Search Results (24)

Climate change is intensifying the frequency and severity of extreme weather events, threatening the resilience of buildings and urban infrastructure. While technical solutions for climate adaptation in buildings are well documented, their economic viability remains a critical, yet underexplored, dimension of decision-making. This novel systematic review analyzes publications with an exclusive focus on climate adaptation strategies for buildings using cost-based evaluation methods. This review categorises the literature into three methodological clusters: Cost–Benefit Analysis (CBA), Life Cycle Costing (LCC), and alternative methods including artificial intelligence, simulation, and multi-criteria approaches. CBA emerges as the most frequently used and versatile tool, often applied to evaluate micro-scale flood protection and nature-based solutions. LCC is valuable for assessing long-term investment efficiency, particularly in retrofit strategies targeting energy and thermal performance. Advanced methods, such as genetic algorithms and AI-driven models, are gaining traction but face challenges in data availability and transparency. Most studies focus on residential buildings and flood-related hazards, with a growing interest in heatwaves, wildfires, and compound risk scenarios. Despite methodological advancements, challenges persist—including uncertainties in climate projections, valuation of non-market benefits, and limited cost data. This review highlights the need for integrated frameworks that combine economic, environmental, and social metrics, and emphasises the importance of stakeholder-inclusive, context-sensitive decision-making. Ultimately, aligning building adaptation with financial feasibility and long-term sustainability is achievable through improved data quality, flexible methodologies, and supportive policy instruments. Full article

As nations worldwide commit to carbon neutrality targets in response to accelerating climate change, the spatial modeling of carbon emissions has emerged as an indispensable tool for policy implementation and assessment. This paper presents a systematic review of the field from bibliometric and methodological perspectives. We synthesize key developments in spatial allocation techniques, data-driven models, and emission characterization methods. A central focus is the transformative role of geospatial big data in improving model accuracy and applicability, particularly how fine-grained, high-resolution modeling enhances the efficacy of emission reduction strategies. Our analysis reveals several key conclusions. First, the literature on carbon emission spatial modeling is expanding rapidly, with a discernible shift in focus from coarse, large-scale assessments toward more granular analyses that are sector-specific, high-resolution, and multidimensional. Second, hybrid models that integrate top-down and bottom-up approaches are now the predominant strategy for enhancing both accuracy and applicability; coupling mechanistic models with machine learning techniques effectively reconcile macro-scale data consistency with micro-scale heterogeneity. Third, the integration of geospatial big data is revolutionizing the field by providing the high-resolution, multidimensional, and dynamic inputs necessary to transition from macro- to micro-scale analysis. This is particularly evident in fine-grained assessments of urban systems—including spatial functions, morphology, and transportation networks—where such data dramatically improve the characterization of emission sources, intensities, and their spatiotemporal heterogeneity. This study ultimately elucidates the critical role of fine-grained modeling in advancing the quantitative understanding of carbon emission drivers, enabling robust scenario simulations for carbon neutrality, and informing effective low-carbon spatial planning. The synthesis presented here aims to provide a firm theoretical and technical foundation to support the ambitious carbon reduction targets set by nations worldwide. Full article

Multi-scale thermal regulation of urban green spaces is critical for climate-adaptive planning. Addressing the limited research on key indicators and cross-scale synergies in high-density areas, this study developed an integrated framework combining multi-granularity grids and boosted regression tree (BRT) modeling to investigate nonlinear scale-dependent relationships between landscape parameters and land surface temperature (LST) in the central urban area of Shijiazhuang. Key findings: (1) Spatial heterogeneity and scale divergence: Vegetation coverage (FVC) and green space area (AREA) showed decreasing contributions at larger scales, while configuration metrics (e.g., aggregation index (AI), edge density (ED)) exhibited positive scale responses, confirming a dual mechanism with micro-scale quality dominance and macro-scale pattern regulation. (2) Threshold effects quantification: The BRT model revealed peak marginal cooling efficiency (0.8–1.2 °C per 10% FVC increment) within 30–70% FVC ranges, with minimum effective green patch area thresholds increasing from 0.6 ha (micro-scale) to 3.5 ha (macro-scale). (3) Based on multi-scale cooling mechanism analysis, a three-tier matrix optimization framework for green space strategies is established, integrating “micro-level regulation, meso-level connectivity, and macro-level anchoring”. This study develops a green space optimization paradigm integrating machine learning-driven analysis, multi-scale coupling, and threshold-based management, providing methodological tools for mitigating urban heat islands and enhancing climate resilience in high-density cities. Full article

