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This study aims to determine how changes in macroeconomic indicators and the housing prices index (HPI) are related. These factors can cause short-term and long-term changes in the housing market in Spain.

The study used cointegrating regression, fully modified ordinary least squares and dynamic ordinary least squares methodologies. The models are trained using quarterly time series data for these parameters from 2010 to 2022. A comprehensive examination is conducted to explore the relationship between macroeconomic issues and fluctuations in the HPI.

The results indicate statistically significant short-run effects (p < 0.05) of economic growth, inflation, Spanish stock indices, foreign trade and the interest rate on HPI. The inflation variables, Spain’s stock indices, interest rate and monetary rate, have statistically significant long-run effects (p < 0.05) on HPI. The exchange rate, unemployment and money supply have no substantial impact on HPI in Spain.

The study’s findings significantly contribute to increased information concerning the level of investing activity in the Spanish housing sector. After conducting an in-depth study of both the long-run and short-run connections with HPI, the study proved to be highly effective in formulating appropriate policies.

Despite the present focus on improving the resilience of homes to flooding in UK flood risk management policy and strategy, a general measurement framework for determining levels of flood resilience in UK homes does not exist. In light of this, the aim of this study was to develop a means to evaluate the levels of resilience in flood-prone homes from the perspective of homeowners'.

A quantitative research methodology was employed, with empirical data obtained through a postal survey of homeowners who had experienced flooding. The responses received were then analysed using a combination of statistical techniques including agreement/reliability tests and multiple regression to develop a model of flood resilience.

A predictive model was developed that allows the resilience of a property to be quantified and measured as perceived by homeowners. The findings indicate that the main factors found to influence the level of flood resilience were: property type (PT), presence of cellar/basement (C/B), property wall type (PWT), property ground floor type (PGFT), kitchen unit type (KU), flood experience (FE), flood source (FS) and flood risk level (FRL).

The resulting model provides unique insights into resilience levels to the benefit of a range of stakeholders including policy makers (such as Defra/Environment Agency), Local Authority flood teams, property professionals, housing associations and homeowners. As a result, homeowners will be in a better position to determine which interventions should be prioritised to ensure better flood protection.

This is the first study of its kind to have rigorously quantified the level of flood resilience for individual homes. This study has quantified the effectiveness of individual resilience measures to derive the first reliable means to measure the overall levels of resilience at the individual property level. This is regarded as a significant contribution to the study of flood risk management through the quantification of resilience within individual UK homes, enabling the prioritisation of interventions and the overall monitoring of resilience.

This paper aims to quantitively assess the resilience of residential properties to urban flooding in Port Harcourt, Nigeria, and assess whether they vary at spatially aggregated scales relative to the level of flood exposure.

The study synthesizes theoretical constructs/indicators for quantifying property level resilience, as a basis for measuring resilience. Using a two-stage purposive/stratified randomized sampling approach, 407 questionnaires were sent out to residents of 25 flood-prone areas, to solicit information on the resilience constructs as indicated by the adaptation behaviors of individual households and their property attributes. A principal component analysis approach is used as a mechanism for weighting the indicators, based on which aggregated spatial-scale resilience indices were computed for the 25 sampled areas relative to their levels of flood exposure.

Area 11 located in the moderate flood zone has the lowest resilience index, while Area 20 located in the high flood zone has the highest resilience index. The resilience indices for the low, moderate and high flood zone show only minimal and statistically insignificant differences indicating maladaptation even with incremental levels of flood exposure.

The approach to resilience measurement exemplifies a reproducible lens through which the concept of “living with floods” can be holistically assessed at the property level while highlighting the nexus of the social and technical dimensions.

The study moves beyond theoretical conceptualization, to empirically quantify the complex concept of property-level flood resilience.

Recent United Nations Climate Change Conferences recognise extreme climate change of heatwaves, floods and droughts as threatening risks to the resilience and success of public–private partnership (PPP) infrastructure projects. Such conferences together with available project reports and empirical studies recommend project managers and practitioners to adopt smart technologies and develop robust measures to tackle climate risk exposure. Comparatively, artificial intelligence (AI) risk management tools are better to mitigate climate risk, but it has been inadequately explored in the PPP sector. Thus, this study aims to explore the tools and roles of AI in climate risk management of PPP infrastructure projects.

Systematically, this study compiles and analyses 36 peer-reviewed journal articles sourced from Scopus, Web of Science, Google Scholar and PubMed.

The results demonstrate deep learning, building information modelling, robotic automations, remote sensors and fuzzy logic as major key AI-based risk models (tools) for PPP infrastructures. The roles of AI in climate risk management of PPPs include risk detection, analysis, controls and prediction.

For researchers, the findings provide relevant guide for further investigations into AI and climate risks within the PPP research domain.

This article highlights the AI tools in mitigating climate crisis in PPP infrastructure management.

This article provides strong arguments for the utilisation of AI in understanding and managing numerous challenges related to climate change in PPP infrastructure projects.

The escalating levels of greenhouse gas emissions have become a growing global concern, with household energy consumption emerging as a significant contributor. To develop effective public policies, it is crucial to understand the energy-saving behavior of households. This study delves into the determinants of energy-saving practices in a developing country.

