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Despite worldwide climate change and the problems caused by using fossil fuels, energy consumption in the world keeps rising every year. The areas with extremely cold or scorching climates are large, and significant amounts of energy are getting used in these areas for heating, cooling, and ventilation. The general purpose of this study is to investigate the possible relationship between the climatic characteristics of the Esfahak, a village located in the hot desert region of Iran, and the physical characteristics of its built environment.

The method of this research is qualitative and somewhat descriptive-analytical. In this regard, the architectural features of Esfahak village are compared with the principles mentioned in the Mahoney tables to determine the degree of compliance of the architecture of this village with the climatic condition.

The results show that design principles have been used in all indicators discussed in the Mahoney tables. By applying these principles, not only did the acute weather conditions not prevent the initial settlement in the village location, they have not caused inhabitants to leave the site over time as well.

The impacts of bioclimatic design strategies on thermal comfort in hot desert regions are seldom studied. This research provides evidence-based and informed design recommendations that can help building designers and city authorities integrate bioclimatic design strategies at the earliest conceptual design phases in hot desert climates.

Climate change increases systematic risk for firms, especially those in the agricultural industry. Therefore, the need to examine the consequences of climate-related risks on agribusiness companies' financial performance across the globe and emerging markets has risen. In this context, the paper aims to investigate the effects of climate change risks on the financial performance of agriculture listed firms in Vietnam.

The study sample includes 77 Vietnamese listed firms in the agricultural industry in the period of 2015–2019. The authors chose temperature, wind, rainfall and humidity proxies to measure climate change. The OLS regression, random regression and sub-sample analysis have been used to examine the impacts of climate risks on firms' financial performance.

Empirical results show that rain and temperature have positive impacts on financial performance of Vietnamese agriculture listed firms, while wind and humidity have insignificant impacts on financial performance.

The research helps researchers, businesses, practitioners and policymakers interested in the agricultural industry, especially those in developing and emerging countries, to develop a deep understanding of the impact of climate change risks on firm performance and therefrom prepare necessary measures to reduce the negative impacts.

This study adds to the literature stream on the impacts of climate change on financial performance. It is the first study to investigate this impact in Vietnam, a country which depends mainly on agriculture.

The purpose here is to show how the “shadow” economy has grown in scale and impetus in recent years, though even before modern times it has been present (e.g. the City of London, Shaxson, 2011) since at least the middle ages. The reasons for this have become complicated, but we can identify some “deep structures” that are common. Firstly, “globalisation” made it easier for multinationals to escape national regulatory regimes. Secondly, one of the ways neoliberal trading regulations allowed such actors to augment their assets was by means of what they initially called “transfer-pricing” but which now is officially known as “profit shifting” through tax havens. Thirdly, the growth in international trade in legal and illegal ways caused money laundering – even by otherwise respectable banks – to grow across borders. Conversely, from the supply-side, tax haven status was increasingly accessed by jurisdictions that sought to achieve economic growth by supplying tax haven services, both Delaware and Ireland as exemplars of a “developmental” fiscal policy.

This paper adopts a “pattern recognition” design, an approach that is abductive, meaning interpretive, as shown in the observation that explanation can be valid or reliable without direct observation. This is shown in the indirect observation that “rain fell because the terrace has puddles” or “ancient glaciers once carved this valley”.

Reviewing the European Union’s (EU) list of non-co-operating jurisdictions in support of the OECD’s review of base erosion and profit-shifting activity, Collin concluded the EU’s listing “moved the needle” somewhat but was only a modest success. This is because of its reluctance to sanction its own members or large economies like the USA. Data on foreign direct investment and offshore banking assets suggest listed jurisdictions did not suffer notably from being named and shamed. In all cases studied, this contribution found legally damaging, fraudulent, conflict of interest and corrupt practice activities everywhere.

The originality is found in three spheres. Firstly, the pattern recognition method was vindicated in yielding hard to research results. Secondly, the “assemblage-thirdspace” theory was found advantageous in demonstrating the uneven geography of tax haven clusters and their common history in turbocharging economic development. Finally, the empirics showed the ruses executed by cluster members in tax havens to circumvent the law from global management consultancies to micro-firms consisting of tax lawyers and other experts interacting in knowledge supply chains of dubious morality.

Modern human society has continuous advancements that have a negative impact on the quality of the air. Daily transportation, industrial and residential operations churn up dangerous contaminants in our surroundings. Addressing air pollution issues is critical for human health and ecosystems, particularly in developing countries such as Egypt. Excessive levels of pollutants have been linked to a variety of circulatory, respiratory and nervous illnesses. To this end, the purpose of this research paper is to forecast air pollution concentrations in Egypt based on time series analysis.

