynamics, particularly regarding Land Surface Temperature (LST) and the Urban Surface Heat Island (SUHI) effect. This study conducts a systematic literature review alongside a bibliometric analysis of 123 peer‐reviewed articles published between 2020 and 2025, examining how these phenomena relate to environmental variables. Using the PRISMA framework and VOSviewer software, we mapped thematic trends, methodological approaches, and key research gaps, classifying studies by publication year. Additionally, we conducted an empirical assessment using a case study dataset to compare several statistical techniques—including Pearson's correlation, ordinary least squares regression (OLS), geographically weighted regression (GWR), and spatial models such as SEM and SAR—to evaluate their explanatory power. Findings reveal that although conventional methods like Pearson and OLS remain dominant, spatial techniques are gaining traction, offering improved residual independence and better model performance. Drawing from this, we propose a practical methodological guide for researchers, reviewers, and editors to support the selection of appropriate statistical tools according to the spatial characteristics of the data. Ultimately, the study aims to encourage the inclusion of spatial reasoning in urban thermal analysis and to foster more accurate, context‐sensitive interpretations.

Extreme heat waves (HWs), defined here as periods when daily maximum temperature exceeds the 98th percentile for three or more consecutive days, pose a significant threat to public health. The extreme heat risk is particularly relevant to urban residents due to the urban heat island (UHI) effect and its synergistic interactions with HWs in some cities. Previous research on the interactions between UHI and HWs has focused on a single city or region, and both positive and negative interactions have been reported. The global patterns of interactions between UHI and HWs across various climate backgrounds, as well as their underlying mechanisms, remain unclear. Here, we simulate the global urban heat island intensity (UHII) from 1985 to 2013 using the Community Land Model (CLM). By conducting a global-scale analysis of interactions between UHII and HWs, we diagnose their spatial and diurnal patterns across different regions and climate zones. To identify and explain the key contributors to the UHI-HW interactions, we employ machine learning models (CatBoost) and the SHapley Additive exPlanations (SHAP) framework to quantify the contributions of local energy flux, climate background, and land surface characteristics. UHI-HW interaction is quantified as the difference between UHII during heatwave (HW) days and non-heatwave (NHW) periods. We find that the UHI-HW interaction, which peaks at 6 AM local solar time (LT), is more positive at night than during the day. We identify net longwave radiation as a strong indicator of the interaction, while humidity emerges as a key driver, alongside contributions from sensible heat flux and wind speed. However, the influence of these factors varies across different Köppen-Geiger climate zones. Our study provides new insights into the complex interaction between UHIs and heat waves, with implications for urban climate adaptation strategies in a warming world. The machine learning-based approach offers a novel method for attributing the spatial variability in UHI heat wave interactions to specific biophysical variables.

Rapid urbanization is reshaping thermal environments worldwide, with the strongest impacts occurring at the interface between urban and non-urban areas. Impervious surfaces, as key indicators of urban expansion, are critical for monitoring urban growth and assessing surface urban heat island (SUHI) effects. Land use and land cover change (LULCC) provides an essential link between urban dynamics and their environmental and societal consequences. Here, we integrated the U.S. Geological Survey (USGS) Climate Global Issues (CGI) Land Cover Product with Landsat thermal time-series to investigate SUHI evolution in two contrasting metropolitan regions: Wuhan, China, and Brasília, Brazil. Using data spanning 1986–2023, we analyzed the relationships between land cover, Landsat-based land surface temperature (LST), and SUHI intensity, and identified persistent thermal hotspots. Results demonstrate that the land cover data utilized increases the accuracy of impervious surface mapping along urban–rural gradients. Average SUHI intensities were 3.4 °C in Wuhan and 3.3 °C in Brasília, with statistically significant warming trends of 0.04 °C/year and 0.01 °C/year, respectively. Maximum temperature proved to be a robust indicator of SUHI intensification, capturing long-term upward trends. Our findings highlight the important role of urban land cover dynamics in shaping temporal SUHI variability and hotspot emergence. This prototype framework demonstrates the scientific and policy value of combining long-term land cover monitoring information with satellite thermal monitoring to quantify and track SUHI at city scale, supporting sustainable urban planning and climate adaptation strategies.