Wildfires represent an increasing threat to ecosystems and communities, driven by climate change, fuel dynamics, and human activities. In Ambato, Ecuador, a city in the Andean highlands, these risks are exacerbated by prolonged droughts, vegetation dryness, and urban expansion into fire-prone areas within the Wildland–Urban Interface (WUI). This study integrates climatic, ecological, and socio-economic data from 2017 to 2023 to assess wildfire risks, employing advanced geospatial tools, thematic mapping, and machine learning models, including Multinomial Logistic Regression (MLR), Random Forest, and XGBoost. By segmenting the study area into 1 km² grid cells, microscale risk variations were captured, enabling classification into five categories: ‘Very Low’, ‘Low’, ‘Moderate’, ‘High’, and ‘Very High’. Results indicate that temperature anomalies, reduced fuel moisture, and anthropogenic factors such as waste burning and unregulated land-use changes significantly increase fire susceptibility. Predictive models achieved accuracies of 76.04% (MLR), 77.6% (Random Forest), and 76.5% (XGBoost), effectively identifying high-risk zones. The highest-risk areas were found in Izamba, Pasa, and San Fernando, where over 884.9 ha were burned between 2017 and 2023. The year 2020 recorded the most severe wildfire season (1500 ha burned), coinciding with extended droughts and COVID-19 lockdowns. Findings emphasize the urgent need for enhanced land-use regulations, improved firefighting infrastructure, and community-driven prevention strategies. This research provides a replicable framework for wildfire risk assessment, applicable to other Andean regions and beyond. By integrating data-driven methodologies with policy recommendations, this study contributes to evidence-based wildfire mitigation and resilience planning in climate-sensitive environments. Full article

Global warming and climate change are significantly impacting local climates, causing more intense heat during the summer season, which poses risks to individuals with pre-existing health conditions and negatively affects overall human health. While various studies have examined the Surface Urban Heat Island (SUHI) phenomenon, these studies often focus on small to large geographic regions using low-to-moderate-resolution data, highlighting general thermal trends across large administrative areas. However, there is a growing need for methods that can detect microscale thermal patterns in environments familiar to urban residents, such as streets and alleys. The temperature-humidity index (THI), which incorporates both temperature and humidity data, serves as a critical measure of human-perceived heat. However, few studies have explored microscale THI variations within urban settings and identified potential THI hotspots at a local level where SUHI effects are pronounced. This research aims to address this gap by estimating THI at a finer resolution scale using data from multiple sensor platforms. We developed a model with the random forest algorithm to assess THI trends at a resolution of 0.5 m, utilizing various variables from different sources, including Landsat 8 land surface temperature (LST), unmanned aerial system (UAS)-derived LST, Sentinel-2 NDVI and NDMI, a wind exposure index, solar radiation modeled from aircraft and UAS-derived Digital Surface Models, and vehicle density and building floor area from social big data. Two models were constructed with different variables: Model_natural, which includes variables related to only natural factors, and Model_mix, which includes all variables, including anthropogenic factors. The two models were compared to reveal how each source contributes to the model development and SUHI effects. The results show significant improvements, as Model_natural had a fitting R² = 0.5846, a root mean square error (RMSE) = 0.5936 and a mean absolute error (MAE) = 0.4294. Moreover, when anthropogenic factors were introduced, Model_mix performed even better, with R² = 0.9638, RMSE = 0.1751, and MAE = 0.1065 (n = 923). This study contributes to the future of microscale SUHI analysis and offers important insights into urban planning and smart city development. Full article

Global climate change has exacerbated alterations in urban thermal environments, significantly impacting the daily lives and health of city residents. Measuring and understanding urban land surface temperatures (LST) and their influencing factors is important in addressing global climate change and enhancing the well-being of residents. However, due to limitations in data precision and analytical methods, existing studies often overlook the microscale examination closely related to residents’ daily lives, and lack a deep exploration of the spatial heterogeneity of the influencing factors. This leads to these results being ineffective in guiding the planning and construction of cities. Taking Shenzhen as a case study, our study investigates the effects of various microscale build environment characteristics of LST using street view images and machine learning. A convolutional neural network model adopting the SegNet architecture is used to perform semantic segmentation on street view images, extracting features of the microscale urban-built environment. The LST is inverted through the Google Earth Engine (GEE) platform. By using Multiscale Geographically Weighted Regression (MGWR) models, our study reveals the comprehensive impact of the urban-built environment on LST and its significant spatial heterogeneity. The findings indicate that the proportions of sky, roads, and buildings are positively correlated with LST, while trees have a significant cooling effect. Although earth and water can reduce LST, their overall contribution is minimal due to limitations in their area and distribution patterns. This study not only reveals the key factors affecting urban LST at the microscale but also emphasizes the necessity of considering the spatial heterogeneity of these factors’ impacts. This suggests the need for targeted strategies for different areas to effectively improve the urban thermal environment and achieve sustainable urban development. Full article