Deep learning models are leveraged to analyze air quality time series in the 6th of October City, Egypt. In this regard, convolutional neural network (CNN), long short-term memory network and multilayer perceptron neural network models are used to forecast the overall concentrations of sulfur dioxide (SO₂) and particulate matter 10 µm in diameter (PM₁₀). The models are trained and validated by using monthly data available from the Egyptian Environmental Affairs Agency between December 2014 and July 2020. The performance measures such as determination coefficient, root mean square error and mean absolute error are used to evaluate the outcomes of models.

The CNN model exhibits the best performance in terms of forecasting pollutant concentrations 3, 6, 9 and 12 months ahead. Finally, using data from December 2014 to July 2021, the CNN model is used to anticipate the pollutant concentrations 12 months ahead. In July 2022, the overall concentrations of SO₂ and PM₁₀ are expected to reach 10 and 127 µg/m³, respectively. The developed model could aid decision-makers, practitioners and local authorities in planning and implementing various interventions to mitigate their negative influences on the population and environment.

This research introduces the development of an efficient time-series model that can project the future concentrations of particulate and gaseous air pollutants in Egypt. This research study offers the first time application of deep learning models to forecast the air quality in Egypt. This research study examines the performance of machine learning approaches and deep learning techniques to forecast sulfur dioxide and particular matter concentrations using standard performance metrics.

The growing availability of naturalistic driving datasets (NDDs) presents a valuable opportunity to develop various models for autonomous driving. However, while current NDDs include data on vehicles with and without intended driving behavior changes, they do not explicitly demonstrate a type of data on vehicles that intend to change their driving behavior but do not execute the behaviors because of safety, efficiency, or other factors. This missing data is essential for autonomous driving decisions. This study aims to extract the driving data with implicit intentions to support the development of decision-making models.

According to Bayesian inference, drivers who have the same intended changes likely share similar influencing factors and states. Building on this principle, this study proposes an approach to extract data on vehicles that intended to execute specific behaviors but failed to do so. This is achieved by computing driving similarities between the candidate vehicles and benchmark vehicles with incorporation of the standard similarity metrics, which takes into account information on the surrounding vehicles' location topology and individual vehicle motion states. By doing so, the method enables a more comprehensive analysis of driving behavior and intention.

The proposed method is verified on the Next Generation SIMulation dataset (NGSim), which confirms its ability to reveal similarities between vehicles executing similar behaviors during the decision-making process in nature. The approach is also validated using simulated data, achieving an accuracy of 96.3 per cent in recognizing vehicles with specific driving behavior intentions that are not executed.

This study provides an innovative approach to extract driving data with implicit intentions and offers strong support to develop data-driven decision-making models for autonomous driving. With the support of this approach, the development of autonomous vehicles can capture more real driving experience from human drivers moving towards a safer and more efficient future.

Effective agricultural product price regulation policies depend on market integration and the degree of symmetry in the transmission of agricultural product price signals. This study analyzes the transmission and asymmetry of the price series between the Ouagadougou consumer market and assembly markets considering three primary cereal products in Burkina Faso.

This study applies the nonlinear autoregressive distributed lag (NARDL) econometric model, which is an asymmetric extension of the ARDL cointegration model. The price series examined covers the period extending from January 2005 to December 2020.

Our analysis provides novel insights regarding short- and long-term asymmetric effects in the transmission of price signals between assembly markets and the consumer market. We also determine that the effects of negative shocks are more persistent than those of positive shocks in several markets.

For markets that exhibit symmetrical responses of assembly market prices to consumer market prices, the results could reflect the continuous efforts of market players, particularly the government, to eliminate market failures and ensure the long-term efficiency of cereal markets. To this end, an agricultural market information system can have a crucial role in easing information access for all market players.

This study provides new evidence regarding the nature of the transmission and asymmetry of price information on primary cereal products in the largest markets in Burkina Faso. Applying the NARDL model makes it possible to simultaneously estimate short- and long-term asymmetry.

The purpose of this study is to evaluate whether a diversification by grape varieties may help wine producers reduce uncertainty in quantity and quality variations due to increasingly erratic climate conditions.

This study hand-collects granular quantity and quality data from wine harvest reports for vintages 2003 to 2017 for the Valais region in Switzerland. The data allows us to obtain detailed data on harvested kilograms/liters and Oechsle/Brix degrees. It is then merged with precise meteorological data over the same sample period. The authors use this data set to capture weather conditions and their impact on harvested quantities and quality. Finally, they build portfolios including different grape varieties to evaluate whether this reduces variations in quality and quantity over vintages.