GIS-based assessment of thermal dynamics and urban heat Island effects in response to land use changes in Ziro Valley, Arunachal Pradesh, India

The intensification of the urban heat island effect is an environmental and health problem that increases air temperature, summer energy demand, and heat stress. This study focuses on mitigation measures involving roofs due to their scalability and high surface availability. An exploratory and critical literature review was conducted, with a systematic search of indexed databases (mainly Scopus and Web of Science, as well as complementary sources), covering the period 2000–January 27, 2026, with eligibility criteria for empirical studies, modeling, and reviews with quantitative results, standardized extraction, and quality/bias risk assessment adapted to the type of evidence. The synthesized findings show that cool roofs consistently reduce surface temperature (typically 8–20 °C in summer) and generate measurable decreases in urban air temperature; at the city scale, a 0.1 increase in average roof albedo is associated with approximate reductions of 0.1–0.33 K, in addition to significant decreases in cooling loads and peak load. Green roofs provide thermal attenuation through evapotranspiration and hydrological co-benefits (retention and runoff delay), but their performance depends on substrate, moisture/irrigation, and maintenance. It is concluded that both strategies are complementary and should be prioritized according to climate, urban morphology, fraction intervened, life cycle costs and co-benefit targets, with an emphasis on maintenance and a focus on “hot spots” and vulnerable populations.

This article proposes and studies two Huber-type estimation approaches, namely, the Huber instrumental variable (IV) estimation and the Huber generalized method of moments (GMM) estimation, for a spatial autoregressive model. We establish the consistency, asymptotic distributions, finite sample breakdown points, and influence functions of these estimators. Simulation studies show that compared to the corresponding traditional estimators (the two-stage least squares estimator, the best IV estimator, and the GMM estimator), our estimators are more robust when the unknown disturbances are long-tailed, and our estimators only lose a little efficiency when the disturbances are short-tailed. Moreover, the Huber GMM estimator also outperforms several robust estimators in the literature. Finally, we apply our estimation method to investigate the impact of the urban heat island effect on housing prices. A package is published on GitHub for practitioners to use in their empirical studies.

Moths (Lepidoptera: Heterocera) represent a dominant group within the order Lepidoptera, playing indispensable roles in global ecosystems. This review synthesizes current knowledge on moth morphology, developmental processes, species diversity, ecological contributions, and the multifaceted threats they face. By examining their life cycle—encompassing egg, larva (caterpillar), pupa, and adult stages—we highlight their intricate physiological adaptations and evolutionary significance. With an estimated global diversity exceeding 255,000 species, moths surpass butterflies in ecological and taxonomic richness, contributing to nutrient cycling, energy transfer, pollination, and population regulation. However, anthropogenic pressures, including habitat destruction, light pollution, urban heat islands, and climate change, pose severe risks to moth populations, resulting in declines in species richness and abundance. Drawing on extensive literature, this study elucidates differential responses among moth families to environmental disturbances and proposes actionable conservation strategies, such as mitigating light pollution, restoring habitats, and enhancing monitoring programs. As ecological indicators, moths provide critical insights into environmental health. Future research should integrate interdisciplinary approaches to deepen our understanding of moth-ecosystem interactions, supporting biodiversity conservation efforts. This review aims to provide a scientific foundation for researchers and policymakers to advance moth conservation.

