Search Results (18)

This study investigates the industrial electricity pricing (IEP) profiles of 22 OECD countries to understand the effect of renewable energy and taxes on overall prices. Clustering analysis was performed on pricing data from the year 2000 to 2018 to observe how prices evolved. Ordinal logit regression analysis was performed to determine possible associations between the clustered groups and the percentage share of renewables generated (REG), specifically linked to wind, solar photovoltaics and solar thermal. Other independent variables indicating economic and market structures were also considered. Clustering results for both prices before and after tax indicated three pricing clusters, termed low, median, and high pricing clusters. IEP in Italy and Germany was found to have the highest effect owing to taxes, while IEP in countries such as the US, Norway, Canada, and Denmark was least affected by taxes. Regression results show positive associations between the clustered profiles and REG. The positive association between the non-taxed component of IEP and a unit increase in REG is 1.41 times, whereas the positive association of overall IEP price (including taxes) and a unit increase in REG is 56.26 times, which is 39.9 times higher. Our results show that REG penetration has had a minimal effect on IEP over the time under consideration, but rather that the taxation on IEP coincides with REG penetration, contributing to IEP increases. Full article

According to the Federal Reserve of the United States, in the second quarter of 2024, American credit card debt reached USD 1.14 trillion, the highest balance ever recorded. In an age of high-interest, complex credit cards, how does financial literacy affect credit card debt repayment? Also, how could financial literacy and education stop the rise in credit card debt in America? To answer these questions, we use microdata from the latest wave of the Survey of Consumer Finances for 2022. We aim to capture the likelihood of credit card repayment behaviors related to the monthly balances owed by 3865 credit card holders. We consider three categories of self-reported credit card payoff behavior: hardly ever, sometimes, and always or almost always. Given the ordinal nature of our outcome variable, we perform a series of likelihood-ratio and Brant tests to assess the assumption of the proportionality of odds across response categories. Following the failure of the tests, we conclude with the selection of a generalized ordered logit/partial proportional odds model that allows us to relax the parallel lines constraint for those variables for which it is not justified. In our logistic regressions, we account for a comprehensive set of demographic characteristics, and from our results, we highlight the following: For credit card holders with low financial literacy, we find that the odds of moving to a higher category of payoff behavior are 21% and significantly lower than those of high financial literacy respondents. Further, for college-educated card holders, the odds of paying off always or almost always versus sometimes and hardly ever are 2.49 times and significantly greater than the odds for credit card holders without a college education. Credit card holders who are minority group members, female, under 45, have dependents, or earn less than USD 50,000 demonstrate a tendency for poor credit card payoff behavior. In our conclusion, we discuss how to improve credit card repayments. We stress the importance of monitoring people closely. We also aim to provide better financial advice to certain groups. Lastly, we present a more realistic approach to building and sustaining financial literacy. Full article

Islamic finance (IF) holds significant potential for economic development and the enhancement of financial inclusion in Uzbekistan. Sukuk, as a key Islamic capital market instrument and Shari’ah-compliant investment alternative, plays an important role in this context. However, the demand for sukuk and its determinants are not well understood by policymakers and industry practitioners in Uzbekistan. This study aims to address this research gap by utilizing an ordinal logit model on primary data collected through a survey of 196 individuals from diverse demographic and professional backgrounds, with varying levels of IF and capital market knowledge and experience. The regression results indicate that factors such as prior investment experience, knowledge of sukuk, and a strong inclination toward Shari’ah-compliant investments positively influence an individual’s intent to buy sukuk. Conversely, we found that residents of Tashkent (the capital city) are less likely to invest in sukuk compared to residents of other regions in Uzbekistan or those residing abroad. Based on this study’s findings, several essential policy and practical recommendations are provided to relevant stakeholders. Full article

