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Rainfall-triggered landslides are most deadly in developing countries, and future urban sprawl and climate change could intensify existing risks. In these regions, enhancing efforts in urban landslide risk mitigation and climate change adaptation is crucial. Current landslide probability assessment methodologies struggle to support effective mitigation because they fail to represent local anthropogenic factors (e.g. informal housing) across space and time scales. To meet this challenge, we demonstrated in previous work that hillslope-scale mechanistic models representing such localised changes can be used to create synthetic libraries of urban landslides that account for both data and future scenario uncertainty. Here, we show how these libraries can become an explorative tool for researchers and stakeholders, allowing them to investigate slope stability variations across spatial scales and landscapes. Results highlight, for example, how the main slope instability drivers change according to the location (e.g., upper vs lower catchment), the landcover (e.g. forest vs urban) and the spatial scale analysed (e.g. at hillslope scale slope stability was mostly controlled by water table height, whereas at regional scale by slope geometry). Ultimately, we demonstrate that stochastic analyses can lead to a greater understanding of the system interactions and they can support the identification of mitigation strategies that perform well across spatial scales and uncertain scenarios. These strategies should be prioritised even if future conditions are unknown. This reasoning is shown on a data-scarce region with expanding informal housing. However, the same methodology can be applied to any urban context and with any mechanistic-based model. [ABSTRACT FROM AUTHOR]

Climate change has significantly altered the precipitation rates in Pakistan. Its capital Islamabad, once a dynamic, futuristic, and resilient city, faces floods during monsoon and chronic water scarcity during summer. Using empirical data and mixed research methods, this paper has analysed the flood and drought resilience of the city and its inhabitants. The study has established the notion that the city and its residents have partial coping capacity to heavier rainfalls which, if left unattended, may lead to serious economic and physical losses in the future. Moreover, the residents also suffer from acute water scarcity during summer, confirming the fact that the city no longer ensures sufficient water supply to all of its residents. Based on the research findings and suggestions received from various stakeholders such as the residents, key-informants, and city management; this paper has highlighted the strengths in city resilience and also identified the gaps, which warrant immediate attention. [ABSTRACT FROM AUTHOR]

The Sixth Assessment Report (AR6) of the United Nations Intergovernmental Panel on Climate Change (IPCC) highlights that Climate Resilient Development (CRD) can support sustainable development for all by reducing greenhouse gas emissions and offering adaptation options, while also contributing to innovation and transformation in urban areas. Based on China’s national pilot projects for low-carbon cities, sponge cities, and climate-resilient cities, this paper analyzes the potential and feasibility of Chinese cities in exploring the construction of “low-carbon-resilient cities”. First, referring to the relevant studies, the “Climate-Resilient Index” and “Low-Carbon Development Index” are constructed for 178 cities at or above the prefecture level in China. Second, by comparing the climate resilience index and the level of low-carbon development, this paper categorizes 178 Chinese cities into four groups with the potential to the co-benefits of synergy actions in mitigation and adaptation. Third, this paper showcases the potential and progress from some pilot model cities of the “climate-adaptive cities” in achieving the goal of “climate resilient development”. This paper will help advance China’s synergistic implementation of climate adaptation and mitigation policies, and provide examples and insights for cities in other developing countries to promote climate resilient development. [ABSTRACT FROM AUTHOR]

The urbanization effect (UE) on local or regional climate is a prominent research topic in the research field of urban climates. However, there is little research on the UE of Urumqi, a typical arid region city, concerning various climatic factors and their spatio–temporal characteristics. This study quantitatively investigates the UE of Urumqi on multiple climatic factors in summer based on a decade-long period of WRF–UCM (Weather Research and Forecasting model coupled with the Urban Canopy Model) simulation data. The findings reveal that the UE of Urumqi has resulted in a reduction in the diurnal temperature range (DTR) within the urban area by causing an increase in night-time minimum temperatures, with the maximum decrease reaching −2.5 °C. Additionally, the UE has also led to a decrease in the water vapor mixing ratio (WVMR) and relative humidity (RH) at 2 m, with the maximum reductions being 0.45 g kg−1 and −6.5%, respectively. Furthermore, the UE of Urumqi has led to an increase in planetary boundary layer height (PBLH), with a more pronounced effect in the central part of the city than in its surroundings, reaching a maximum increase of over 750 m at 19:00 Local Solar Time (LST, i.e., UTC + 6). The UE has also resulted in an increase in precipitation in the northern part of the city by up to 7.5 mm while inhibiting precipitation in the southern part by more than 6 mm. Moreover, the UE of Urumqi has enhanced precipitation both upstream and downstream of the city, with a maximum increase of 7.9 mm. The UE of Urumqi has also suppressed precipitation during summer mornings while enhancing it in summer afternoons. The UE has exerted certain influences on the aforementioned climatic factors, with the UE varying across different directions for each factor. Except for precipitation and PBLH, the UE on the remaining factors exhibit a greater magnitude in the northern region compared to the southern region of Urumqi. [ABSTRACT FROM AUTHOR]

Purpose: Since the occurrence of humans, soil has been affected by them. Thus, rural areas have been modified. Currently, urban areas are increasing extensively. They are locations with a high population density that are impacted by settlements and industrial technologies. This paper shows how soils are further discriminated by urbanization. Material and methods: A collection of observations and conclusions about the characteristics of natural and urban soils and the discrimination by interpretation of the particularities of discovered soils are presented. This view is enlarged by the specific features of the mode of human action and their effects on soil formation. Results and discussion: The effects of humans on soils in rural areas are numerous. They concern effects from soil use, evolving soil fertility and raw material supply by mining. Changes in soil formation occur due to natural factors in the landscapes. Discussions about the nature of soils in urban areas stress the situation of a low soil age, that they become relicts, are highly disturbed, contain artifacts, and are mostly byproducts of human decisions. Based on the specific urban climate, there are zonal soils. Perceptions about what qualifies these soils to be separately designated as urban soils are presented. The new view of the differentiation of artifacts by their origin from heat-treated products allows a more precise definition of soils in urban areas. Soil formation can occur quickly. Conclusion: Urban soil research is a wide field for discovering new modes of soil formation and the relationship between humans and soils. [ABSTRACT FROM AUTHOR]

Intensifying climate change and its impact on urban areas has led to the emergence of the climate urbanism model that advocates action at the intersection of urban development and climate change. The model has presented itself varyingly across geographies. While offering an opportunity for inclusionary development in some, in others it has exacerbated social injustice. In India, it echoes the flawed top-down, largely context-blind approach to urban planning, thus exacerbating urbanisation induced exclusions. Drawing on our involvement in the evolution of Community-based Climate Action Plans in Bhopal, India, the paper advocates for co-production as a pathway to inclusive climate urbanism. Given the ability of co-production to democratically improve services for and agency of the marginalised, the paper evidences the efficacy of co-production on (a) an equal acknowledgement of the strengths and limitations of the co-production partners, thereby facilitating negotiations on how and who would bridge these limitations; (b) negotiations that allow for the resolution of challenges and embedded power hierarchies between stakeholders; (c) the integration of climate and development actions as against a mere superimposition of the former onto development agendas; and; (d) the crucial role of decentralized local area engagement, one that affords the marginalized an agency to plan for themselves. [ABSTRACT FROM AUTHOR]

Climate stresses like droughts amplify economic precarity, pushing low-income, caste-oppressed communities across India to pursue temporary rural-urban migration as an adaptation strategy. This paper develops an intersectional framework on 'migrating injustices' to examine how caste and regional identity mediate interstate rural-urban migrants' experiences of vulnerabilities and injustices, particularly as these experiences move and endure with migration, shaping migrants' adaptive abilities. The framework is applied to trace the injustices that a group of 800-odd drought-impacted, caste-oppressed, landless persons from central India experience through their adverse economic incorporation as sanitation workers in Tiruppur's privatized sanitation sector in southern India. Findings reveal that a history of caste oppression and uneven development in their home region produced unequal drought impacts for our informants, pushing them to migrate. However, migrants' caste and regional identity combine with their climate and economic precarity to direct their migration into precarious, unjust working conditions and employer-provided, environmentally risky accommodations—both removed from local socio-political networks, undermining migrants' ability to contest injustices in Tiruppur. In highlighting the translocal and trans-sectoral intersections between the migrating environmental, economic, and caste-based injustices for circular migrants, the paper argues that eliminating migrating injustices is crucial for achieving transformative adaptation and urban climate justice. [ABSTRACT FROM AUTHOR]

