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Golestan province in the northern part of Iran has been affected by devastating floods. There has been a significant change in the pattern of rainfall in Golestan province based on an analysis of the seven heaviest rainfall events in recent decades. Climate change appears to be a significant contributing factor to destructive floods. Thus, this paper aims to assess the susceptibility of this area to flash floods in case of heavy downpours.

This paper uses a variety of computational approaches. Following the collection of data, spatial analyses have been conducted and validated. The layers of information are then weighted, and a final risk map is created. Fuzzy analytical hierarchy process, geographic information system and frequency ratio have been used for data analysis. In the final step, a flood risk map is prepared and discussed.

Due to the complex interaction between thermal fluctuations and precipitation, the situation in the area is further complicated by climate change and the variations in its patterns and intensities. According to the study results, coastal areas of the Caspian Sea, the Gorganrood Basin and the southern regions of the province are predicted to experience flash floods in the future. The research criteria are generalizable and can be used for decision-making in areas exposed to flash flood risk.

The unique feature of this paper is that it evaluates flash flood risks and predicts flood-prone areas in the northern part of Iran. Furthermore, some interventions (e.g. remapping land use and urban zoning) are provided based on the socioeconomic characteristics of the region to reduce flood risk. Based on the generated risk map, a practical suggestion would be to install and operate an integrated rapid flood warning system in high-risk zones.

Although photographs in sustainability reports are useful in conveying complex messages, they may also be used to manipulate the presentation of disclosures to exploit the limited cognitive processing capacity of humans. Therefore, this paper aims to examine the features of photographs aimed at capturing individuals’ attention through visual structures and evoking specific emotions through carefully chosen content. Furthermore, it examines whether such framing practice is explained by incentives for legitimizing behaviours and influencing reputation.

The authors conduct a content analysis of photographs in 154 sustainability reports published by US companies. The authors captured the nature of photographs, the context in which they are being used, their themes and emotional content and layout and interaction features to understand how photographs are used for attribute framing to influence information processing. Furthermore, the authors statistically examine the framing practice between companies with different characteristics to identify any patterns for the impression management use of photographs in sustainability reports.

Photographs are often large with a horizontal orientation to capture attention and show content viewed at eye level and in either medium or close-up shots to engage viewers. Furthermore, photographs are emotionally loaded with different themes such as depictions of people, technology and nature. These themes are used to predominately evoke positive emotions of awe, nurturance, pride, amusement and attachment. This practice is often used by companies in environmentally sensitive areas that have close consumer relationships or are covered controversially in the media.

The authors reveal reporting practices and identify photographic features that attract attention and convey emotions that go beyond aesthetic qualities. This is important because emotions conveyed through photographs can be potentially misleading and influence judgements subconsciously.

Evaluating the implementation of environmental management plans (EMPs) in highway construction projects is essential to avoid climate change. Public evaluations can help ensure that the EMP is implemented correctly and efficiently. To allow public evaluation of EMP implementations, this study aims to investigate performance indicators (PIs) for assessing EMP implementation in highway construction projects. To that end, the study objectives are to compare the critical PIs between environment auditors (EAs) and environment officers (EOs) and among the main project stakeholders (i.e. clients, contractors and consultants), create components for the critical PIs and assess the efficiency of the components.

The paper identified 39 PIs from interviews with environmental professionals and a systematic literature review. Then a questionnaire survey was developed based on the PIs and sent to EAs and EOs. The data were analyzed via mean score ranking, normalization, agreement analysis, factor analysis and fuzzy synthetic evaluation (FSE).

The analyses revealed 21 critical PIs for assessing EMP implementation in highway construction projects. Also, the critical PIs can be grouped into four components: ecological, pollution, public safety and ecological. Finally, the overall importance of the critical PIs from the FSE is between important and very important.

To the best of the authors’ knowledge, this paper is the first-of-its-kind study on the critical PIs for assessing EMP implementation in highway construction projects.

This study aims to develop a machine learning model to detect structure fire fatalities using a dataset comprising 11,341 cases from 2011 to 2019.

Exploratory data analysis (EDA) was conducted prior to modelling, in which ten machine learning models were experimented with.

The main fatal structure fire risk factors were fires originating from bedrooms, living areas and the cooking/dining areas. The highest fatality rate (20.69%) was reported for fires ignited due to bedding (23.43%), despite a low fire incident rate (3.50%). Using 21 structure fire features, Random Forest (RF) yielded the best detection performance with 86% accuracy, followed by Decision Tree (DT) with bagging (accuracy = 84.7%).

Limitations of the study are pertaining to data quality and grouping of categories in the data pre-processing stage, which could affect the performance of the models.

The study is the first of its kind to manipulate risk factors to detect fatal structure classification, particularly focussing on structure fire fatalities. Most of the previous studies examined the importance of fire risk factors and their relationship to the fire risk level.

Recently, there has been a shift toward the embodied energy assessment of buildings. However, the impact of material service life on the life-cycle embodied energy has received little attention. We aimed to address this knowledge gap, particularly in the context of the UAE and investigated the embodied energy associated with the use of concrete and other materials commonly used in residential buildings in the hot desert climate of the UAE.

Using input–output based hybrid analysis, we quantified the life-cycle embodied energy of a villa in the UAE with over 50 years of building life using the average, minimum, and maximum material service life values. Mathematical calculations were performed using MS Excel, and a detailed bill of quantities with >170 building materials and components of the villa were used for investigation.

For the base case, the initial embodied energy was 57% (7390.5 GJ), whereas the recurrent embodied energy was 43% (5,690 GJ) of the life-cycle embodied energy based on average material service life values. The proportion of the recurrent embodied energy with minimum material service life values was increased to 68% of the life-cycle embodied energy, while it dropped to 15% with maximum material service life values.

The findings provide new data to guide building construction in the UAE and show that recurrent embodied energy contributes significantly to life-cycle energy demand. Further, the study of material service life variations provides deeper insights into future building material specifications and management considerations for building maintenance.