Recognizing the growing trend of the urban population and the undeniable fact of global and regional climate change, it becomes increasingly important to explore how we can improve the livability of our cities not only in the distant future but also in the next few years. A critical aspect of this endeavor involves studying how we can effectively mitigate human heat load in urban areas. In our research, in the case of a medium-sized city (Szeged, Hungary), we examined the effect of surface modifications caused by vegetation on human thermal perception during the day and night of two heatwave days. To achieve this, we used the MUKLIMO_3 micro-scale climate model to simulate the thermal climate of Szeged, while the thermal load was assessed with the perceived temperature calculated by the Klima-Michel model. Our analysis also relied on the local climate zone (LCZ) system to describe the original land cover and the additional urban green spaces in the study area. We scrutinized the effects of added vegetation of different types and densities, as well as the presence of protective forests surrounding the city. Our findings revealed that the effect of the added vegetation can only be detected on the modified surfaces and in their immediate vicinity. Notably, dense urban greenery resulted in up to a 2–3 °C reduction in perceived temperature in certain areas during the daytime, highlighting the profound impact of targeted green space development. In addition, it is crucial to consider the airflow-blocking effect of woody vegetation, which can increase thermal load by 1–3 °C in the areas located in a downwind direction. Therefore, the changing regional climatic conditions (e.g., wind direction) and the development of the right type and location of urban green areas deserve special attention during modern urban planning processes. Full article

Citizens in urban areas are affected by the urban heat island (UHI) effect, resulting in increased thermal heat compared to rural areas. This threat is exacerbated by global climate change. Therefore, it is necessary to assess human thermal comfort and risk for decision making. This is important for planners (climate resilience), the health sector (information for vulnerable people), tourism, urban designers (aesthetics), and building architects. Urban structures modify local meteorological parameters and thus human thermal comfort at the microscale. Knowledge of the pattern of a city’s UHI is typically limited. Based on previous research, generalized additive models (GAMs) were built to predict the spatial pattern of the UHI in the city of Karlsruhe. The models were trained with administrative, remotely sensed, and land use and land cover geodata, and validated with measurements in Freiburg. This identified the hot and cold spots and the need for further urban planning in the city. The model had some limitations regarding water bodies and anthropogenic heat production, but it was well suited for applications in mid-latitude cities which are not topographically characterized. The model can potentially be used for other cities (e.g., in heat health action plans) as the training data are freely available. Full article

Cities are facing significant challenges related to climate change, particularly due to the increasing impact of the Urban Heat Island (UHI) phenomenon. The present study investigated the UHI phenomenon at the microscale in Annaba, Algeria. The research involved a multi-step approach, starting with on-site measurements of urban microclimate parameters, performed in downtown Annaba on 6 July 2023. The UHI intensity was quantified by comparing city-measured temperatures with rural surroundings. Thermal imaging is then used to empirically identify the contributing factors to UHI initiation at the microscale. The study employed the ENVI-met model to analyse mitigation strategies, manipulating parameters for six scenarios including the current design of the study area. Outputs were used to assess the impact of these strategies on air temperature, mean radiant temperature, relative humidity, and wind speed. The findings revealed an intense UHI effect in Annaba city with a peak difference of 6.9 °C, with practical implications for buildings, ground and roads, vehicles, air conditioners, and specific facade materials. Introducing urban vegetation, particularly urban trees and green roofs, proved highly effectiveness in mitigating the UHI in downtown Annaba. Urban trees demonstrated the most substantial impact, reducing temperatures by 1.9 °C at 1 p.m., while green roof temperature reductions ranged from 0.1 °C to 2 °C. Full article

