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The purpose of this paper is to explore the degree of inflation persistence across all US metro areas over the post-pandemic period.

Both the Multivariate Core Trend (MCT) model and a fractional integration model, that is the Multivariate Unobserved-Components Stochastic Volatility Outlier-adjusted (MUCSVO) model are estimated.

The findings provide clear evidence of a significant inflation persistence in ten metro areas and the absence of persistence in the remaining areas, implying that in the former areas, inflation clearly indicates a strong persistent pattern. In other words, in these ten areas, the persistent component dominates the evolution of the trend and stands as a significant driver of inflation.

The findings have important implications for US policymakers to consider implementing more targeted policies to address inflation in specific metro areas to reduce the overall inflation rate, or they may need to consider tailoring fiscal policies to address inflationary pressures in specific metro areas. The findings illustrate the need for targeted policy interventions to address inflationary pressures in specific areas, as well as the importance of understanding the drivers of inflation persistence to develop effective policy responses. The findings also provide insights to firms on how to mitigate the risks of inflation. They may need to diversify their products or supplier base so that they do not rely on areas experiencing persistent inflation.

This paper contributes to the literature by extending the discussion of the impact of the recent pandemic crisis on US regional inflation. The findings have important implications for US policymakers to consider implementing more targeted policies to address inflation in specific metro areas to reduce the overall inflation rate, or they may need to consider tailoring fiscal policies to address inflationary pressures in specific metro areas. The findings illustrate the need for targeted policy interventions to address inflationary pressures in specific areas, as well as the importance of understanding the drivers of inflation persistence to develop effective policy responses. The findings also provide insights to firms on how to mitigate the risks of inflation. They may need to diversify their products or supplier base so that they do not rely on areas experiencing persistent inflation.

The study aims to explore the impact of National Health Insurance Scheme (NHIS) enrolment on farm investments in a developing country setting. We classify farm investments into (1) soil and land investments and (2) hired adult labour.

This study used data on 5,883 farm households from the sixth round of the Ghana Living Standard Surveys (GLSS), which is nationally represented data at the household level. The data also includes a Labour Force Survey module. The sample frame was divided into a primary and secondary sampling unit, with interviews taking place in 1,200 enumeration areas (EAs). The estimation of impacts was carried out using ordinary least squares (OLS) estimations and addressed endogeneity concerns using propensity score matching (PSM) and instrumental variable (IV) estimators.

The study finds a strong positive association between the NHIS enrolment status of farm households and investments in agricultural land and soil health improvement. Precisely, farm households who are enroled in the health insurance system tend to invest about 32% more in soil and land improvement activities and 30% more in hired farm labour than households who are not enroled in NHIS.

The overall evidence from our study suggests that instead of high investments in fertilizer and other input subsidy programmes in Africa, sustainable smallholder agricultural investments can be achieved if concerns and issues of farmers’ health coverage are adequately addressed.

This is one of the first papers that have explored the impact of NHIS in developing countries on farm investments.

Green roofs are strategies for the ecological intensification of cities and a measure of meeting some of the sustainable development goals (SDGs). They have widely been adopted as an adaptation strategy against an urban heat island (UHI). However, they are conventionally soil-based making it difficult and expensive to adopt as a strategy for greening existing buildings (GEB). This paper, therefore, develops a novel green roof system using climbers for thermal-radiative performance. The paper explores the vitality of climbing species as a nature-based strategy for GEB, and for the ecological improvement of the predominantly used cool roofs in sub-Saharan Africa (SSA).

Simulation for the same building Kejetia Central Market (KCM) Redevelopment; the existing aluminium roof (AL), soil-based extensive green roof (GR1) and the proposed green roof using climbing plants (GR2) were performed using ENVI-met. The AL and GR1 were developed as reference models to evaluate and compare thermal-radiative performance of the conceptual model (GR2). The long wave radiation emission (Q_lw), mean radiant temperature (MRT) and outdoor air temperature (T_a) of all three roofing systems were simulated under clear sky conditions to assess the performance and plant vitality considering water access, leaf temperature (T_f) and latent heat flux (LE₀) of GR1 and GR2.

