Socio-Economic Factors Affecting the Income of Small-scale Agroforestry Farms in Hill Country Areas in Yemen: A comparison of OLS and WLS Determinants

Yemen is a less-developed country in the Arabian Peninsula, with only 3% arable land. An agroforestry land-use system has been practiced traditionally by small-scale farmers, but is associated with low productivity and income. A study has been undertaken to determine the socio-economic attributes of farmers that influence the financial performance of agroforestry and nonagroforestry farms in the Bura’a Mountain region. A survey was conducted of 150 farmers involved in both agroforestry and non-agroforestry. Both OLS and WLS regression were applied, and coefficients compared in terms of consistency and goodness of fit. Incomes of farmers were found to be influenced by education, area of land, livestock holding, family size, and whether coffee is grown, but not farmer’s age. The WLS method produced efficient and consistent results, whereas OLS regression was affected by the heteroscedasticity. The findings of the study indicate that the farmers of the study area are in need of financial and technical support from government to increase their income. Infrastructural development and public intervention in developing farmers’ technical know-how could enhance production and ensure the optimum use of land as well as soil and water conservation

The Five Factor Model of personality and evaluation of drug consumption risk

The problem of evaluating an individual's risk of drug consumption and misuse is highly important. An online survey methodology was employed to collect data including Big Five personality traits (NEO-FFI-R), impulsivity (BIS-11), sensation seeking (ImpSS), and demographic information. The data set contained information on the consumption of 18 central nervous system psychoactive drugs. Correlation analysis demonstrated the existence of groups of drugs with strongly correlated consumption patterns. Three correlation pleiades were identified, named by the central drug in the pleiade: ecstasy, heroin, and benzodiazepines pleiades. An exhaustive search was performed to select the most effective subset of input features and data mining methods to classify users and non-users for each drug and pleiad. A number of classification methods were employed (decision tree, random forest, $k$-nearest neighbors, linear discriminant analysis, Gaussian mixture, probability density function estimation, logistic regression and na{\"i}ve Bayes) and the most effective classifier was selected for each drug. The quality of classification was surprisingly high with sensitivity and specificity (evaluated by leave-one-out cross-validation) being greater than 70\% for almost all classification tasks. The best results with sensitivity and specificity being greater than 75\% were achieved for cannabis, crack, ecstasy, legal highs, LSD, and volatile substance abuse (VSA).Comment: Significantly extended report with 67 pages, 27 tables, 21 figure

Determinants of Carbon Emission Disclosures and UN Sustainable Development Goals: The Case of UK Higher Education Institutions

In recent years, organisational sustainability has become a topical issue in many institutional fields and a number of calls have been made to improve the disclosure of carbon information as part of sustainability efforts. This paper responds to these calls, chiefly examining the determinants of (CED) in the annual reports of UK higher education institutions (HEIs). It also aims to predict the relationship between the extent of CED and UN Sustainable Development Goals (SDGs) reporting by UK universities. We construct a disclosure index to capture the extent and type of CED in the annual reports of UK HEIs, finding that carbon reduction targets imposed by the Government, environmental audit, and the amount of actual carbon emissions are significant and positively associated with CED. However, we find no relationship between CED and the disclosure of SDGs. We argue that HEIs'. CED can be useful in developing relevant regulatory policies given the targets are carefully set. Our research has important implications for policymakers regarding carbon reduction targets and related non?mandatory guidance, as these can be utilised as an effective mechanism in increasing carbon emission disclosure voluntary CED that are integrated into SDG disclosures