Urban development and its climatic consequences have caused urban decision-makers to establish strategies to mitigate climate change. The implementation of different green spaces is one of the main strategies to reduce the environmental and climatic consequences of urbanization. Therefore, the main objective of this research is to reveal the effect of different green space scenarios on micro-bioclimatic conditions of a hospital located in Gorgan city, Golestan province. Therefore, in order to determine the position of the hospital building relative to Gorgan’s urban heat island (UHI), the location and changes in UHI intensity of Gorgan were determined as evidence of urban expansion. Since 27 July was determined as the hottest day in Gorgan city based on historical data analysis, the climatic conditions during 27 July 2021 were measured using an AR847 data logger installed in the hospital environment. Additionally, four different conditions, including actual environmental conditions of the hospital (actual conditions), along with the application of cypress trees (scenario A), plane trees (scenario B), and Buxus shrubs (scenario C), have been used to analyze the impact of different vegetation species on the bioclimatic conditions of 5 Azar Hospital during two time intervals, including observational periods (1970–2020) and the decade of the 2040s. Finally, spatiotemporal patterns of the predicted mean vote (PMV) thermal index were calculated for the observational period and during the 2040s using the ENVI-met micro-scale model. Results showed that the study site is in the UHI, which can affect the micro-bioclimatic conditions and the patient’s thermal perception. For all designed scenarios, results indicate that the average PMV index will increase by the 2040s. However, implementing different green space scenarios showed that the minimum and maximum values of PMV were found in scenario B, of 2.7. The actual PMV conditions of the studied site increased by 3.5. The scenario introduction of green spaces during the 2040s indicates that the average PMV at the hospital site will be decreased by 0.9 compared to the actual conditions. The study proves that appropriate green space strategies can reduce thermal loads occurring due to global climate change and improve the thermal conditions in the study area. Full article

Daily walks are recommended for health gains, and walkable urban environments are recommended as one strategy to combat climate change. Evidence of the relationship between physical environments and psychological health is increasing. The aim of this study was to systematically review and compile evidence regarding micro-scale characteristics in urban outdoor environments that impacted pedestrian short-term experience and/or long-term psychological health. The databases ScienceDirect, Scopus, PubMed, PsychInfo, and Google Scholar were used. To explore the area, a large heterogeneity in publications was allowed; therefore, it was not possible to conduct a meta-analysis. From 63 publications, data items were extracted from full text and categorized according to the main study characteristics. Environmental characteristics impacting pedestrians psychologically were identified and categorized into themes: grey, green, blue, and white areas, and weather, temporalities, topography, person factors, and safety. Environmental factors were analyzed from the perspective of the circumplex model of human affect (negative/positive dimensions and activation/deactivation). The findings included the fact that urban pedestrians need both positively activating and deactivating (restorative) areas during walkabouts. Perceived safety is essential for experiencing the positive aspects of urban environments. Some characteristics interact differently or have different importance for health in different groups. To further develop research on pedestrian environments, psychological experiences should be included. Full article

The mitigation of the urban heat island effect is increasingly imperative in light of climate change. Blue–green space, integrating water bodies and green spaces, has been demonstrated to be an effective strategy for reducing the urban heat island effect and enhancing the urban environment. However, there is a lack of coupled analysis on the cooling island effect of blue–green space at the meso-micro scale, with previous studies predominantly focusing on the heat island effect. This study coupled the single urban canopy model (UCM) with the mesoscale Weather Research and Forecasting (WRF) numerical model to simulate the cooling island effect of blue–green space in the Eastern Sea-River-Stream-Lake Linkage Zone (ESLZ) within the northern subtropical zone. In particular, we comparatively investigated the cooling island effect of micro-scale blue–green space via three mitigation strategies of increasing vegetation, water bodies, and coupling blue–green space, using the temperature data at the block scale within 100 m square of the urban center on the hottest day in summer. Results showed that the longitudinally distributed lakes and rivers in the city had a significant cooling effect on the ambient air temperature (Ta) at the mesoscale, with the largest cooling range occurring during the daytime and ranging from 1.01 to 2.15 °C. In contrast, a 5~20% increase in vegetation coverage or 5~15% increase in water coverage at the micro-scale was observed to reduce day and night Ta by 0.71 °C. Additionally, the most significant decrease in physiologically equivalent temperature (PET) was found in the mid-rise building environment, with a reduction of 2.65–3.26 °C between 11:00 and 13:00 h, and an average decrease of 1.25°C during the day. This study aims to guide the optimization of blue–green space planning at the meso-micro scale for the fast-development and expansion of new urban agglomerations. Full article