Along with the rapid development of industries and the acceleration of urbanisation, the problem of air pollution is becoming more serious. Exploring the relevant factors affecting air quality and accurately predicting the air quality index are significant in improving the overall environmental quality and realising green economic development. Machine learning algorithms and statistical models have been widely used in air quality prediction and ranking assessment. In this paper, based on daily air quality data for the city of Xi’an, China, from 1 October 2022 to 30 September 2023, we construct support vector regression (SVR), gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), random forests (RF), neural network (NN) and long short-term memory (LSTM) models to analyse the influence of the air quality index for Xi’an and to conduct comparative tests. The predicted values and 95% prediction intervals of the AQI for the next 15 days for Xi’an, China, are given based on the Bootstrap-XGBoost algorithm. Further, the ordinal logit regression and ordinal probit regression models are constructed to evaluate and accurately predict the AQI ranks of the data from 1 October 2023 to 15 October 2023 for Xi’an. Finally, this paper proposes some suggestions and policy measures based on the findings of this paper. Full article

This study evaluates the feasibility of a new food delivery service involving only food products with quality certification. In particular, through an ad hoc survey, it evaluates the influence of consumers’ personal characteristics and choice motives on joining this innovative service and the willingness to pay of the respondents. A survey was completely anonymously and voluntarily administered during the SARS-CoV-2 pandemic. A total of 630 answers were collected. Logit and ordinal logit regression were carried out to analyze data. Women and respondents who have more leisure time are more likely to join the service. The analysis of choice motives suggests that consumers more concerned with food quality, and those devoting a higher weekly budget to buying groceries are more likely to be interested in the proposed service. Individuals willing to buy groceries based on certifications and organoleptic properties and people who habitually consume one to five meals outside were more likely to be willing to increase their weekly budget to join the service. To the best of our knowledge, this is the first study evaluating the influence of personal characteristics and choice motives on an innovative food delivery service involving only certified quality products in Italy. Full article

Using public transport as an alternative to private motorized vehicles (PMVs) is becoming increasingly popular in many cities worldwide. To understand what incentives and enhancements are necessary to attract and retain more passengers, a comprehensive understanding of the quality of public transport services is essential. This study aims to broaden the existing literature on the knowledge of public transport services in a large, heterogeneous metropolitan area. A cross-examination between a binary and an ordinal logit model is proposed, using data from a mobility survey in the Porto Metropolitan Area (PMA) in Portugal. The results show that households use PMVs mainly for speed (58.8%), comfort (49.3%), and lack of public transport to the destination (35.7%). Households using public transport cite not driving/owning a PMV (52.6%), lack of alternative transport modes (49.1%), and service cost (38.2%) as primary reasons. The perceived service quality (PSQ) within the PMA exhibits variance on multiple levels, depending on the characteristics of the household and the municipality’s location. This study provides policymakers of different cities in the PMA with insight into what incentives would most effectively increase the PSQ and, in turn, attract more passengers. This insight would be valuable in developing strategies to improve public transport usage and reduce PMV usage in the PMA. Adopting these strategies will contribute to reducing environmental impact and reducing traffic congestion. Full article

Logistic regression is one of statistical methods that used to analyze the correlation between categorical response variables and predictor variables which are categorical or continuous. Many studies on logistic regression have been carried out by assuming that the predictor variable and its logit link function have a linear relationship. However, in several cases it was found that the relationship was not always linear, but could be quadratic, cubic, or in the form of other curves, so that the assumption of linearity was incorrect. Therefore, this study will develop a bivariate polynomial ordinal logistic regression (BPOLR) model which is an extension of ordinal logistic regression, with two correlated response variables in which the relationship between the continuous predictor variable and its logit is modeled as a polynomial form. There are commonly two correlated response variables in scientific research. In this study, each response variable used consisted of three categories. This study aims to obtain parameter estimators of the BPOLR model using the maximum likelihood estimation (MLE) method, obtain test statistics of parameters using the maximum likelihood ratio test (MLRT) method, and obtain algorithms of estimating and hypothesis testing for parameters of the BPOLR model. The results of the first partial derivatives are not closed-form, thus, a numerical optimization such as the Berndt–Hall–Hall–Hausman (BHHH) method is needed to obtain the maximum likelihood estimator. The distribution statistically test is followed the Chi-square limit distribution, asymptotically. Full article