This study began from the assumption that community self-organization, characterized by independent action without external control, could be a suitable approach to developing green infrastructure and strengthening climate resilience in urban neighborhoods in Serbia. The study employed a mixed methods approach to verify this assumption, combining a technical case study and citizen survey analysis. Technical simulations demonstrated that self-organized community interventions on green infrastructure could contribute to climate resilience, even in neighborhoods with unfavorable conditions. However, the survey uncovered significant social constraints that cannot be resolved within the community, including a perceived lack of internal capacity; belief in the primacy of external actors; moderate cohesion level; lack of community platforms; limited understanding of the interconnections between resilience, climate change, and the role of green infrastructure; limited environmental literacy; and unclear collective action benefits. Based on these findings, the study proposed a multi-level and multi-phase model for improving neighborhood green infrastructure. The model emphasizes participatory citizen collaboration and applies to the current context of Serbian urban neighborhoods. [ABSTRACT FROM AUTHOR]

Urban woodland composition and configuration have strong associations with land surface temperatures (LST), but the evidence is contradictory due to different spatial scales, regional climate zones, woodland types and urban contexts. In this study, we analyse associations between urban woodland and LST within and between five cities in different Köppen climate zones. Our consistent methodology is framed around local climate zones and conducted at a fine spatial scale. We find that urban woodland fragmentation, connectedness, and shape complexity all influence LST, though much less than overall cover. The importance of cover holds for all climates except for hot-desert (Cairo). Otherwise, every 1% increase in woodland cover corresponds to a reduction of LST of around 0.07 °C to 0.02 °C (London-Cfb > Toronto-Dfa > Nanjing-Cfa > Shenyang-Dwa). Within cities, increasing urban woodland cover generally reduces LST more in built-up compared to vegetated zones. Nevertheless, associations between local LST and urban woodland composition and configuration are highly heterogeneous across cities, especially in cooler climates. Thus, to unravel the complexities of urban woodland cooling, systematic analysis of contemporaneous local and regional factors is required. [ABSTRACT FROM AUTHOR]

Urbanization have been significantly reshaping the form of urban areas and natural landscapes, leading to complex urban morphologies. In 2012, the Local Climate Zone (LCZ) classification was proposed to address this issue and has since been widely adopted in urban climate studies globally. Despite its prevalence, literature on dynamic mapping of urban morphology remains sparse, making it difficult to delve into the study of urban renewal year by year. In this study, we compared different training scales, producing dynamic mappings of urban morphology with a spatial resolution of 100 meters spanning from 2000 to 2022 in major Chinese cities, based on the LCZ scheme. The results demonstrate strong inter-year consistency, and the accuracy of urban morphology change mapping is overall higher than 70%. Additionally, our results exhibit good alignment with other LCZ mapping datasets, more suitable for the current development situation in China, and effectively discriminate between building heights and densities across different LCZ types. This dataset holds significant potential for enhancing urban morphology monitoring and advancing urban climate research. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) tools based on Machine learning (ML) have demonstrated their potential in modeling climate-related phenomena. However, their application to quantifying greenhouse gas emissions in cities remains under-researched. Here, we introduce a ML-based bottom-up framework to predict hourly CO2 emissions from vehicular traffic at fine spatial resolution (30 × 30 m). Using data-driven algorithms, traffic counts, spatio-temporal features, and meteorological data, our model predicted hourly traffic flow, average speed, and CO2 emissions for passenger cars (PC) and heavy-duty trucks (HDT) at the street scale in Berlin. Even with limited traffic information, the model effectively generalized to new road segments. For PC, the Relative Mean Difference (RMD) was +16% on average. For HDT, RMD was 19% for traffic flow and 2.6% for average speed. We modeled seven years of hourly CO2 emissions from 2015 to 2022 and identified major highways as hotspots for PC emissions, with peak values reaching 1.639 kgCO2 m−2 d−1. We also analyzed the impact of COVID-19 lockdown and individual policy stringency on traffic CO2 emissions. During the lockdown period (March 15 to 1 June 2020), weekend emissions dropped substantially by 25% (−18.3 tCO2 day−1), with stay-at-home requirements, workplace closures, and school closures contributing significantly to this reduction. The continuation of these measures resulted in sustained reductions in traffic flow and CO2 emissions throughout 2020 and 2022. These results highlight the effectiveness of ML models in quantifying vehicle traffic CO2 emissions at a high spatial resolution. Our ML-based bottom-up approach offers a useful tool for urban climate research, especially in areas lacking detailed CO2 emissions data. [ABSTRACT FROM AUTHOR]

The Yangtze River Midstream Urban Agglomeration is a pivotal economic zone in China. Over the past 35 years, climate change has increased ecosystem vulnerability and instability in this area. Therefore, assessing landscape ecological risk (LER) is vital for this region. This study analyzed LER variations from 1985 to 2020 using statistical methods and landscape indices, and forecasted LER changes from 2025 to 2050 with multi-model meteorological data. Results show the following: (1) LER indices have decreased over the last 35 years, with a notable shift from high-risk to medium-risk areas. High LER areas remained concentrated in low-elevation, densely urbanized central regions, indicating severe ecological damage compared to high-elevation areas. (2) Climate change significantly impacts the LER index, with temperature exerting a stronger influence than precipitation. Overall, LER decreases with rising temperatures and precipitation, though some cities exhibit opposite trends. (3) Future multi-Shared Socioeconomic Pathway scenarios suggest a general decrease in LER with increased temperature and decreased precipitation, predominantly resulting in low- and medium-risk zones. High LER areas will persist in low-elevation zones, particularly in the southern and central plains. Given the high LER in the plains, the timely governmental regulation and control measures are essential for ensuring ecologically sustainable development. [ABSTRACT FROM AUTHOR]

Cities have become increasingly vocal in addressing climate change, crafting climate mitigation strategies, and committing to ambitious emission reductions. Previous studies found no evidence that ambitious targets, analyzed as a single factor, translate into actual emission reductions in cities. Yet, is this still the case if ambitious targets are analyzed in combination with other institutional and socioeconomic factors? We carry out a fuzzy-set Qualitative Comparative Analysis of all cities reporting their emissions to the Carbon Disclosure Project (CDP) where data are available for at least four years between 2000 and 2020. The analysis tests whether ambitious emission reduction targets, in conjunction with size, affluence, and favourable domestic enabling conditions are systematically associated with substantial emission reductions. Results show different configurations leading to emission reductions. In some configurations, ambitious targets are redundant or counterproductive. In other configurations, ambitious targets are necessary to achieve emission reductions. These results call for greater attention to cities' heterogeneity when studying urban climate governance. Key policy insights Three configurations seem systematically associated with downward emission trends: being large and affluent; being small and without ambitious emission reduction targets; and being large, with ambitious emission reduction targets but without favourable domestic enabling conditions. Ambitious emission targets and favourable conditions at the national level seem redundant for emission reductions in cities that are both large and affluent. These cities seem to achieve emission reductions regardless of the presence of ambitious targets. Small cities need to cooperate with other actors to reduce emissions and therefore need to be pragmatic and strategic in setting their targets. Large cities may need to set ambitious targets to exploit the benefits of their size for emission reduction. This seems to be necessary when they are lacking favourable conditions at the national level. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses articles in the issue on topics including urban climate finance, private financialization, and the roles of cities in managing climate vulnerabilities and justice.

