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There is a growing concern in recent years regarding climate change risks to real estate in the developed and developing countries. It is anticipated that the property sector could be affected by variable climate and related extremes as well as by the strategies adopted to combat greenhouse gas (GHG) emissions. This paper aims to analyse the current knowledge regarding future climate changes to understand their possible impacts on the real estate sector of Malaysia with an aim to help stakeholders to adopt necessary responses to reduce negative impacts.

Available literature is reviewed and data related to climatic influences on buildings and structures are analysed to understand the climate change impacts on real estate in Malaysia.

The study reveals that temperature in the Peninsular Malaysia will increase by 1.1 to 3.6°C, rainfall will be more variable and river discharge in some river basins will increase up to 43 per cent during the northeast monsoon season by the end of this century. These changes in turn will pose risks of property damage and increase property lifecycle costs. Furthermore, property prices and the overall growth of the property sector may be affected by the government policy of GHG emission reduction by up to 45 per cent by the year 2030. This study concludes that the property sector of Malaysia will be most affected by the implementation of GHG emission reduction policy in the short term and due to the physical risk posed by variable climate and related extremes in the long term.

The study in general will assist in guiding the operational responses of various authorities, especially in terms of those interventions aimed at climate change risk reduction in the property sector of Malaysia.

Changes in climate may have both beneficial and harmful effects on crop yields. However, the effects will be more in countries whose economy depends on agriculture. This study aims to measure the economic impacts of climate change on crop farming in Bangladesh.

A Ricardian model was used to estimate the relationship between net crop income and climate variables. Historical climate data and farm household level data from all climatic zones of Bangladesh were collected for this purpose. A regression model was then developed of net crop income per hectare against long-term climate, household and farm variables. Marginal impacts of climate change and potential future impacts of projected climate scenarios on net crop incomes were also estimated.

The results revealed that net crop income in Bangladesh is sensitive to climate, particularly to seasonal temperature. A positive effect of temperature rise on net crop income was observed for the farms located in the areas having sufficient irrigation facilities. Estimated marginal impact suggests that 1 mm/month increase in rainfall and 10°C increase in temperature will lead to about US$4-15 increase in net crop income per hectare in Bangladesh. However, there will be significant seasonal and spatial variations in the impacts. The assessment of future impacts under climate change scenarios projected by Global Circulation Models indicated an increase in net crop income from US$25-84 per hectare in the country.

The findings of this study indicate the need for development practitioners and policy planners to consider both the beneficial and harmful effects of climate change across different climatic zones while designing and implementing the adaptation policies in the country.

Literature survey of the Web of Science, Science Direct and Google Scholar indicates that this study is the first attempt to measure the economic impacts of climate change on overall crop farming sector in Bangladesh using an econometric model.

The purpose of this paper is to develop a statistical-based model to forecast future domestic water demand in the context of climate change, population growth and technological development in Yellow River.

The model is developed through the analysis of the effects of climate variables and population on domestic water use in eight sub-basins of the Yellow River. The model is then used to forecast water demand under different environment change scenarios.

The model projected an increase in domestic water demand in the Yellow River basin in the range of 67.85 × 10⁸ to 62.20 × 10⁸ m³ in year 2020 and between 73.32 × 10⁸ and 89.27 × 10⁸ m³ in year 2030. The general circulation model Beijing Normal University-Earth System Model (BNU-ESM) predicted the highest increase in water demand in both 2020 and 2030, while Centre National de Recherches Meteorologiques Climate Model v.5 (CNRM-CM5) and Model for Interdisciplinary Research on Climate- Earth System (MIROC-ESM) projected the lowest increase in demand in 2020 and 2030, respectively. The fastest growth in water demand is found in the region where water demand is already very high, which may cause serious water shortage and conflicts among water users.

The simple regression-based domestic water demand model proposed in the study can be used for rapid evaluation of possible changes in domestic water demand due to environmental changes to aid in adaptation and mitigation planning.

Construction projects in Malaysia are often delayed and over budget due to heavy reliance on labor. Linear regression (LR) models have been used in most labor cost (LC) studies, which are less accurate than machine learning (ML) tools. Construction management applications have increasingly used ML tools in recent years and have greatly impacted forecasting. The research aims to identify the most influential LC factors using statistical approaches, collect data and forecast LC models for improved forecasts of LC.

A thorough literature review was completed to identify LC factors. Experienced project managers were administered to rank the factors based on importance and relevance. Then, data were collected for the six highest ranked factors, and five ML models were created. Finally, five categorical indices were used to analyze and measure the effectiveness of models in determining the performance category.

Worker age, construction skills, worker origin, worker training/education, type of work and worker experience were identified as the most influencing factors on LC. SVM provided the best in comparison to other models.

The findings support data-driven regulatory and practice improvements aimed at improving labor issues in Malaysia, with the possibility for replication in other countries facing comparable problems.