There was no short wave radiation (Q_sw) absorption at the GR2 substrate since the climbers have no underlying soil mass, recording daily mean average Q_lw emission of 435.17 Wm⁻². The soil of GR1, however, absorbed Q_sw of 390.11 Wm⁻² and a Q_lw emission of 16.20 wm⁻² higher than the GR2. The AL recorded the lowest Q_lw value of 75.43 Wm⁻². Also, the stomatal resistance (r_s) was higher in GR1 while GR2 recorded a higher average mean transpiration flux of 0.03 g/sm³. This indicates a higher chance of survival of the climbers. The T_a of GR2 recording 0.45°C lower than the GR1 could be a good UHI adaptation strategy.

No previous research on climbers for green roof systems was found for comparison, so the KCM project provided a unique confluence of dynamic events including the opportunity for block-scale impact assessment of the proposed GEB strategy. Notwithstanding, the single case study allowed a focussed exploration of the novel theory of redefining green roof systems with climbers. Moreover, the simulation was computationally expensive, and engaging multiple case studies were found to be overly exhaustive to arrive at the same meaningful conclusion. As a novelty, therefore, this research provides an alternative theory to the soil-based green roof phenomenon.

The thermal-radiative performance of green roofs could be improved with the use of climbers. The reduction of the intensity of UHI would lead to improved thermal comfort and building energy savings. Also, very little dependence on the volume of soil would require little structural load consideration thereby leading not only to cheaper green roof construction but their higher demand, adoption and implementation in SSA and other low-income economies of the global south.

The reduction of the consumption of topsoil and water for irrigation could avoid the negative environmental impacts of land degradation and pollution which have a deleterious impact on human health. This fulfils SDG 12 which seeks to ensure responsible consumption of products. This requires the need to advance the research for improvement and training of local built environment practitioners with new skills for installation to ensure social inclusiveness in the combat against the intractable forces of negative climate impacts.

Climbers are mostly known for green walls, but their innovative use for green roof systems has not been attempted and adopted; it could present a cost-effective strategy for the GEB. The proposed green roof system with climbers apart from becoming a successful strategy for UHI adaptation was also able to record an estimated 568% savings on topsoil consumption with an impact on the reduction of pollution from excavation. The research provides an initial insight into design options, potentials and limitations on the use of climbers for green roofs to guide future research and experimental verification.

Social avoidance disorders can be disruptive to the individual as they begin to over-manage their lives, at times avoiding social and work commitments. A potential therapeutic approach is exposure therapy and a virtual reality (VR) treatment approach, gameChange VR, has been developed. This provides an opportunity to test their fears in virtual environments. This study aims to investigate the feasibility of using this VR approach to treat people with social avoidance problems within a secure mental health setting.

Nine participants completed the gameChange VR sessions. Participants received 30-minute VR sessions for up to six weeks using selected scenarios at five levels of difficulty. Patients’ comments and behaviours were noted and clinical staff also provided feedback on their observations of patients’ use and experience with VR.

Overall, patients felt that the gameChange VR helped them to build their confidence and reduce their overall anxiety as they became more comfortable with the equipment. Both patients and staff generally found the intervention easy to use and the staff reported an overall high level of engagement among the participants. The key issues raised largely related to technical and safety issues.

While a previous study was carried out using gameChange VR with an outpatient cohort, this was the first using an inpatient group in a secure mental health setting. It has demonstrated that the intervention is viable in this setting, although further studies are required to identify the specific patient population that would benefit optimally from the therapy.