Many urban areas are characterized by both a growing population and an intensification of summer heat events in the context of climate change. Thus, more and more people are exposed to heat stress and corresponding health consequences. Measures for climate change adaptation, such as unsealing strategies, are needed in the existing urban fabric to reduce sensible heat flux by increasing latent heat flux to cool down the urban environment without requiring additional space or changing the basic function of the area. Unsealing measures like grass grid pavers (GGPs) can also help to reduce flooding risks due to increased infiltration and water storage capacities. Up to now, a parameterization of GGPs for microclimatic simulations is not available. To fill this research gap, we present a new GGP model parameterization developed for the fluid dynamics microclimate ENVI-met model based on field measurements with double-ring infiltrometers, etc., which can also be implemented in other microscale models in the field of urban climatology. To analyse the microclimatic effects and the cooling potential of this GGP parameterization, scenario analyses were performed using a validated ENVI-met model setup for an urban high-density study area in Cologne, Germany. An extreme scenario was designed to address the maximum cooling potential of the GGPs in comparison to the dominant sealed asphalt surfaces in the study area, along with a more realistic scenario with a usage-compatible installation of GGPs in the model domain only in side streets and inner courtyards while main streets remain sealed. We found a maximum cooling potential of up to -20.1 K for ground surface temperature and up to -7.1 K for air temperature at 1 m above ground level for the hottest hour of a simulated 3 d heat wave in summer 2022, which represents a 20-year heat event in Cologne. On spatial average, a decrease of up to -11.1 K for surface temperature and up to -2.9 K for air temperature was determined. On temporal average for the 3 d heat event, statistically significant mean temperature differences of -5.8 K for surface temperature and -1.1 K for air temperature were simulated. Cooling effects are more pronounced during the daytime for surface temperature, especially in unshaded areas, while cooling effects for air temperature are strongest during the nighttime. Model results also show that the entire air volume in the study area is cooled down due to this adaptation measure, even in areas of the domain where no surfaces have been unsealed in the scenario design. The more realistic GGP scenario shows cooling effects of a comparable magnitude to the extreme GGP scenario. Thus, even partial GGP unsealing is an effective adaptation measure for reducing extreme temperatures in cities if water availability is not limited. [ABSTRACT FROM AUTHOR]

This paper explores the personal experiences of Sandra Kaire as a postdoctoral researcher who worked with Margaret Somerville in relation to methodological approaches. The ideas were initially developed in consultation when Sandra worked together with Margaret at Western Sydney University, Australia. The paper explores Sandra's personal commitments and intimate engagement with young climate activists, and the development of visual, place-based and posthuman methodological approaches in relation to Sandra's postdoctoral research with young climate activists in three European counties. It includes photographs of their activist environments, observations, and transcripts of in-depth interviews. The paper reveals the significance of climate activism experiences of young people to their educational concerns, and the relationships of the locations of their activism to experiences of becoming more than itself by adding new ways of thinking about human and more-than-human relation. [ABSTRACT FROM AUTHOR]

As living beings that change with the seasons, urban trees both indicate and structure time in the city. They do so through their own agency and life cycles and through the care and management they require in urban environments where they often stand in conflict with other land uses. In temperate zones, urban trees have been planted for centuries especially to provide cooling shade in hot summers. Their changeable and seasonal aesthetic effects, including their color, form, sound, and smell, have not only fostered individual urban identities but also impacted human psychological wellbeing. Focusing on cities in the temperate climate zone in the nineteenth and twentieth centuries, this article shows how some tree species were bred and introduced in urban areas to create seasonal effects, while others were eliminated or manipulated to avoid unwanted seasonal characteristics. Scientists and citizen scientists from an early time onwards considered urban trees as indicators of seasonal variations of the urban climate and of the urban heat island effect. Urban tree phenology transgressed the popular Western notion of the four seasons by offering a more nuanced structure of the year. It also revealed variations of seasonal change as climate change, and it anticipated that the trees growing in our future cities will likely be of different species. [ABSTRACT FROM AUTHOR]

In this article we present three instruments: (1) a social vulnerability to extreme heat index to identify the areas of a city (and populations thereof) more vulnerable to extreme heat due to climate change (heat islands); (2) a new overall fragility index that incorporates social vulnerability to extreme heat as well as socioeconomic indicators; and (3) a climate shelter index (CSI) to identify areas within a city that can provide relief from extreme heat based on green and blue solutions. We elaborated these three indexes to measure social vulnerability to extreme heat in the municipality of Bologna, which serves as this article's case study. By analyzing the connections between social vulnerability to extreme heat and several socio-demographic variables in Bologna, we found that a decrease in income is significantly correlated with an increase in social vulnerability to extreme heat in urban contexts. A comparison between our new overall fragility index and the existing index adopted by the municipality of Bologna (Indice di fragilità, Comune di Bologna) showed that about 75% of the statistical areas observed are worse off when social vulnerability to extreme heat is also considered. Considering social vulnerability to extreme heat shows vulnerabilities in a city (here: Bologna) that the pre-existing index did not consider. These findings and our new indexes can support the Bologna administration (and other local administrations) in addressing the consequences of climate change for their most vulnerable residents. [ABSTRACT FROM AUTHOR]

The thermal and optical behavior of different elements in the urban environment is critical for urban climate regulation and planning. This study investigates the micrometeorological conditions prevailing in an urban green space (UGS) in Greece, during the heatwave of July 2023, addressing the effects of various surface materials on thermal dynamics and the urban heat island (UHI) phenomenon. The research is based on ground surface temperature and albedo measurements on different materials in the UGS, in the morning and at noon, showing great temperature differences between the different types of materials. The findings highlight the complex interaction between high-albedo surfaces and surface temperature values, suggesting that the proper selection of materials can highly affect the optical and thermal behavior of the urban environment. Artificial materials absorb more heat compared to natural vegetation, leading to high surface temperature values, reaching at noon, for example, 58.9 °C for asphalt. For the natural surfaces, dry bare soil presents similar thermal behavior (64.1 °C at noon), while green surfaces had much lower temperatures (e.g., 38.3 °C for grass). Thermal comfort indices revealed that July 2023 experienced extensive "very hot" conditions, imposing the urgent need for strategic urban planning to mitigate heat impacts. The study highlights that in order to create climate-resilient environments and improve thermal comfort, it is crucial to include suitable materials and a variety of vegetation in urban design. Such insights into the complex nature of urban microclimate indicates also the issue of the careful selection of materials and plant species in urban greening initiatives to help cities face the UHI phenomenon. [ABSTRACT FROM AUTHOR]

As climate change intensifies with more frequent and severe flood events, urban areas face increasing challenges to protect population wellbeing. Amid urban development challenges, political uncertainty, and socioeconomic pressures, finding sustainable solutions to enhance urban resilience has become urgent and complex. This article explores the limitations of traditional drainage systems in an urban zone of Bucharest, Romania, and the integration of nature-based solutions for flood mitigation. We compare the existing situation with those simulated in a climate change scenario before and after implementing green solutions. The imperviousness of parking lots was set at 60%, that of green roofs at 65%, and that of parking lots at 85%. A hydraulic model was used for this purpose. The results demonstrate that the current stormwater systems struggle to meet the demands of increasing rainfall intensity and highlight how sustainable strategies can effectively address extreme weather challenges while contributing to the restoration of natural environments within the city. In the case of using 'gray' solutions, only 10–20% of the area affected by floods is reduced. In comparison, a combination of gray and green infrastructure achieved an average reduction in peak water levels of 0.76 m. [ABSTRACT FROM AUTHOR]