This article discusses the use of drones in Hungary and considers their future penetration, based on the responses to a nationally representative 2021 questionnaire among 200 large-scale farmers engaged in precision farming and in crop production. Both the applied trans-theoretical model (with ordinal logit regression model) and the questionnaire design are suitable for comparison with the results of a similar survey in Germany. In this study, similar results were found for farm size, age, main job and education, but the evidence that higher education in agriculture has the largest positive effect on the use of drones is a novelty. The frequency values obtained for adopting precision technology elements are not fully suitable for classification due to interpretational shortcomings. The use of drones within precision technologies is no longer negligible (17%), but is nevertheless expected to grow significantly due to continuous innovation and the selective application of inputs. The state could play a major role in future uptake, particularly in the areas of training and harmonisation of legislation. Full article

Traffic accidents are inevitable events that occur unexpectedly and unintentionally. Therefore, analyzing traffic data is essential to prevent fatal accidents. Traffic data analysis provided insights into significant factors and driver behavioral patterns causing accidents. Combining these patterns and the prediction model into an accident prevention system can assist in reducing and preventing traffic accidents. This study applied various machine learning models, including neural network, ordinal regression, decision tree, support vector machines, and logistic regression to have a robust prediction model in injury severity. The trained model provides timely and accurate predictions on accident occurrence and injury severity using real-world traffic accident datasets. We proposed an informative negative data generator using feature weights derived from multinomial logit regression to balance the non-fatal accident data. Our aim is to resolve the bias that happens in the favor of the majority class as well as performance improvement. We evaluated the overall and class-level performance of the machine learning models based on accuracy and mean squared error scores. Three hidden layered neural networks outperformed the other models with 0.254 ± 0.038 and 0.173 ± 0.016 MSE scores for two different datasets. A neural network, which provides more accurate and reliable results, should be integrated into the accident prevention system. Full article

This research aims to determine the belief-based drought perceptions and attitudes of farmers in Şanlıurfa, which is in a semi-arid climate regime, and the factors affecting them. The surveys were conducted through face-to-face interviews with farmers selected by a simple random sampling method in 2020. Analyses were performed with ordinal logit regression in STATA. According to the results, while the effects of settlement location, land size, age, and the size of the household were statistically significant to farmers seeing drought, which is the dependent variable, as caused by fate, the effects of income, experience, and education level were insignificant. For the probability of predicting drought for each independent variable in the sequence analysis, the highest probabilities were found among farmers in the Harran Plain, with 21–30 years of experience, from a household of one to four people, with the land area between 5.1 and 10.0 hectares, aged 61 and above, who were primary school graduates, and who had an annual income of less than 25,000 TL ($3561). The subject of drought should be given more place in religious education in the entire research area by prioritizing these groups. It would also be beneficial to organize workshops for the farmers by agricultural consultants, where Islamic scholars would be present to support science and knowledge in terms of faith. This study is the first in this context in Turkey and provides useful data to policymakers for drought-mitigation policies. Full article

Climate change and disruption in the water cycle patterns are leading to water scarcity. This unsustained water provision is drastically affecting the areas of limited water resources. This research has studied the impacts of climate change on water availability and the localized indigenous technique of glacier grafting for sustained water provision. This adaptation strategy helps the water-stressed locality to conserve water for food security. For this reason, 160 self-administered questionnaires were deployed at the household level, and the primary data were analyzed through STATA Software for ordinal logit regression to estimate the results for both restricted and unrestricted models, against the three dependent variables of glacier grafting, glacier melt water and food security. It is found that glacier grafting ensures sustained water provision for irrigation. It increases fertile land and agricultural production to achieve food security. The income of the households from non-/agricultural products leads to afford a better standard of living. The extension of the glacier grafting strategy to curb climatic effects can help global societies to address the food insecurity issue for sustained living. Full article