Vietnam's construction technology (CT) adoption is low when compared to other countries with similar gross domestic product (GDP) per capita resulting in lesser productivity. The research objectives are: (1) To undertake an extensive literature review on CT adoption challenges; (2) To investigate CT adoption challenges unique to Vietnam's construction sector; and (3) To propose data-driven solutions for a greater rate of CT adoption.

A two-stage descriptive survey method was adopted in alignment with the research aim and objectives. Based on the literature review of 215 articles, a questionnaire was designed and administered to experienced construction managers (CM) to identify whether CT has been adopted, barriers to adoption, drivers, and the most popular CT tools. Descriptive statistics were used to summarize the characteristics of interest in the empirical dataset and SPSS-based inferential statistics to estimate the means, frequency counts, variance and test hypotheses that informed the drawing of conclusions concerning the research objectives.

The popular CT tools identified were Autodesk, Microsoft Office and Primavera. The most influential CT adoption barriers: (1) Unknow`n impact on productivity, (2) Late implementation of software in construction projects, (3) Lack of understanding of importance and needs in the construction industry (4) Lack of funds during budget planning for technological advances and implementation (5) Lack of experts required for technological change, and insufficient skills in the industry.

It is expected that the findings could inform data-driven regulatory and practice reforms targeted at increasing greater uptake of CT in Vietnam with potential for replication in countries facing similar adoption challenges.

The findings are intended to support data-driven regulatory and practice improvements aimed at improving CT adoption in Vietnam, with the possibility for replication in other countries facing comparable problems.

The present study aims to evaluate the effect of climate change on the flood hazard potential in the Kelantan River Basin using current and future scenarios.

The intensity-duration-frequency (IDF) was used to estimate the current 50- and 100-year return period 24-h design rainfall, and the climate change factor (CCF) was used to compute the future design rainfall. The CCF was calculated from the rainfall projections of two global climate models, CGCM1 and CCSM3, with different pre-processing steps applied to each. The IDF data were used in the rainfall-runoff-inundation model to simulate current and future flood inundation scenarios.

The estimated CCF values demonstrate a contrast, whereby each station had a CCF value greater than one for CGCM1, while some stations had a CCF value of less than one for CCSM3. Therefore, CGCM1 projected an aggravation and CCSM3 a reduction of flood hazard for future scenarios. The study reveals that topography plays an essential role in calculating the CCF.

To the best of the author’s knowledge, this is the first study to examine flood projections in the Kelantan River Basin. It is, therefore, hoped that these results could benefit local managers and authorities by enabling them to make informed decisions regarding flood risk mitigation in a climate change scenario.

Despite its devastating nature, the COVID-19 pandemic has also catalyzed a substantial surge in the adoption and integration of technological tools within economies, exerting a profound influence on the dissemination of information among participants in stock markets. Consequently, this present study delves into the ramifications of post-pandemic dynamics on stock market behavior. It also examines the relationship between investors' sentiments, underlying behavioral drivers and their collective impact on global stock markets.

Drawing upon data spanning from 2012 to 2023 and encompassing major world indices classified by Morgan Stanley Capital International’s (MSCI) market and regional taxonomy, this study employs a threshold regression model. This model effectively distinguishes the thresholds within these influential factors. To evaluate the statistical significance of variances across these thresholds, a Wald coefficient analysis was applied.

The empirical results highlighted the substantive role that investors' sentiments and behavioral determinants play in shaping the predictability of returns on a global scale. However, their influence on developed economies and the continents of America appears comparatively lower compared with the Asia–Pacific markets. Similarly, the regions characterized by a more pronounced influence of behavioral factors seem to reduce their reliance on these factors in the post-pandemic landscape and vice versa. Interestingly, the post COVID-19 technological advancements also appear to exert a lesser impact on developed nations.

This study pioneers the investigation of these contextual dissimilarities, thereby charting new avenues for subsequent research studies. These insights shed valuable light on the contextualized nexus between technology, societal dynamics, behavioral biases and their collective impact on stock markets. Furthermore, the study's revelations offer a unique vantage point for addressing market inefficiencies by pinpointing the pivotal factors driving such behavioral patterns.

The purpose of this paper is to boost the existing literature on adaptive market hypothesis (AMH) as it first time links predictability of gold, silver and metal returns with AMH which permits the predictability of returns to vary over time.

To know whether commodity (gold, silver and metal) market is efficient or not, the commodity returns are observed by using appropriate linear time series tests (variance ratio test, runs test and auto-correlation test). To capture the varying efficiency of three commodities, the study employs subsamples of five years and all sub-samples are exposed to linear econometric tests to reveal how market efficiency (independency of returns) has behaved over time.