• Combined climate change and UHI model improves urban microclimate accuracy. • Projected cooling demand increases in Chinese cities due to GCC and UHI. • FTMY data reveals varying climate impact on heating and cooling energy loads. • UHI-influenced models better predict future urban temperature and humidity trends. While global climate change (GCC) and urban heat island (UHI) effects have been extensively studied separately, their combined impact on urban microclimates remains poorly understood. This study introduces a novel lightweight simulation procedure that effectively integrates these two phenomena to assess their combined impact on urban microclimates in three representative Chinese megacities. By validating the selected global climate model (GCM) using observed weather data from the last decade, we demonstrate that incorporating UHI effects into future urban weather condition downscaling significantly improves prediction accuracy for local air temperature, reducing MRMSE by 0.56 °C, 0.34 °C, and 0.11 °C (17.09 %, 15.15 %, and 4.30 %) in Beijing, Shenzhen, and Shanghai. Moreover, simulation of prototypical building models in urban areas based on future representative year (FRY) weather data reveals that the changes in future cooling energy consumption range from −3.26 % to 100.24 % compared to cooling energy consumption under TMY with most positive change ratios. The trend of heating energy consumption can vary in cities from −98.68 % to 15.78 %. This integrated approach advances the understanding of combined impacts of climate change and heat island on urban environments and provides a computationally efficient framework for future urban climate predictions. [ABSTRACT FROM AUTHOR]

• The influence of sea breezes in heat stress was analyzed in Lisbon (Portugal). • Wind fields were modelled using GRAMM-SCI mesoscale model with ERA5 reanalysis. • UTCI was calculated for 80 weather stations on sea breeze and N/NW wind days. • Sea breezes in Lisbon can reduce heat stress (UTCI) by 5 °C during heatwaves. • Wind corridors proved crucial for climate resilient urban planning and design. In this study, the influence of the Tagus river and Atlantic Ocean breezes in a Mediterranean city (Lisbon) on outdoor thermal comfort was analyzed during the thermal summer days. Hourly wind fields were modeled using the GRAMM-SCI software, initialized with ERA5 reanalysis data. The Universal Thermal Climate Index (UTCI) was calculated for 80 weather stations across the city. Additionally, the UTCI anomalies (ΔUTCI) relative to a reference site (the airport weather station) were calculated during breeze and non-breeze events (typically N/NW winds). Results showed that sea breezes can reduce UTCI levels by up to 2,2° C during typical breeze days and up to 5,1° C during heatwave breeze events. The effect of these breezes on heat stress conditions is felt up to 4 km from the Tagus river, but especially on the areas up to 500 m. However, in 50 % of the cases where no thermal stress was recorded at the airport during breeze events, Lisbon's riverfront can be more uncomfortable (moderate heat stress) than the northern part of the city (from 2 to 8 km). Additionally, cooling effect of the breezes is only significant enough to cause a transition to a more comfortable UTCI class (especially from very strong to strong heat stress and from strong to moderate heat stress) during heatwaves (strong to very strong heat stress) and on areas up to 1,5 km from the estuary. The promotion of wind corridors is, therefore, crucial to insuring the progression of sea breezes in urban areas and to reduce heat stress. [Display omitted] [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) acquired from polar orbiters serves as a critical dataset for investigating daily clear-sky climatology of surface urban heat islands (SUHIs). However, these orbiters only capture instantaneous LSTs at specific overpass times within a diurnal cycle, thus limiting the analysis of temporally sensitive SUHI metrics such as daily mean, maximum, and minimum SUHI intensity (SUHII). Consequently, the representativeness of daily clear-sky SUHI climatology derived from these instantaneous LSTs remains unclear, especially across global cities. Here we employ Aqua & Terra MODIS LST data alongside a well-established diurnal temperature cycle (DTC) model to assess such representativeness of daily clear-sky SUHI climatology, primarily based on the SUHII biases estimated from DTC-derived diurnally continuous and satellite-based temporally discrete LSTs across global cities. This approach discloses the fidelity of satellite-derived instantaneous LSTs to in representing daily clear-sky SUHI climatology. We further dissect the drivers of SUHII biases using the LightGBM model and SHAP algorithm. Our results reveal substantial underestimation of daily mean and maximum SUHIIs alongside overestimation of minimum SUHII when compared to the estimates directly based on instantaneous LSTs. The annual global mean SUHII biases for daily mean, maximum, and minimum conditions are 0.21 ± 0.13 K, 0.51 ± 0.18 K, and − 0.43 ± 0.17 K, respectively. We observe substantial seasonal and geographic variability in SUHII biases, with greater SUHII biases during winter, in snow climates, and across Europe and Oceania when compared to other seasons, climates, and continents. Notably, background climate is the principal contributor (34 %) to variation in SUHII bias, followed by surface properties (28 %), urban metrics (20 %), and human activity (18 %). Our findings emphasize the importance and show the feasibility of correcting SUHII biases in daily clear-sky SUHI climatology derived from instantaneous LSTs from polar orbiters across global cities. • Instantaneous LST induces bias in daily mean, maximum and minimum SUHI. • Instantaneous LST-induced SUHI bias increases with latitude. • SUHI bias varies with season and geographical region. • SUHI bias is influenced by background climates and surface properties. [ABSTRACT FROM AUTHOR]

Cities in Europe are facing significant challenges as rising temperatures exacerbate health risks, energy consumption, and environmental degradation. This study investigates the multiple features affecting land surface temperature (LST) across 780 European cities, categorized into eight macro-regions. The methodology involved utilizing SHAP values to interpret the results of a Random Forest model that evaluated the impact of individual urban features on LST. Additionally, the Generalized Additive Model (GAM) was employed to explore non-linear relationships between key urban features and LST, offering a deeper understanding of how specific features influence urban temperatures. The results highlight the important cooling effects of ecological attributes like tree height and evapotranspiration, particularly in warm regions (e.g., the Iberian Peninsula and Turkey). Conversely, urban structure elements like built-up volume were shown to increase LST. GAM analysis further revealed non-linear relationships, such as the diminishing returns of evapotranspiration (ET) on cooling in the Iberian Peninsula, where LST decreased sharply at ET < 3–4.5 mm/d but plateaued at higher values. These results emphasize the complexity of urban climate regulation, where ecological and built environments interact in diverse ways across different regions. This study underscores the importance of region-specific urban planning strategies that integrate both ecological and urban structure features to effectively mitigate the effects of urban heat. The findings offer actionable insights for policymakers seeking to enhance urban climate resilience and manage the growing challenges posed by global warming in European cities. [ABSTRACT FROM AUTHOR]

Urban greening initiatives are increasingly integrated into urban planning and climate change strategies, offering multiple benefits to residents. Despite growing advocacy for incorporating diverse stakeholder preferences and concerns into policy-making processes, there remains a knowledge gap about differences in perceptions between decision-makers and other stakeholders. Using Canberra, Australia, as a case study, this research explores the perceptions of policymakers and key stakeholder groups regarding urban forest ecosystem services (ESs), ecosystem disservices (EDs), and associated trade-offs through analysing legislative instruments, policy documents, and stakeholder submissions. The findings reveal that while legislation exhibits a high level of cohesiveness, policy documents and stakeholder submissions prioritise ESs variably. Notwithstanding generally shared acknowledgement of the urban forest's importance, perceptions of key stakeholder groups about ESs, EDs and trade-offs differed, and from those of policymakers, in some key respects. The disparities are mainly attributed to the different scales at which urban forests are perceived, and divergent value orientations among stakeholders. Results suggest that policymakers can draw from the differing perceptions to inform decisions at both city-wide and neighbourhood levels. However, making informed choices about trade-offs requires careful consideration of multiple policy objectives and the diverse values of stakeholders. More proactive engagement with key stakeholders in the policy-making process could foster a consistent and comprehensive understanding of, and response to, these diverse perceptions. These results from Canberra underscore the benefits of more inclusive approaches to the development of urban greening strategies, to achieve more socially-sustainable outcomes. • Policymakers and stakeholders view urban forest ESs and EDs at different scales. • Perceived trade-offs stem from biophysical interactions between ESs and EDs. • Divergent beliefs and prioritised values lead to implicit trade-offs. • Proactive engagement with stakeholders helps achieve multiple policy objectives. [ABSTRACT FROM AUTHOR]