The urgency to address climate change and its devastating consequences has never been more pressing. As societies become increasingly aware of the detrimental impact of traditional housing on the planet, there is a growing demand for eco-friendly housing solutions that prioritize energy efficiency, resource conservation and reduced carbon emissions. Therefore, this study aims to investigate the factors that influence customers’ priority toward eco-friendly house purchasing intention.

This study collected 386 data using a quantitative research strategy and purposive sampling method. This study uses a hybrid analysis technique using partial least squares structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA) approaches to identify the influencing factors.

The PLS-SEM analysis found that attitude toward the eco-friendly house, subjective norms, performance expectancy, environmental knowledge and environmental sensitivity have a positive influence on eco-friendly house purchasing intention. However, perceived behavioral control and willingness to pay were found to have insignificant effect on customers’ intention to purchase eco-friendly houses. The fsQCA results further revealed complex causal relationships between the influencing factors.

This research will not only contribute to academic knowledge but also provide practical guidance to real estate developers, policymakers and individuals looking to make environmentally responsible choices. By understanding the factors that influence consumers’ intentions to purchase eco-friendly houses, we can pave the way for a more sustainable and resilient future.

This study has used a hybrid analysis technique, combining PLS-SEM and fsQCA, to enhance the predictive accuracy of eco-friendly house purchase intentions among individuals residing in densely populated and highly polluted developing countries, such as Bangladesh.

As video-based interventions are continuously utilized as alternatives to physical site visits, directing students' attention to specific learning contents within videos could increase their comprehension and stimulate their interest. Students' knowledge of construction practice can be reinforced, misconceptions and improper inferences can be reduced by calling out significant learning concepts. However, few studies have formalized practice concepts that could be beneficial in preparing students for the workplace. This paper presents an investigation of construction practice concepts, based on site visits that would be beneficial in complimenting classroom teaching to prepare students for the realities of practice.

A mixed methods research approach was employed combining qualitative and quantitative data collection and analysis. An online questionnaire, semi-structured interviews and a focus group were conducted with industry practitioners and instructors to identify the topics and practice concepts significant for supporting classroom teaching with site visits.

The findings suggest that the most relevant topics typically supported with site visits are preconstruction management, excavation and foundation work, construction equipment, construction means and methods, project management, road construction, sustainability, building systems, structures, construction technology, building construction, capstone, site logistics and safety. Practice concepts were identified for each of these topics.

The study will guide researchers in the design of video-based pedagogical tools to be used as an effective complement of or alternative to site visit experiences. The findings will support instructors on how to structure their teaching practices to prepare students for some of the complexities of the workplace.

This study adds value to the existing literature by providing insights into industry perception of practice concepts for complementing classroom teaching.

Incipient detection of pavement deterioration (such as crack identification) is critical to optimizing road maintenance because it enables preventative steps to be implemented to mitigate damage and possible failure. Traditional visual inspection has been largely superseded by semi-automatic/automatic procedures given significant advancements in image processing. Therefore, there is a need to develop automated tools to detect and classify cracks.

The literature review is employed to evaluate existing attempts to use Hough transform algorithm and highlight issues that should be improved. Then, developing a simple low-cost crack detection method based on the Hough transform algorithm for pavement crack detection and classification.

Analysis results reveal that model accuracy reaches 92.14% for vertical cracks, 93.03% for diagonal cracks and 95.61% for horizontal cracks. The time lapse for detecting the crack type for one image is circa 0.98 s for vertical cracks, 0.79 s for horizontal cracks and 0.83 s for diagonal cracks. Ensuing discourse serves to illustrate the inherent potential of a simple low-cost image processing method in automated pavement crack detection. Moreover, this method provides direct guidance for long-term pavement optimal maintenance decisions.

The outcome of this research can help highway agencies to detect and classify cracks accurately for a very long highway without a need for manual inspection, which can significantly minimize cost.

Hough transform algorithm was tested in terms of detect and classify a large dataset of highway images, and the accuracy reaches 92.14%, which can be considered as a very accurate percentage regarding automated cracks and distresses classification.