The authors chose a multivariate probit model, as it allowed to look after possible correlations among seven energy-saving practices within households.

The findings underscore the significant influence of sociodemographic variables, such as gender, civil status, income and education, on energy-saving practices. Furthermore, the authors discovered that households where the head actively volunteers in social organizations are more likely to adopt energy-saving behaviors. Additionally, internet access positively contributes to pro-environmental behavior. This research reveals that certain energy-saving practices are interconnected, acting as complements or substitutes.

Recommendations for public policy include prioritizing education in rural areas to boost energy-saving practices, improving internet access in nonurban regions and promoting citizen involvement in social organizations to enhance environmental awareness and encourage energy-saving behavior. The authors contribute to literature evidencing that certain energy-saving practices are not independent of each other, they are rather complementary and, in some cases, substitutes.

Recommendations for public policy include prioritizing education in rural areas to boost energy-saving practices, improving Internet access in nonurban regions and promoting citizen involvement in social organizations to enhance environmental awareness and encourage energy-saving behavior.

Previous studies have overlooked these interdependencies, highlighting the necessity of a system of equations to yield more efficient estimates by considering correlations between error terms.

This study aims to examine the determinants that influence housing prices in Dammam metropolitan area (DMA), Saudi Arabia, by using the analytic hierarchy process (AHP) model. The study considers determinants such as building age (BLD AG), building size (BLD SZ), building condition (BLD CN), access to parking (ACC PK), proximity to transport infrastructure (PRX TRS), proximity to green areas (PRX GA) and proximity to amenities (PRX AM).

The AHP decision model was used to assess the determinants of housing prices in DMA, using a pair-wise comparison matrix to determine the influence of the investigated factors on housing prices.

The study’s results revealed that building size (BLD SZ) was the most critical determinant affecting housing prices in DMA, with a weight of 0.32, trailed by proximity to transport infrastructure (PRX TRS), with a weight of 0.24 as the second most influential housing price determinant in DMA. The third most important determinant was proximity to amenities (PRX AM), with a weight of 0.18.

This study addresses a research gap by using the AHP model to assess the spatial determinants of housing prices in DMA, Saudi Arabia. Few studies have used this model in examining housing price factors, particularly in the context of Saudi Arabia. Consequently, the findings of this study provide unique insights for policymakers, housing developers and other stakeholders in understanding the importance of building size, proximity to transport infrastructure and proximity to amenities in influencing housing prices in DMA. By considering these determinants, stakeholders can make informed decisions to improve housing quality and prices in the region.

Understanding the technical and socioeconomic dimensions to resilience is core to making a business case for property-level flood risk adaptation. The study investigates the socioeconomic factors that impact the technical dimensions to property-level flood risk adaptation, and whether there is a typology of households adopting similar adaptation measures in Port Harcourt.

Exploratory and inferential statistical analysis of data collated from 407 questionnaires was carried out. Using chi-square analysis, significant bivariate associations were sought between the level of uptake of different categories of property level adaptation and the socioeconomic characteristics of households. A two-step cluster analysis was used to explore discernible patterns of households implementing similar adaptation measures. Logistic regression analysis was further used to evaluate the extent to which socioeconomic parameters impact residents' willingness to undertake adaptation measures given the option of relocation.

The chi-square analysis highlighted a lack of significant association between some socioeconomic parameters and the uptake of individual adaptation measures. The regression analysis however showed that the socioeconomic parameters exert varying degrees of influence on the residents' willingness to undertake adaptation measures. Two homogeneous groups of residents with similar socioeconomic characteristics were identified via the cluster analysis but did not translate into strongly discernible adaptation differences/patterns.

The study shows that although socioeconomic parameters to some extent underlie the technical dimensions to flood resilience, there is no distinctive typology of households in Port Harcourt adopting a similar combination of measures.

The study offers insights into understanding property-level flood risk adaptation responses within the context of the developing world.

This study investigated the role of community infrastructure planning in Sarawak, Malaysia, by concentrating on the obstacles that hinder effective governance of community infrastructure planning.

The qualitative approach via case study design was adopted in this study. The Song District, located in the State of Sarawak, Malaysia, was selected as the case study area. Semi-structured interview sessions were conducted with 21 respondents who belonged to the government agencies involved in rural community infrastructure planning and village community representatives to capture their views on community infrastructure development planning in the selected district. The gathered responses were analysed thematically and the outcomes are discussed.

The findings revealed that although the development of community infrastructure in Song District has progressed and has exerted transformative impacts on rural livelihood, several challenges were identified in the delivery of community infrastructure. The four main themes of challenges that emerged from the data were location factors, financial resources, cooperation and collaboration of stakeholders, as well as community involvement. The outcomes disclosed that, in terms of governance, the institutional roles of the community, stakeholders and government agencies must be integrated throughout the rural community infrastructure planning process to resolve the emerging challenges effectively so that the demands of the rural community are met meritoriously.

An effective and efficient community infrastructure planning approach ascertains that the community infrastructure development gaps in rural areas are bridged and brings fruition to the rural communities for their livelihood transformation.

This paper delineates a view on the governance aspect of community infrastructure planning in Sarawak, Malaysia, that is largely untapped. The study outcomes may facilitate practitioners and academics to move forward and recommend improvements in the approach to rural community infrastructure planning.