• The angles obtained by dividing 360° by n yield distinct patterns in SVF estimate. • Rotation angles that are factors of 90° shall be preferred in a vector-based method. • Finite canyons with L/W ratio greater than 57.29 behave as infinitely-long canyons. Methods for calculating the sky-view factor (SVF) range from analytical approaches to automatic extraction from satellite images and street views. An appropriate method may be selected based on the canyon's length, symmetry, and complexity. For a yet-to-develop urban environment, a vector-based method is preferred in which selecting a suitable rotation angle and search radius is important. Researchers have used rotation angles ranging from 1° to 22.5° in the absence of standardization. This study explores the relationship between the SVF and rotation angle in an idealized urban canyon. The SVF function of the Shadow library of R is used with rotation angles varying from 0.001° to 30°. The result indicates that when the rotation angle is not a factor of 90°, lowering its value may not increase the accuracy of the SVF estimate. The rotation angles and canyon's length-to-width ratios together affect the SVF. A finite canyon with a length-to-width ratio of 57.29 behaves as infinitely long when measured using a rotation angle of 1°. A distinct pattern of change in the SVF estimate is observed at angles obtained by dividing 360° by n, where n is an integer. The study also reports the impact of variations in canyon dimensions on SVF. An increase in canyon length with constant height and width results in the occurrence of breakpoints with decreasing step widths. The canyon height is not related to the rotation angle. A rotation angle that is a factor of 90° shall be preferred in the vector-based method of SVF calculations. [ABSTRACT FROM AUTHOR]

The reliability of land surface phenology (LSP) derived from satellite remote sensing is crucial for obtaining accurate estimates of the phenological response of vegetation to future climate change in urban ecosystems. Differences in phenological definition and extraction methodology using remote sensing can generate systemic errors in estimating the phenological temperature sensitivity to predict the biological response of vegetation. Here, we evaluated the start of the season (SOS), the end of the season (EOS), and the growing season length (GSL) between the Terra and Aqua combined Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Dynamics (MCD12Q2) and the Suomi National Polar-Orbiting Partnership NASA Visible Infrared Imaging Radiometer Suite (VIIRS) Land Cover Dynamics (VNP22Q2) over 1470 urban clusters worldwide. We found that the availability of valid phenological estimation in urban areas was low across climates, and cities without valid phenological data were mainly distributed in tropical and dry climate zones. The phenological comparison for each urban cluster worldwide revealed significant differences between the two products. For SOS, EOS, and GSL, only a small proportion of urban clusters (6.01%, 3.54%, and 1.50%, respectively) exhibited R 2 surpassing 0.5, and approximately 31.63%, 37.37%, and 58.57% showed the RMSD values exceeding two weeks. These results remained consistent and robust across various climates, latitudinal gradients, and valid pixel proportions. We also revealed discrepancies in the inter-annual variability of LSP between the two products. These disparities in phenological metrics directly lead to systematic uncertainties in the measurements of temperature sensitivity. Furthermore, urban phenology disparities primarily arise from the different phenology definitions employed by the two products. Standardizing the definition of phenology metrics extraction from remote sensing data is crucial to the comprehensive understanding of phenological responses to urban environments, which thus provides referencing resources for studying how future environmental changes affect vegetation in natural ecosystems. • We assessed the MCD12Q2 and VNP22Q2 phenology products in urban areas worldwide. • The availability of valid phenology data in urban areas is low across climates. • The MCD12Q2 and VNP22Q2 products show notable disparities in urban phenology. • Phenological discrepancy leads to systematic biases in temperature sensitivity. • Standardizing the definition of phenology metrics extraction is crucial. [ABSTRACT FROM AUTHOR]

• Models used to predict future changes in precipitation and temperature are reviewed. • Studies are categorized based on the type and geographic focus of climate models. • Neural networks and Coupled Model Intercomparison Project Phase 6 are dominant. • Most models indicate significant changes in precipitation and temperature across cities. • These changes could intensify the frequency of heatwaves, floods, and droughts. Climate change is increasingly putting pressure on urban areas and threatening millions of lives. In this context, diverse climate models have been utilized to predict future changes in precipitation and temperature. Despite substantial research in this field, there is a lack of review of various climate models, their predictive reliability, and their geographical focus. This systematic review, covering studies from 2009 to 2023, examines the use of different climate models to predict precipitation and temperature trends in cities. We organized the studies into two primary categories based on the type and geographic focus of climate models used in this field. Using deductive content analysis, our analysis found that researchers used combined neural networks and Coupled Model Intercomparison Project Phase 6 (CMIP6) models the most. Time series models, particularly those optimized with machine learning, demonstrated superior accuracy compared to other models. Furthermore, scenarios such as SSP5–8.5 and RCP8.5 projected significant changes in precipitation and temperature across cities in Europe, North America, Asia, and the Gulf Cooperation Council (GCC) countries. These changes could intensify the frequency of heatwaves, floods, and droughts in urban areas by the end of the 21st century. A key gap in the existing literature is the insufficient analysis of small cities, indicating a promising area for future research. This study provides valuable insights for policymakers in climate change adaptation and urban resilience planning. [ABSTRACT FROM AUTHOR]

• Textual content analysis was conducted for 203 US urban climate actions plans. • The analysis was stratified by time, region of the US, and city size. • Plans generally placed a heavy emphasis on reducing emissions. • More recent plans were more holistic and mentioned equity more frequently. Cities within the United States (US) have sought to address climate change by creating local Climate Action Plans (CAPs). This paper explores the state of urban climate planning by performing a textual content analysis of CAPs for 203 cities in the US. Specifically, the analysis quantified the degree to which CAPs emphasized each component of the following three climate planning triads: 1) greenhouse gas emissions, urban heat, and urban flooding; 2) economic, environmental, and health aspects; and 3) mitigation, adaptation, and equity-based strategies. The study also evaluated how the balance across these three climate planning triads varied spatially, temporally, and based on city population. The findings indicated that CAPs overall placed a notable emphasis on reducing emissions while adapting to localized urban climate hazards featured less frequently. More recent plans were less emissions-centric and exhibited a greater focus on equity and health. Differences across city size were also observed, as larger cities produced CAPs that more frequently mentioned health and equity as well. Overall, the analysis highlighted that a broader holistic approach focused on addressing the full spectrum of local to global climate hazards is necessary to ensure CAPs effectively protect the future livability and economic vitality of urban centers. [ABSTRACT FROM AUTHOR]

• EWI is influenced by the interaction of spatiotemporal factors. • Low rise enclosed residential areas with high greening rate provide better EWI. • When walking less, spaces with concentrated outdoor activity facilities provide better EWI. • Cold-adapted design is beneficial to EWI in severely cold residential areas. Regular walking is instrumental in maintaining optimal immune function. Concurrently, climate and the urban environment exert significant influences on human immune health. However, the effectiveness of walking on the immune level in regions with distinct climatic conditions remains largely unexplored. This study amalgamated medical physiological detection techniques with questionnaire surveys and employed quantitative analysis of panel data to investigate the relationship between the built environment, season, and the effectiveness of walking on the immune level (EWI) within severely cold residential areas. The results indicate that: (1) Residents' walking helps alleviate depression and anxiety, and enhance sIgA levels, but varies in different seasons and residential areas; (2) Compared to summer and autumn, residents' immune level decrease in winter, and the impact of the built environment on immune level decreases between different residential areas; (3) Low rise enclosed residential area with high greening rate, provides better EWI. When walking less, residents gain sIgA advantages in spaces with centralized outdoor activity facilities. The study contributes to improving the theory of healthy urban space and provides strategic guidance for the planning and design of healthy residential areas in severely cold regions based on the perspective of human immune health. [ABSTRACT FROM AUTHOR]