In the midst of increasing global warming and accelerated urbanization, urban parks, serving as significant carbon sinks, are increasingly recognized for their role in mitigating the urban heat island effect. However, limited research investigating the urban park carbon cycle hinders our full understanding and effective use of their carbon sink potential. This study employed metagenomics sequencing and 16S rRNA gene sequencing to characterize the carbon cycle and its influencing factors within soil and water from collected from nine city parks. Notably, the abundance and alpha diversity of carbon cycle microbes and genes were higher in soil compared to water. Specifically, soil samples exhibited enrichment of carbon cycling genes involved primarily in polysaccharide metabolism, particularly those associated with starch and cellulose metabolism. Conversely, water samples, revealed a greater prevalence of genes associated with chitin metabolism. The most important factor affecting soil carbon cycling genes was bacterial community, followed by non-nutritional factors and nutrient factors, while heavy metals demonstrated no effect on soil carbon cycling genes. The most important factor affecting water carbon cycling genes was only bacterial community. The analysis yielded 381 high-quality metagenomic assembled genomes (MAGs) containing carbon cycling genes, with significant covariation observed between the pta and carbon cycling genes ackA and acyP, which encode cellulose degradation functions. These findings contribute to a better understanding of microbial carbon metabolism within urban parks and offer a foundation for effective carbon emission management strategies.

This study investigates the interplay between urban morphology, vegetation, and thermal environments by integrating mobile air temperature (AT) measurements with satellite-derived land surface temperature (LST). The case study is the city of Bologna (Italy). Correlation analysis revealed strong multicollinearity among morphological indicators, with building density and floor area ratio nearly collinear, while vegetation cover (PV) remained the most independent predictor. A composite urban density indicator (CUDI), derived through principal component analysis, was introduced to address redundancy among morphological metrics. Ordinary least squares regressions demonstrated significant associations, with PV exerting a pronounced cooling effect and CUDI amplifying both AT and LST. Model diagnostics confirmed statistical robustness, though residual spatial autocorrelation necessitated spatial regression approaches. Spatial lag models (SLMs) substantially improved explanatory power, highlighting spatial spillovers and neighborhood effects as central to understanding urban heat dynamics. Comparative analysis with spatial error models reinforced the dominance of SLM in capturing localized dependencies. Despite limitations in spatial coverage, temporal scope, and indicator transferability, findings emphasize the critical roles of vegetation and urban compactness in shaping thermal environments. This work underscores the necessity of integrating greening strategies with urban form management for effective heat mitigation and provides a methodological framework for analyzing urban heat islands through multi-source thermal and morphological data.

This study quantitatively evaluates the health impacts of urban temperature changes and the potential health benefits of highly reflective roofs as an urban heat island (UHI) mitigation measure. First, empirically derived relationships between ambient temperature and health-related indicators were established for multiple diseases, including both fatal and non-fatal outcomes. Health impacts were assessed using disability-adjusted life years (DALYs), integrating years of life lost (YLLs) and years lived with disability (YLDs). Target diseases included heat- and cold-related mortality, heatstroke, infectious diseases, sleep disturbance, and fatigue. Next, a meteorological simulation was conducted using a Weather Research and Forecasting (WRF) model to estimate outdoor air temperature changes resulting from the implementation of highly reflective building roofs in Osaka Prefecture, Japan. Roof surface reflectance was increased from 0.15 to 0.65 within an urban canopy model, and temperature reductions were evaluated at a 2 km spatial resolution for a one-year period. The results indicate that highly reflective roofs reduced daytime air temperatures by approximately 1.2–1.8 °C, with greater effects observed in high-density urban areas. By integrating the simulated temperature reductions with the temperature–health relationships, annual health impacts were quantified. Although wintertime increases in cold-related health burdens were observed, the annual cumulative DALYs decreased by 1767, corresponding to approximately a 5% reduction in total temperature-related health burdens in Osaka Prefecture. These findings demonstrate that rooftop reflectivity enhancement can contribute to net health improvements while highlighting the importance of accounting for seasonal trade-offs in UHI mitigation strategies.

Cities are often warmer than rural surroundings due to a phenomenon known as the urban heat island, which can be influenced by various factors, such as regional climate and land surface types. Under climate change, cities face not only the challenge of increasing temperatures in their surrounding hinterland but also the challenge of potential changes in their heat islands. However, even high-resolution global Earth system models (ESMs) with "urban tiles" can only properly resolve the largest urban areas or megacities. Here, we address these limitations by applying a process-based statistical learning model to ESM outputs to provide projections of changes in land surface temperature (LST) for 104 medium-sized cities of population 300 K to 1 M in the subtropics and tropics. Under a 2 °C global warming scenario, annual mean LST in 81% of these cities is projected to increase faster than the surrounding area. In 16% of these cities, mostly in India and China, mean LST is projected to increase by an additional 50-112% above ESM projections of the surrounding area. Our findings underscore the importance of investigating the specific effects of climate change on urban heat exposure.