Despite the impact of flow cessation on aquatic ecology, the hydrology of intermittent rivers has been largely overlooked. This has resulted in a lack of monitoring projects, and consequently, datasets spanning a period of sufficient duration to characterise both hydrological extremes. This report documents an investigation into the potential for statistical modelling to simulate the spatiotemporal dynamics of flowing, ponded and dry hydrological states in an internationally rare hydrological state dataset. The models presented predict unrecorded hydrological state data with performance metrics exceeding 95%, providing insights into the relationship between ponding prevalence and the performance of statistical simulation of this ecologically important intermediate state between drying and flowing conditions. This work demonstrates the potential for hydrological intermittence to be simulated in areas where hydrological state data are often sparse, providing opportunities for quality control and data infilling. This further understanding of the processes driving intermittence will inform future water resource assessments and the influence of climate change on hydrological intermittence. Full article

This study aimed to investigate the association between corporate governance characteristics, shariah governance characteristics, and the credit rating of Asian Islamic banks. To do so, we collected data from 22 banks during the 2006–2018 period. In total, we observed 286 data points. Credit rating was measured through an adaption of the credit rating scale that measured the long term credit of Islamic banks on an ordinal scale. From these data, 19 scores (Aaa) were considered high credit ratings and 1 score (C) was considered a low credit rating. Descriptive statistics, correlations, and the ordered logit regression model were applied in a panel setting. We found that the board interlock, board independence, CEO duality, and board foreign directorship negatively affected credit ratings. We also found that the board size, board accounting, finance knowledge, presence of women on the board, shariah board size, presence of supervisory shariah board, the shariah board interlock, and presence of female shariah scholars all were positively associated with credit ratings. This study suggests that Islamic banks can access more funds with higher shariah compliance. As such, we concluded that evaluating organizations’ credit ratings must consider shariah governance attributes as determinants of the credit rating of Islamic banks. Full article

This study evaluates the impact of unemployment and government financial assistance during the COVID-19 pandemic on the working-age population’s mental health and further examines the differential impacts between urban and non-urban groups, as well as African American (AA) and non-African American groups. Based on the COVID-19 Household Impact Survey, four measures of mental health conditions (nervous, depressed, lonely, and hopeless) are constructed. Our empirical analysis applies the ordinal regression model (ordered logit model) that takes both the week and regional factors into consideration to control for potential time effects and time-invariant confounders varying across regions. The results show that government aid only mitigates the psychological symptoms for the group in non-urban areas, with no significant impacts on the urban group. On the other hand, the AA working-age group experiences similar or more favorable mental health than other ethnic groups, while government aid does not alleviate the mental pressure for the AA group. Therefore, government interventions should recognize the heterogeneity of impacts on socioeconomic groups within the target population. Full article

The Aynak Copper Mine was first discovered in 1973, and mining was initiated by the Metallurgical Company of China in 2009. However, its use has been suspended several times and the mine has never been fully exploited due to political unrest and terrorism, among other issues, in Afghanistan. Afghanistan has been recognized as one of the most fragile, conflict-affected, and landlocked countries in the world by international organizations and the global community, especially after the events of 9/11. Hence, understanding the obstacles influencing the development process of the Aynak Copper Mine is of crucial value in sustaining the development of the overall economy and society in Afghanistan. The aim of this paper is to explore the factors that have caused suspensions of the developments of the Aynak Copper Mine, which may apply to other developing projects in Afghanistan and in other fragile states. The findings will provide a better understanding of the difficulties in maintaining a sustainable environment for developing the regional economy in Afghanistan, and fill a gap in the literature with regards to the applied and theoretical economic growth model in fragile states. The materials of this research are partly based on a radical analysis of the official documents of the Afghan government and international organizations. We adopted statistical analysis to identify the factors associated with the progressive process of developing the Aynak Copper Mine, and an ordinal logit regression to analyze those factors. We specifically considered the factors associated with the degree of administrative capacity, labor investment, labor productivity, capital investment, efficiency of capital, terroristic activities, and religious issues. Among these factors, the relationships between the degree of administrative capacity, terroristic activities, and religious issues were strongly associated with the development status of the Aynak Copper Mine. The other investigated factors were not found to be relevant. This study is among the first on the Mining Project in Afghanistan. Full article