[Display omitted] • Effects of building heat emission on microclimate at various scales were estimated. • Air temperature was increased by 1.0 °C (1.4 °C) in residential (mixed-use) blocks. • Lowering building density helps diffuse the building heat emission more effectively. • Building heat emission easily gather in mixed-use blocks with high floor area ratios. • The urban form renewal strategies from block to district scales were proposed. Air conditioning systems transfer heat from indoors to the outdoors during hot summers, exaggerating the urban heat island (UHI) phenomenon and the risks of relevant climatic disasters. Urban form renewal strategies are urgently required to improve the diffusion of building heat emissions (BHEs). In this study, the impacts of BHEs on the outdoor airflow and air temperature fields of 42 typical urban blocks in Xi'an were simulated using scSTREAM program. BHEs increased the near-surface UHI intensities by 1.0 °C and 1.4 °C in residential and mixed-use blocks, respectively. The diffusion conditions of the BHEs were better for blocks with lower building densities. However, the UHI increase caused by the BHEs was positively correlated with the floor area ratio only in the mixed-use blocks. Regression formulas describing the relationships between the UHI increase caused by the BHEs and block-form indicators were then proposed. In a district renewal project for urban climate optimization, these formulas were used as the fitness functions of genetic algorithm to determine the form indicators for all blocks therein. The district UHI intensity caused by BHEs could be reduced by 0.3 °C with the maintaining of the district development intensity. [ABSTRACT FROM AUTHOR]

• PV/T-assisted desiccant cooling system for high-temperature, humid urban climates. • Hyperplane optimization balance technical, environmental, energy, and economic goals. • Optimized system offers 22.9 kW cooling capacity, enhancing indoor load management. • Achieved 58 % energy savings and 62 % CO 2 reduction over traditional systems. • Economic analysis shows annual savings of 22,766 HKD, demonstrating cost efficiency. The escalating demand for sustainable air conditioning systems in buildings has catalyzed the development of green and efficient alternative air handling systems like the integrated photovoltaic/thermal regenerative desiccant cooling system (PV/T-DCS). This study investigates a multivariate optimization strategy for PV / T - D C S tailored for high-density, hot, and humid urban environments. By integrating a photovoltaic/thermal (P V / T) system with a liquid desiccant-based evaporative cooling system, the objectives of the proposed system configuration and optimization scheme include maximizing cooling capacity, minimizing energy consumption, and reducing emissions. This research executes multivariate hyperplane optimization to balance technical, environmental, energy, and financial goals by employing a novel operational strategy tested under local climatic conditions. Results indicate that the PV/T-DCS system achieves a 58.1 % reduction in energy consumption and a 61 % decrease in carbon dioxide (C O 2) emissions compared to conventional systems, providing an effective cooling capacity of 22.9 kW and generating annual savings of 22,766 HKD. This optimization framework may underscore the potential of integrating renewable energy in desiccant air conditioning systems and adapting these technologies to bolster sustainability in air handling systems. [ABSTRACT FROM AUTHOR]

Research on municipal sustainability and climate policy has grown substantially in recent years and has been shaped by the choices and efforts of local policymakers and administrators. Recent research has looked at the framing and language used by policymakers and city administrators as they draft and implement climate change policy. This project provides additional information about the evolution of climate change language in municipal government to understand how municipal actors conceptualize the work they do and the language they use. This project analyzes content from city plans along with elite interviews of municipal staff to capture the ways that communities tap into environmental policymaking and how their understanding of these ideas has changed over time. Relying on data from Western United States and Southern United States cities, this research examines the usage of the dialogues surrounding sustainability, climate mitigation and adaptation, and resilience by municipal governments. It demonstrates that there have been changes in how municipal governments think about different climate change concepts, as well as further insights about sustainability framing links to implementation efforts by municipalities. • Sustainability remains the most popular way to frame climate change issues. • Resilience is becoming more popular for broad usage by local governments. • Cities' use of language has not fully overcome implementation challenges. [ABSTRACT FROM AUTHOR]

• Climate policy uncertainty (CPU) increases the level of urban climate risk (UCR). • A theoretical analysis framework is provided for the impact of CPU on UCR. • CPU increases UCR through the industrial structure of the production sector. • Household sector behaviors intensify the positive correlation between CPU and UCR. • CPU has a greater impact on UCR in capital cities than in non-capital cities. Climate change mitigation policies must be developed to reduce the adverse effects of escalating climate risks. However, climate policies are fraught with uncertainty, which leads to unavoidable negative consequences. It is crucial to understand the impact of climate policy uncertainty on urban climate risk. This study uses the entropy weighting method to measure the urban climate risk of 274 prefectural-level cities in China from 2011 to 2021 based on the Hazard-Exposure-Vulnerability (H-E-V) adaptation decision-making framework, and employs the two-way fixed effects model to investigate the impact of climate policy uncertainty on urban climate risk through multidimensional empirical tests. The results indicate that: (1) Climate policy uncertainty significantly increases the level of urban climate risk. (2) Climate policy uncertainty affects the level of urban climate risk through the industrial structure of the production sector. (3) Deposit balances and insurance cost of the household sector exacerbate the positive relationship between climate policy uncertainty and urban climate risk. (4) Climate policy uncertainty has a greater impact on climate risk in capital cities than in non-capital cities and it has the most significant impact in cities with medium level of climate risk. The findings offer new perspectives on reducing urban climate change risk and achieving sustainable development. [ABSTRACT FROM AUTHOR]

• Built the first annual Urban Canopy Parameters (UCPs) in Beijing during 1985–2023. • The region between the 4th and 6th rings had significant changes in UCPs. • Urbanization induced a mean temperature increase of 3.10 °C at nighttime. • 3D morphology significantly impacted raising nighttime temperatures. Urban morphology, characterized by Urban Canopy Parameters (UCPs), significantly impacts the local climate. Presently, due to the absence of long-term UCP datasets, their effects on urban climate modeling have not been comprehensively analyzed, especially under a complicated urbanization background. Therefore, we developed a dataset of UCPs for Beijing from 1985 to 2023 annually to characterize the spatial heterogeneity of urban land surface induced by urbanization. After that, using the Weather Research and Forecasting coupled with single layer urban canopy model (WRF-SLUCM), we conducted high-resolution numerical simulations with different UCPs under diverse urbanization stages (UCP1985 and UCP2023) in Beijing during a heatwave episode in June 2023. The developed UCPs indicated significant spatiotemporal changes in Beijing's urban morphology, particularly in the suburbs. Different UCPs were associated with distinct trends, while there were strong positive correlations between UCPs with similar physical meanings. Additionally, the simulation results of the WRF model with UCP2023 showed that the average temperature increased 3.10 °C at nighttime, compared to UCP1985. Meanwhile, the phenomenon of warming in suburban was more significant than in urban and rural regions. The variations in 3D characteristics of urban morphology played an important role in clearly depicting the urban thermal environment. Our results offer a new approach to revealing the nexus between urban morphology and urban climate, showing great potential to promote sustainable urban planning and management. [ABSTRACT FROM AUTHOR]

• Reviews advancements in UBEM assessing indoor overheating in urban contexts. • Summarises methodologies integrating UHI and climate change into BPS. • Identifies the need for adaptive thermal comfort criteria for changing climate. • Emphasizes integrating enriched models into UBEM for reliable indoor assessment. • Proposes research direction for urban climate resilience. Urban microclimate often exhibits higher temperatures than rural areas due to Urban Heat Island (UHI). While UHI reflects outdoor overheating risks, people spend most of their time indoors, where thermal conditions are shaped by complex interactions of external weather, building characteristics, and human behaviour. Thus, UHI effects do not straightforwardly translate to indoor temperature increases. The intricacies of assessing indoor overheating risks in urban settings can be captured using Urban Building Energy Modelling (UBEM) with Building Performance Simulation (BPS). This paper reviews the latest research on BPS in an urban context, offering critical analysis and identifying challenges to guide future research. Conventionally, UBEM has primarily focused on energy simulation rather than indoor thermal comfort. Also, it often uses a generic approach, modelling a limited set of archetype buildings. This simplification can result in suboptimal climate adaptation and mitigation strategies, as indoor thermal comfort depends on specific building conditions like microclimate, window size, orientation, and shading. However, recent advances in UBEM have begun incorporating microclimate data and climate change scenarios. New thermal comfort metrics tailored to climate change are emerging, building upon existing standards. Progress has been made in modelling significant factors affecting building heat balance, including the generation of multizone models, incorporation of trees and vegetation, and improved calculations of longwave radiation and surface albedo. Despite this progress, current research often focuses on a limited set of these factors and the relative contribution to the UBEM model outcomes is often not quantified. Future endeavours should strive for explicit quantification and efficient integration of these interacting elements within urban models, balancing the modelling complexity with computational demands while improving simulation accuracy. Limitations may arise from data availability, necessitating the adoption of alternative data sources and novel modelling techniques. Ultimately, enhanced assessments of urban-level indoor overheating will support better-informed urban planning and building design strategies, bolstering resilience against overheating. [ABSTRACT FROM AUTHOR]