Global climate change has led to flood disasters increasing in terms of frequency and damage caused, which seriously threatens urban and rural security. The flood regulation (FR) service function of the ecosystem plays an important role in mitigating flood disaster risk. Previous studies on flood regulation ecosystem services (FRES) are still lacking in a cross-scale assessment of supply and demand, refined simulation of regional complex hydrology, and application of spatial zoning management. Taking the Fujian Delta as an example, this study established a cross-scale research framework based on the social-ecosystem principle. The SWAT model was used to simulate the regional hydrological runoff and calculate the macro-scale supply of FRES. Taking patches of land as units, a flood risk assessment model was constructed to calculate the micro-scale demand for FRES for urban and rural society. Through a comparison of supply and demand across spatial scales, a zoning management scheme to deal with flood disaster risk was proposed. The results showed that: (1) The supply of FRES differed greatly among the sub-basins, and the sub-basins with low supply were mostly distributed in the lower reaches of Jiulong River and the coastal areas. (2) The demand for FRES was concentrated in high-density urban built-up areas. (3) By comparing the supply and demand of FRES in sub-basin units, 2153 km² ecological space was identified as the primary ecological protection area, and 914 km² cultivated land and bare land were identified as the primary ecological restoration area. (4) By comparing the supply and demand of FRES of land patch units, 65.42 km² of construction land was identified as the primary intervention area. This study provides a decision-making basis for regional flood disaster management from the perspective of FRES. Full article

The concept of low impact development (LID) plays a crucial role in rainstorm management and non-point source pollution prevention and control. Sorting and summarizing related research through the knowledge map can objectively present the disciplinary structure, research focus, and research hotspots of the LID research. Based on 2103 LID pieces of literature in Chinese and English included in the web of science (WOS) database and China’s integrated knowledge resources system (CNKI) database from 2004 to 2021, this paper aims to perform statistical analysis from three aspects: bibliometrics, keyword hotspot co-occurrence and clustering, and literature co-citation clustering. The obtained results reveal that research on LID-based issues maintains a high degree of enthusiasm in China and abroad, but their corresponding focuses are dissimilar. Foreign research essentially focuses on the environmental field with frequent interdisciplinary phenomena, combining the triple goals of water quality improvement, runoff reduction, and multi-functional expansion, and is committed to solving the impact of uncertain factors on urban stormwater management in extreme climates. Chinese research is mostly aimed at unlocking practical engineering problems, which also leads to the majority of research works in the field of building science and engineering. This is mainly due to a series of water-related problems caused by the change in land use types in China. The researchers have determined the type, quantity, location, and combination of the optimal LID measures by establishing appropriate models, using optimization algorithms, and developing multi-level analysis methods. Although the multi-dimensional results of LID in recent years have greatly expanded the framework paradigm, most of the conducted research works are still biased towards the micro-scale. The present hotspot research considers how to make a macroscopic overall layout and efficiently cooperate with the pipelines network, rivers, and lakes systems to unlock the problems pertinent to urban rainwater and non-point source pollution. Full article

The design of urban public open spaces plays a key role in the development of micro-scale reactions to global phenomena (pandemic, climate change, etc.) that are currently reshaping the human habitat. Their transformability and healthy influence on the urban environment make them strategic nodes for acupunctural regeneration with systemic effects. Several methods, models, and indicators have been developed to face the complexity of these spaces, made up of tangible and intangible layers; however, there is a gap between theoretical investigation and the need for public administrations to devise feasible solutions, strategies, and guidelines. The paper focuses on this mediation, presenting, as a case study, an adopted methodology and the first results achieved according to guidelines for the regeneration of the system of squares in the historical center of Alessandria (Piedmont, Italy). In this case, a multidisciplinary approach and a Multi-Criteria Analysis (MCA) method, supported by geospatial analysis and GIS technology, have been employed to work as mediators for a participatory process which will involve public administration, stakeholders, experts, and researchers. The paper presents an overview of the workflow, with a focus on the first set of thematic indicators and an open conclusion. It will explain how they have been defined, integrated, and turned into a dialogic tool, with the aim of laying the foundation for the next stage of involvement by the public administration and stakeholders. Specific attention will be paid to the key role of vegetational and environmental parameters, which represents the requalification strategy’s backbone, for both local and systemic scales. Full article