It is found that the commodity market (gold, silver and metal) is adaptive because fluctuation is observed in the market efficiency. Returns of all three commodities go under the periods of efficiency and inefficiency. Thus, AMH is the better description of behavior of commodity markets than traditional efficient market hypothesis.

Choice of sub-sample in the study is the first limitation as the authors employ a sub-sample comprising five years. Second, commission, fee and taxes (transection cost) are ignored in the study. Finally, the results are reported on the basis of linear econometric tests. In future, longer time period sub-sample analysis is suggested by the study to explore the varying nature of the commodities. Moreover, rolling window analysis may be a more appropriate method to elucidate the idea of AMH in further research. It is further suggested that the method used in the study could be helpful and adapted to examine other commodities (metal and agriculture), bonds and equity markets around the world.

The study will provide a better investment model which can enable the investors to seek more returns in future. Moreover, this research can be extended to explore multiple issues like adaptive behavior of returns from crypto currencies, bonds, stocks and real estate investment trusts.

As all the linear tests reveal that almost all the commodities show inefficient behavior in full sample period, it is clear that past prices widely would be helpful to predict the future prices at NYSE; furthermore, investors can use the time-varying information to reduce the risk of investment at NYSE. The study is helpful for individual investors as well as portfolio managers and brokers to forecast the prices on the bases of findings.

The paper identifies the need to study why behavior of commodity returns varies over time.

This paper aims to enhance the existing literature on adaptive market hypothesis (AMH) as this study first time links the month of Ramadan with AMH that permits the performance of well-known Ramadan effect to fluctuate over time.

To fulfill the purpose, the authors inspect the daily returns of 107 individual firms listed at Pakistan Stock Exchange over the period of 20 years. To explore the varying degree of return predictability during Ramadan, the authors use four different subsamples comprising equal length of observations of five years each. The authors use a GARCH (1,1) regression model which facilitates for time varying nature of volatility in equity returns. To facilitate the non-normal nature of stock return data, the authors use Kruskal–Wallis test statistic.

The authors find that behavior of Ramadan effect evolves over time, as performance of this effect varies from time to time and consistent with AMH. Finally, the paper proposes that AMH is well elucidation of behavior of Ramadan effect than traditional efficient market hypothesis.

First limitation is related to the choice of sub-sample as the study uses a sub-sample of five years. Second, the authors ignore transection cost (commissions, fee and taxes) as it is freely negotiated and varies between 4 and 10% (Khan, 2013). Due to such varying information we ignore the transaction cost. It is suggested that a sub-sample analysis of long period may be a more appropriate method to elucidate the idea of AMH in future research and suggest the current method could be adapted and helpful to examine other calendar and market anomalies in different equity markets.

The paper includes implications for investors to choose a better model for investment. Investors can exploit greater returns in future month of Ramadan periods. Furthermore, the researchers can easily extend the methodology used in the study to address multiple issues like adaptive behavior of returns from bonds, real estate investment trusts, cryptocurrencies and trading rules of strategies.

Study confirms from sample t-test and GARCH (1,1) model that Ramadan effect is present in the full and in certain sub-samples; therefore, based on these discrepancies investors can earn abnormal returns by developing specific investment strategies as investors usually make investments in share according to the religious context of Islamic Calendar. The results provide good references for suitable time of investment in stock market. The findings of this study will be helpful to investors and brokers as well as portfolio managers to capture favorable returns across the Islamic calendar.

The paper identified need to study why behavior of Ramadan effect varies over time. The data set comprises daily returns of 107 individual companies over the period of 20 years to better investigate the varying nature of anomalous effect of month of Ramadan. The findings are valuable for international investors and portfolio managers.

This paper aims to discuss and explore the unique agency issues in Islamic banks which give rise to different agency conflicts exist in Islamic banks as compared to conventional banks. In addition, this paper critically examines agency theory in Islamic banking perspective by incorporating Islamic ethical considerations in the principal–agent setting.

This is a conceptual paper, and the discussions revolve around the review of literature of which important sources have been cited in a way that demonstrates a reasonable understanding of the topic. It attempts to create a discourse around the inclusion of Islamic ethical system in understanding the governance structure of Islamic banks.

This paper concludes that Islamic ethical system embedded in the Islamic banks business activities shapes Islamic banks into organisations that place higher ethical considerations than conventional banks. Therefore, Islamic banks are likely to have less severe agency problems relative to their conventional counterparts.

Because of the chosen research approach, the research results may lack generalisability. Therefore, researchers are encouraged to test the proposed propositions further.

As the discourse generated by the paper, it can ultimately enhance the understanding of Islamic governance structure in the perspective of agency issues.

As the discourse generated by the paper, it can ultimately enhance the understanding of Islamic governance structure in the perspective of agency issues.

The paper attempts to bring to attention the important aspect of principal–agent relationship within the Islamic banking structures and explain the role of incorporating Islamic ethical system in enhancing the understanding of the principal–agent relationship.