• Thermal comfort evaluation in varied micro - environments with distinct green blue features in a humid subtropical city is conducted. • PET values at studied sites are higher than the defined comfort limit (26 – 30 °C). • Tree species vary in their cooling effect, exposure, micro- environments and tree attributes (e.g., canopy diameter, leaf area index and tree height) are significant determinants of OTC conditions prevalent. • Improvement of ΔT and OTC levels depends on the characteristics and synergistic combination of the green-blue features. Urban environments in humid subtropical climates, like Roorkee, India, often experience high summer temperatures and uncomfortable outdoor thermal conditions. However, the impact of green-blue landscape configurations on cooling intensity (ΔT a) and outdoor thermal comfort (OTC) is insufficiently understood. This study, conducted in Roorkee during summer 2022, assessed the effect of green-blue landscape configurations on OTC using Physiological Equivalent Temperature (PET) and mean radiant temperature (T mrt). PET values ranged from slight to extreme heat stress, with statistically significant differences in cooling intensity across varied landscape configurations. Tree shaded canal front locations registered lower T a (1.6 °C) and T mrt (9.3 °C). Similarly, shaded canal front locations registered 4.7 °C lower PET than sun-exposed locations Different tree species showed significant variation in ΔT a , with the highest cooling effect (maximum average ΔT a of 3 °C) occurring during the morning hours. Notably Morus alba and Mangifera indica yielded the highest cooling effect, outperforming artificial and mixed shade. Eucalyptus alba , on the other hand, registered adverse comfort conditions. Denser tree canopies and attributes - canopy diameter, leaf area index, and height were strongly correlated to improved cooling performance. Additionally, increasing pervious surfaces and tree cover within 100 m of intervention areas enhanced cooling, with significant effects noted within a 25–50 m radius. This study highlights the role of green-blue infrastructure, particularly the combination of low sky-view factor (SVF), dense canopy trees, and proximity to water, in reducing heat stress. These findings offer crucial insights for climate-responsive open space design, particularly in Indian cities facing rampant urbanization and global warming. [ABSTRACT FROM AUTHOR]

Urban human thermal stress can be inaccurately estimated along with less-understood heat heterogeneity due to the absence of high-resolution meteorological information and realistic human behavior representation. To this end, we coupled a regional climate model (weather research and forecasting model, WRF) and a human energy balance model (human-environment adaptive thermal stress model, HEATS) to predict pedestrian's dynamic thermal stress at a neighborhood scale. The WRF-human coupling system resolves human-environment heat exchanges based on meteorological and topographical information with the consideration of dynamic human activities. The coupling system has been tested and utilized to study dynamic heat stress in a typical hot, humid, and mountainous city, Hong Kong. Our results revealed widespread heat heterogeneity with up to 7 °C difference in Physiological Subjective Temperature (PST) in the core urban area, and extreme heat exposure (up to 45 °C PST) in calm-wind zones at noon. Heat stress can be further aggregated considering realistic human behaviors such as extra clothing (e.g., protective facemask during pandemics) and physical exercise (e.g., walking along inclined terrain). Optimal-thermal-comfort routes have been designed and suggested based on the simulated neighborhood-scale heat stress map. • A meso-scale climate model and a human model are coupled to study extreme thermal stress. • Inter-individual human behaviors and topographical impacts are considered. • Modeling and mapping of neighborhood-scale dynamic human thermal stress are conducted. [ABSTRACT FROM AUTHOR]

Microclimate research has seen significant growth in recent years, particularly in areas such as outdoor thermal comfort, urban ecology, and urban heat mitigation. However, the short-term nature of many studies in this field presents challenges in ensuring that the collected data accurately represents local climate conditions. This paper introduces a novel method to enhance the quality and applicability of microclimate research by quantifying the representativeness of short-term meteorological data. Our approach employs the Kolmogorov-Smirnov (KS) statistic to compare daily meteorological data from nearby stations against long-term climate trends. Key findings demonstrate that this method effectively identifies representative data periods. This method allows researchers to evaluate the representativeness of each day's data according to their specific study objectives, whether focusing on typical or extreme weather conditions. By implementing this framework, researchers can: (a) Post-filter existing data to identify the most representative samples. (b) Quantify the climate representativeness of their findings, enhancing result interpretation and applicability. (c) More confidently generalize conclusions from short-term studies. The paper also provides simplified alternatives to the full method, making it accessible to a wider range of researchers. By adopting this approach, microclimate studies can achieve greater confidence in their data's representativeness, leading to more robust and generalizable conclusions. Our method addresses a key methodological challenge in microclimate research and provides a flexible data assessment framework. This framework enables researchers to systematically evaluate climate data representativeness, enhancing the reliability and applicability of their findings across various urban climate studies, from thermal comfort assessments to climate adaptation strategies. • Novel method quantifies representativeness of short-term microclimate data. • Kolmogorov-Smirnov statistic used to compare daily data against long-term climate. • Method validated using decade-long hourly weather data from Hong Kong Observatory. • Guidelines provided for framework integration in microclimate studies. • Simplified alternatives offered for widespread application in urban climate study. [ABSTRACT FROM AUTHOR]

Surface net radiation (SNR), a cornerstone in the intricate energy balance of urban climate systems, has garnered significant attention in research. While past endeavors have illuminated the effects of two-dimensional urban configurations on SNR, a conspicuous void persists in elucidating the nuanced interplay between three-dimensional (3D) urban features — encompassing building heights, volumes, and floor area ratios—and their modulation of SNR in urban landscapes. Addressing this gap, our research introduced a bespoke physical inversion approach, refined for high-resolution (30m) seasonal SNR assessment in urban areas, leveraging Landsat 8 imagery and meteorological datasets. We delved deeper into the seasonal dynamics of SNR influenced by 3D urban structures, leveraging the potent XGBoost regression algorithm and SHAP model. Our case study, centered on Beijing's primary urban region, underscores the inversion method's efficacy, with deviations from In-situ measured SNR values amounting to 10.21 W/m2 in spring, 8.43 W/m2 in summer, 11.55 W/m2 in autumn, and 9.14 W/m2 in winter. This precision was also reflected in an impressive R2 value of 0.81 and a standard deviation of 23.90 W/m2 compared with existing literature benchmarks. Seasonally, SNR peaked during summer, averaging 642.12 W/m2, followed by spring (590.08 W/m2), autumn (333.75 W/m2), and winter (275.93 W/m2). Water bodies exhibited the highest SNR throughout, peaking at 809.25 W/m2 in summer, whereas bare land registered the lowest, averaging 494.58 W/m2. Intriguingly, buildings with an average height below 15m significantly mitigated heat gain in summer, while a diverse mix of taller buildings exceeding 23m enhanced energy retention in winter, fostering a warming effect. By uncovering the intricate links between 3D urban structures and SNR, our findings inform strategic interventions that can effectively harness or mitigate SNR, contributing to more resilient and livable cities. • A physical inversion scheme concerning high resolution urban SNR was developed. • SNR results showed high accuracy compared with existing method and in-situ observation. • We first explored the impact of 3D USPs on seasonal SNRs. • MBH, SVF, BU, and FAR yielded significantly influence on seasonal SNRs. [ABSTRACT FROM AUTHOR]

