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As a consequence of climate change and urbanization, many cities will have to deal with more flooding and extreme heat stress. This paper presents a framework to maximize the effectiveness of Nature-Based Solutions (NBS) for flood risk reduction and thermal comfort enhancement. The framework involves an assessment of hazards with the use of models and field measurements. It also detects suitable implementation sites for NBS and quantifies their effectiveness for thermal comfort enhancement and flood risk reduction. The framework was applied in a densely urbanized study area, for which different small-scale urban NBS and their potential locations for implementation were assessed. The overall results show that the most effective performance in terms of flood mitigation and thermal comfort enhancement is likely achieved by applying a range of different measures at different locations. Therefore, the work presented here shows the potential of the framework to achieve an effective combination of measures and their locations, which was demonstrated on the case of the Sukhumvit area in Bangkok (Thailand). This can be particularly suitable for assessing and planning flood mitigation measures in combination with heat stress reduction.

Teacher’s professional identity (TPI) is an essential and decisive element in the way future EY and primary teachers approach their labour. This study aims to analyse how and when student teachers build TPI and to study its relationship with personal well-being. The sample was made up of 135 students of the Faculty of Education of the University of Burgos (Spain), aged 18–44. A questionnaire elaborated ad hoc and an individual psychological well-being scale were both implemented. The results suggest that training activities, initially scheduled by the university courses, are those which most contribute to the building of a TPI; that there seem to be no differences in relation to gender and year of study in the factors which contribute to its development, regardless of whether it is rather innovative or research-oriented and that there is a relationship between types of TPI, as well as between these and the satisfaction with academic choice, and psychological well-being. Future teachers should build a solid TPI which enables them to successfully adapt to complex educational settings and encourages them to innovate. It is necessary to gradually make room for reflection from the beginning of the university training, as this could improve the future professional performance and the students’ psychological well-being.

We undertook an autopsy of the drivers of individual foreign real estate investment ‘bust’ in Australia through a new theoretical lens of ‘habitus’. Our autopsy data drew contours around the individual foreign real estate capital ‘boom and bust’ cycle, as well as the long-term commitment of professionals in the real estate sector to Australia’s real estate market. More specifically, we showed that the foreign capital ‘boom and bust’ cycle began in earnest in about 2010 (starting at A$8.7 billion), grew to A$72.4 billion in 2016–2017, and then declined to A$12.5 billion in 2017–2018. This decline in foreign capital into Australian real estate occurred within a domestic real estate market in Sydney that also started to slow in 2017. Based on 20 semi-structured interviews with real estate professionals in Sydney and public material culture data, we found out that the off-the-plan apartment sales and global policy landscape changes contributed to the decline of foreign real estate investment in Australia. The three possible implications for Sydney’s future residential real estate development: (1) The loss of investors, (2) the evolution of the labor force, and (3) the diversification of housing products, have been raised as part of a future research road map.

In 2021, 56% of the global population lived in cities, and by 2050 the ratio of urban-to-rural population is expected to reach 67% [...]

The current studies on carbon dioxide (CO2) emissions and the impacts on public health focus mainly on evaluating CO2 emissions from two types of emission sources. The first is a fixed source type from industrial plants, which can be controlled or easily evaluated. The second is a mobile source type from the transport sector, especially from medium- and heavy-duty vehicles (MHDVs), which produce high emissions. However, the common methods of evaluation of the average value of CO2 emissions per kilometer of a vehicle use a general Intergovernmental Panel on Climate Change (IPCC) model that does not consider the topography or road conditions. This affects the accuracy of CO2 emission assessments and, in turn, affects the accuracy of any analysis needed to establish health policies and the management of public health within the affected area. In this paper, therefore, we present the development of emission coefficient calculations with varying topography conditions for MHDVs with consideration of the health effects on the populace. The study area was the Asian highway network in Thailand that passes through all regions and is geographically diverse. Data were collected from the Department of Highway’s records, global positioning system (GPS) and electronic control unit (ECU) with data consisting of road data, slope, distance, traffic level and vehicle weight, as well as fuel consumption along the transportation route. To analyze and map out the correlation of the CO2 emission coefficients for each road slope, we determined the coefficient of the CO2 emissions using multiple linear regression analysis and validated this using Huber–White robust standard errors for heteroscedasticity. This method was more cost-efficient and time-efficient compared to the conventional approaches. We also created CO2 emission maps with risk points for health effects, and we propose policy designs and plans to manage the traffic level in each of these areas prone to higher levels of such emissions.

Numerous studies have been undertaken to determine the optimal land use/cover classification algorithm. However, there have not been many studies that have compared and evaluated the performance of maximum likelihood (ML), random forest (RF), support vector machine (SVM), and classification and regression trees (CART) using ASTER imagery, especially in a mining district. Therefore, this study aims to investigate land use/cover (LULC) change over three decades (1990–2020), comparing the performance of the ML, RF, SVM, and CART machine learning algorithms. The Landsat and ASTER data were retrieved using Google Earth Engine (GEE). Traditional ML classification was performed on ArcGIS 10.2 software while RF, SVM, and CART classification were undertaken on GEE. Then, thematic accuracy assessments were conducted for the four algorithms and their performances were compared. The results showed that the largest changes in area occurred in forest cover that decreased from 37.8 to 27.3 km2 during the three decades. The remarkable expansion of gold mining occurred during 2005–2010 with the increases of 1.6%. The mining land rose by 2.9% during the study period whereas agricultural land increased significantly by 10.7% between 1990 and 2020. When comparing the four algorithms, the RF algorithm gives the highest accuracy with an overall accuracy of 95.85% while SVM follows RF with 91.69%. This study proved that RF is the best choice for optimal land use/cover classification, particularly in the mining district.

Road traffic accidents are currently between the seventh and tenth leading cause of death in the world, with approximately 1.35 million people killed per year. Despite extensive efforts by governments, according to the World Health Organization, road accidents still cause far too many deaths, especially among pedestrians, cyclists and two-wheel motor vehicle riders, who together account for almost 50% of road traffic fatalities. In particular, Spain had 410,974 traffic accidents between 2016 and 2019, involving 722,516 vehicles and 61,177 pedestrians with varying degrees of injury. This study uses the Bayesian network method to understand how the pedestrians’ responsibility and actions at the time of the traffic accident affect the injury suffered by said pedestrian, also considering the variables of the road infrastructure and vehicles at the accident site. The results confirm that the variables linked to the unsafe behavior of pedestrians, and their responsibility in traffic accidents, increase the risk of suffering serious or fatal injuries during an accident; for example, if a pedestrian is distracted this increases his/her probability of suffering a severe injury (27.86%) with respect to not being distracted (20.73%). Conditions related to traffic in high-speed areas, areas with no or poor lighting, and areas lacking sidewalks, also record increases in pedestrian injury, as is the case in the age group of pedestrians over 60 years of age.