The findings highlight that the weather varies relatively strongly over the sample period and that climate hazards such as hail, frost or ensuing vine diseases effectively occur. These strongly impact the harvested quantities but less the quality of the wine. The authors further show that planting different grape varieties allows for a significant reduction in the variation of harvested quantities over time and thus acts as a good solution against climate risk.

The effect of climate change on viticulture is becoming increasingly important and felt and bears real economic and social consequences. This study transposes portfolio diversification which is central to reducing risk in the finance industry, into the wine industry and shows that the same principle holds. The authors thus propose a novel idea on how to mitigate climate risk.

The effects of dust exposure in buildings and its health and comfort consequences continue to concern occupants, particularly those who spend most of their time indoors. This study examines the influence of building opening characteristics on surface dust loading in indoor environments to determine the dust particles' impact on different opening configurations.

Indoor Harmattan dust surface loading data were collected from Maiduguri, Northeastern Nigeria, using model rooms with six different window configurations. A simple mathematical relationship was employed to assess surface dust loading characteristics in the model rooms. The study measured dust thrice between December and February for three days (72 h). The results were analyzed using descriptive statistics.

The results determined the highest average surface dust loading of 12.03 g/m2 in the room with awning windows at an indoor-to-outdoor (I/O) ratio of 0.7. In contrast, the experiment in the room with a closed window recorded the lowest average surface dust loading of 5.24 g/m2 at an I/O ratio of 0.30, which is infiltration. The outcomes further indicate that the average surface dust loading varies with the building opening type and position, as higher surface dust loadings were recorded in locations closer to the openings (doors and windows), reaffirming that the dominant source of the dust particles is outdoors. According to the study, dust incursion due to infiltration accounts for 30% of the outdoor surface loading.

Thus, Harmattan dust is a serious challenge to the health, productivity and hygiene of building occupants in the study area. The built-environment professionals must use the study's outcome to optimize building openings' designs (shape, size and form) for effective indoor dust control.

Poor post-harvest handling practices by seaweed farmers are a key issue in seaweed value chains, contributing to low-quality seaweed being supplied to processors. To address this, a range of advanced drying technologies and methods have been developed, yet uptake by farmers remains low. This study examines factors affecting drying technology uptake by seaweed farmers to identify opportunities to incentivise improved drying practices.

This study draws on a quantitative survey of 273 seaweed farmers in two villages in South Sulawesi, 16 months of ethnographic fieldwork and 166 semi-structured interviews.

Farmers engage in limited adoption of improved drying technologies and practices as they don't receive higher prices for higher quality products, instead aiming to meet only the minimum acceptable standards to avoid a price discount or rejection of their product. Technologies and techniques that have been adopted are often used in ways that differ from their original purpose, such as to reduce drying times and labour input, rather than to produce products of low moisture and dirt contents. Similarly, local traders mix high- and low-quality seaweed in order to supply warehouses with seaweed which on average meets minimum quality standards.

This study reveals that improved drying practices are unlikely to be adopted unless incentivised by more targeted price-grade differentials.

This research aims to systematically review studies on significant risks for Critical Infrastructure Projects (CIPs) from selected top-tier academic journals from 2011 to 2023.

In this research, a three-step systematic literature review methodology was employed to analyse 55 selected articles on Critical Infrastructure Risks (CIRs) from well-regarded and relevant academic journals published from 2011 to 2023.

The findings highlight a growing research focus on CIRs from 2011 to 2023. A total of 128 risks were identified and grouped into ten distinct categories: construction, cultural, environmental, financial, legal, management, market, political, safety and technical risks. In addition, literature reviews combined with questionnaire surveys were more frequently used to identify CIRs than any other method. Moreover, oil and gas projects were the subjects most often explored in the reviewed papers. Furthermore, it was observed that publications from Iran, the USA and China dominated CIRs research, making significant contributions, accounting for 49.65% of the analysed articles.

This research specifically focuses on five types of CIPs (i.e. roadways, bridges, water supply systems, dams and oil and gas projects). Other CIPs like cyber-physical systems or electric power systems, were not considered in this research.

Governments and contracting firms can benefit from the findings of this study by understanding the significant risks associated with the execution of CIPs, irrespective of the nation, industry or type of project. The results of this investigation can offer construction professionals valuable insights to formulate and implement risk response plans in the early stages of a project.

As a novel literature review related to CIRs, it lays the groundwork for future research and deepens the understanding of the multi-faceted effects of these risks, as well as sets practical response strategies.