Human development is a driver of global change and a major threat to biodiversity. Protected areas maintain and support biodiversity, but outside stressors, such as climate change and land use change, can negatively influence natural resources within protected areas. We examined the effects of land surface temperature and the surrounding landscape context on the structure and composition of the breeding bird community in national parks in the Mid-Atlantic (USA). We used avian point count surveys, conducted annually from 2007 to 2024, to estimate the composition of 16 avian guilds and estimated land surface temperature at each survey point. We defined 3 landscape context types (forested, urban, and agricultural) based on the dominant land cover surrounding each survey point. We used multivariate generalized linear models to test community-level (all guilds combined) and guild-level (individual guilds) responses to local land surface temperature and landscape context. We hypothesized a negative relationship between within-guild abundance and land surface temperature, and stronger negative relationships in specialist guilds and variation in response based on the landscape context. Landscape context influenced local land surface temperature and, therefore, avian guild responses. Points in forest-dominated landscapes averaged 2°C cooler than points in urban or agricultural landscapes. The majority of specialist guilds had an interaction with land surface temperature and landscape context. There were negative effects of high land surface temperature on the bird community. These effects differed across landscape context, with less extreme negative relationships detected at points surrounded by forest relative to points in urban or agricultural landscapes. Because increased forest cover is important to retain natural cooling and mitigate the effects of urban heat, preserving or increasing forest cover could help preserve and maintain bird community resilience in a warming climate.

Urban expansion drives both loss and compensation in city vegetation productivity.

Quantifying highway expansion impact on urban heat island effect in San Francisco bay area

Assessing thermal inequities: A spatiotemporal analysis of socio-demographic and urban green space in New York City.

Understanding the causal influence of energy balance components on urban heat islands in a mid-latitude city - Chongqing, China

Spatial and seasonal effects of different LCZ types on Urban Heat Islands in a Mediterranean urban area (Beirut, Lebanon)

Abstract Urban thermal discomfort is an escalating concern, particularly in arid cities undergoing rapid urbanization and climate change. Addressing this issue is essential for enhancing urban resilience and livability in vulnerable arid environments, like the Arabian Peninsula (AP). This study leverages Google Earth Engine, remote sensing datasets, and advanced thermal indices to evaluate spatiotemporal variations in urban thermal discomfort across 13 cities of the AP from 1990 to 2024. The results indicate that although many cities achieved a 20–40% increase in vegetation cover, localized land degradation persists in Abha and Sanaa (5–10%). This spatial disparity in vegetation recovery has influenced urban heat dynamics. Inland cities, including Riyadh, Madinah, and Makkah, exhibit the highest increases in Land Surface Temperature (LST), while cities at higher elevations and coastal locations benefit from natural cooling effects. Interestingly, highly urbanized areas often recorded lower Urban Heat Island (UHI) effects than peri-urban and rural areas, suggesting that dense urban morphology and materials with high thermal inertia can mitigate localized heating. The analysis of the Urban Thermal Field Variance Index (UTFVI) reveals that over 80% of urban areas experience high or extreme thermal stress; however, Doha, Muscat, and Sharjah demonstrate significant reductions through strategic urban greening and planning, as seen earlier through a few case studies in the region. This paper offers critical insights into sustainable urban planning and climate adaptation strategies to improve urban thermal comfort and livability in arid cities. Future research should integrate detailed land use assessments, seasonal and nocturnal thermal analyses, and dynamic modeling of key indices to accurately understand urban thermal dynamics.

Public pool usage as adaptation against urban heat

Quantifying the impact of vegetation greenness change on surface urban heat islands across 133 major cities of China.