As the impact of climate change on cities intensifies, the analysis and modeling of the urban microclimate are becoming increasingly important. A key parameter to this end is the estimation of urban surface temperatures. Traditional approaches for such estimates, that use urban micrometeorology models, are based on simulations that require heavy computations and complex inputs — including urban geometry, radiative parameters, and meteorological conditions. As a result, they cannot be applied extensively in all cities, where some of the input parameters might be missing and computational power might not be available. In this paper, we propose an alternative approach founded upon a deep learning framework, requiring markedly simplified inputs: street view imagery and meteorological conditions. We use our model to estimate building facade surface temperatures in the city of London (Ontario, Canada) and compare results both with simulated values obtained from an established simulation software TUF-3D and ground truth data collected through an onsite campaign with a FLIR thermal imager. Results substantiate the superiority of our proposed approach over TUF-3D, concurrently emphasizing its advantages in terms of input simplicity and computational resource efficiency. As street view imagery is becoming ubiquitous across the world – for instance, through platforms such as Google Street View – our approach lays the foundation for a new paradigm of fast, cost-effective, and highly scalable models, empowering urban designers and local authorities to better understand a changing urban climate. • Urban energy models underestimate effects of shading and complex geometry. • Deep image translation networks accurately and quickly predict surface brightness temperatures. • Novel urban microclimate prediction models can be trained on street view images. • Common street view imagery platforms can empower scalable urban climatology tools. [ABSTRACT FROM AUTHOR]

Differences in lifestyle, age, abilities, material possibilities, but also gender are the rule in urban space, homogeneity and commonalities the exception. Dealing with these differences in the management and planning of shared spaces is a major challenge for cities. Based on a three-year observation of the rhythms of a neighbourhood park in Geneva, we argue that such spaces are urban commons that can be managed by a bottom-up logic if some conditions of self-organisation are present, in particular a design of the place facilitating the production of trust and encounters, a social context of respect of differences generated by an urban climate of indifference and finally a democratic way of solving problems (a liberal way of coordination). The results show that the temporal division of use of a same place, based on users' observations of the others, on expectations, conflictive short encounters, and reflexive learning processes are fundamentals to transform a space into a shared common place of inclusion. In this process without negotiators, the temporal organisation of the space is a key element in the creation of a common space for all. • To be inclusive, parks can be organised by a temporal division of use. • Inclusive dynamics are based on observations of the other users of the park. • Negotiations of the use are based on reflexive learning processes. • Temporal and spatial divisions of the park create the common space. • The design of a park can facilitate the production of trust and encounters [ABSTRACT FROM AUTHOR]

In the face of climate change and increasing urbanisation, ensuring outdoor thermal comfort is becoming an increasingly crucial consideration for sustainable urban planning. However, informed decision-making is limited by the challenge of obtaining high-resolution thermal comfort data. This study introduces an interdisciplinary, low-resource, and user-friendly methodology for thermal comfort mapping, employing a self-built low-cost meteorological device for mobile climate monitoring. This device was utilised in the city center of Dresden, Germany to collect air temperature, humidity, pressure, surface temperature, global radiation, and globe temperature data as key inputs for the calculation of thermal comfort indices. These measurements were then used to calculate the Universal Thermal Climate Index (UTCI) using the RayMan Pro urban climate modelling program. Due to the limited resource capacities of urban planning departments, clear priority areas must be identified. Therefore, an exemplary approach for the prioritisation of consistent hotspots using the highest 5% of UTCI values was developed. The spatial variances in UTCI were validated through mobile pedestrian thermal comfort questionnaires, which allowed for the comparison of objective and subjective estimates of thermal comfort and gave the basis on which to make holistic and practical suggestions for urban planning interventions. This paper demonstrates an accessible and interdisciplinary approach to thermal comfort mapping which can empower urban planning stakeholders with scientifically informed and cost-effective decision-making tools for climate-adapted urban development. [ABSTRACT FROM AUTHOR]

Recognising the increasing complexities posed by climate challenges to urban environments, it is crucial to develop holistic capabilities for urban areas to effectively respond to climate-related risks, forming the backbone of sustainable urban planning strategies and demanding a comprehensive understanding of urban climate justice. It requires a thorough examination of how climate change exacerbates social, economic, and environmental inequalities within urban settings, which requires a series of sophisticated spatial modellings and relies on data collected periodically. This paper introduces a novel dual-GNN approach, Multi-Hyper Graph Neural Network (MHGNN), with street view imagery as input. The proposed model integrates a multigraph and a hypergraph to model intricate spatial patterns for classifying urban climate justice. The multigraph component of the MHGNN captures spatial proximity and pair-wise connections between urban areas to assess climate impacts. Meanwhile, the hypergraph component addresses higher-order dependencies by incorporating hyperedges that connect multiple geographic areas based on their similarities, thus capturing the multi-faceted relationships among areas with comparable geographic characteristics. By harnessing the strengths of both multigraph and hypergraph structures, the MHGNN provides a comprehensive understanding of the spatial dynamics of urban climate justice. It achieves nearly a 24% performance improvement compared to conventional spatial modelling methods, establishing it as a valuable tool for researchers and policymakers in this domain. Codes available at GitHub. 1 1 https://github.com/PengyuanLiu1993/SensingUrbanClimate. [Display omitted] • A dual-GNN to model multifaceted spatial patterns for studying urban climate justice. • Spatially-explicit GeoAI incorporates Laws of Geography. • Nearly 24% performance improvement compared to conventional spatial modelling methods. • Socio-economic indicators are crucial for understanding urban climate vulnerabilities. • Spatial structures from multiple levels contribute holistically to the urban climate justice classification. [ABSTRACT FROM AUTHOR]

Climate models are increasingly used to predict urban climate, but uncertainties in urban surface properties, such as thermal conductivity and capacity, lead to inaccuracies in comfort indices calculations. The forthcoming TRISHNA satellite mission will provide a unique dataset of thermal infrared imagery (TIR) to study urban area, with a planned spatial resolution of 57 meters and a revisit schedule of three times every eight days. This study aims to develop a novel method to identify urban thermal properties based on prior knowledge by combining urban microclimate modeling and high spatial resolution satellite TIR, to provide input to microclimate urban models, for outdoor summer comfort assessment. Using a Bayesian approach, the method compares observed satellite-derived land surface temperatures (LST) with simulated LST from a combination of microclimate (Solene micro-climate) and radiative (DART) models. In this present work, as TRISHNA not yet operational, simulated data also serves as the observations. An ideal street canyon model, representing typical urban structures, was used to validate the method. The approach improved the accuracy of thermal property estimates for horizontal surfaces by up to three times compared to random estimations within prior intervals. Best results were achieved with daytime TIR images acquired during the hottest days. • Inaccurate urban thermal properties lead to poor summer outdoor comfort predictions. • Horizontal urban surfaces thermal properties can be determined using satellite data and an urban microclimate model. • Best estimations were with daytime satellite images acquired on the hottest days. [ABSTRACT FROM AUTHOR]

Cities are positioning themselves at the center of the Anthropocene, hosting most of the world's population and global socioeconomic activities. The increasing prospect of escalating climate hazards is threatening cities and citizens worldwide, indicating the unprecedented importance of urban climate resilience building. However, the disconnect between resilience scholarship and practical policymaking hinders effective, evidence-based policymaking. By analyzing case-based, peer-reviewed articles worldwide, this study reveals the status of current resilience-building policies and the gaps therein. The results suggest that less than a third of the literature discusses policy implementations, with a notable absence of macro-level policies and crisis management toolkits. The authors underscore the potential integrated pathways to transcending the traditional place- and community-based resilience building practices, emphasizing the importance of integrating scholarly insights into practical policy frameworks for a more resilient urban future. [Display omitted] [ABSTRACT FROM AUTHOR]