This study examines the effects of energy resources in the form of clean and unclean energy on the economic progress of 30 selected developed economies. This study used data from 1990 to 2020 and it employs the CS-ARDL method to obtain results. The results present that both clean and unclean energy significantly stimulate economic progress. The findings further expose that foreign investment resources in the form of inflow are significant factors that accelerate economic progress in developed economies. The results reveal that tourism development, capital accumulation, and exports are significant factors in boosting economic progress in the selected economies. Estimates from Dumitrescu and Hurlin's method for heterogeneous panels confirm the presence of the feedback-effect hypothesis for unclean energy, while the energy-conservation hypothesis holds for clean energy. This study suggests that targeting low-cost clean energy production is crucial for promoting economic growth and protecting the environment through carbon mitigation strategies. There is also a need to develop a policy framework that emphasizes the transformation of industry towards clean energy at a macro level. Furthermore, transitioning from unclean to clean energy may enhance economic progress by improving environmental quality norms in the selected developed economies. Finally, policies for tourism development, export improvement, and increased inflow of FDI should be directed towards fostering clean energy agreements and achieving environment-friendly economic progress in developed economies.

Background: With significant numbers of individuals in the criminal justice system having mental health problems, court-based diversion programmes and liaison services have been established to address this problem.Aims: To examine the effectiveness of the New South Wales (Australia) court diversion programme in reducing re-offending among those diagnosed with psychosis by comparing the treatment order group with a comparison group who received a punitive sanction.Method: Those with psychoses were identified from New South Wales Ministry of Health records between 2001 and 2012 and linked to offending records. Cox regression models were used to identify factors associated with re-offending.Results: A total of 7743 individuals were identified as diagnosed with a psychotic disorder prior to their court finalisation date for their first principal offence. Overall, 26% of the cohort received a treatment order and 74% received a punitive sanction. The re-offending rate in the treatment order group was 12% lower than the punitive sanction group. ‘Acts intended to cause injury’ was the most common type of the first principal offence for the treatment order group compared with the punitive sanction group (48% v. 27%). Drug-related offences were more likely to be punished with a punitive sanction than a treatment order (12% v. 2%).Conclusions: Among those with a serious mental illness (i.e. psychosis), receiving a treatment order by the court rather than a punitive sanction was associated with reduced risk for subsequent offending. We further examined actual mental health treatment received and found that receiving no treatment following the first offence was associated with an increased risk of re-offending and, so, highlighting the importance of treatment for those with serious mental illness in the criminal justice system.

This article presents an improved class of efficient estimators aimed at estimating the finite population variance of the study variable. These estimators are especially useful when we have information about the minimum/maximum values of the auxiliary variable within a framework of simple random sampling. The characteristics of the proposed class of estimators, including bias and mean squared error ($MSE$) under simple random sampling are derived through a first-order approximation. To assess the performance and validate the theoretical outcomes, we conduct a simulation study. Results indicate that the proposed class of estimators has lower $MSEs$ as compared to other existing estimators across all simulation scenarios. Three datasets are used in the application section to emphasize the effectiveness of the proposed class of estimators over conventional unbiased variance estimators, ratio and regression estimators, and other existing estimators.

Offender populations are noted for the high numbers of individuals with mental illness, including serious mental illness, such as psychosis.^1-3 The high costs of incarceration combined with its questionable rehabilitative and deterrent qualities have prompted moves to consider community-based alternatives for certain offender groups,⁴ including diversion into treatment for those with mental illness.⁵ Internationally, studies have found reduced rates of reoffending and other positive outcomes in those diverted by the courts into community-based mental health treatment.^6-8 Albalawi et al⁹ recently reported the results of a population-based data-linkage study finding that reoffending was reduced in individuals with psychosis who received a treatment order from the local courts compared with those receiving punitive sanctions (eg, fines, community orders, bonds). Several studies have examined the effect of mandating contact with mental health services on reoffending as part of transitioning from prison to the community.^10-12 In addition to these studies, psychotropic medication use has been shown to reduce violent and nonviolent offenses in individuals with psychosis.^13-15

The emission of carbon dioxide (CO₂) is considered one of the main factors responsible for one of the greatest challenges faced by the world today: climate change. On the other hand, with the increase in energy demand due to the increase in population and industrialization, the emission of CO₂ has increased rapidly in the past few decades. However, the world’s leaders, including the United Nations, are now taking serious action on how to minimize the emission of CO₂ into the atmosphere. Towards this end, accurate modeling and monitoring of historical CO₂ can help in the development of rational policies. This study aims to analyze the carbon emitted by the Group Twenty (G20) countries for the period 1971–2021. The datasets include CO₂ emissions, nonrenewable energy (NREN), renewable energy (REN), Gross Domestic Product (GDP), and Urbanization (URB). Various regression-based models, including multiple linear regression models, quantile regression models, and panel data models with different variants, were used to quantify the influence of independent variables on the response variable. In this study, CO₂ is a response variable, and the other variables are covariates. The ultimate objective was to choose the best model among the competing models. It is noted that the USA, Canada, and Australia produced the highest amount of CO₂ consistently for the entire duration; however, in the last decade (2011–2021) it has decreased to 12.63–17.95 metric tons per capita as compared to the duration of 1971–1980 (14.33–22.16 metric tons per capita). In contrast, CO₂ emissions have increased in Saudi Arabia and China recently. For modeling purposes, the duration of the data has been divided into two independent, equal parts: 1971–1995 and 1996–2021. The panel fixed effect model (PFEM) and panel mixed effect model (PMEM) outperformed the other competing models using model selection and model prediction criteria. Different models provide different insights into the relationship between CO₂ emissions and independent variables. In the later duration, all models show that REN has negative impacts on CO₂ emissions, except the quantile regression model with tau = 0.25. In contrast, NREN has strong positive impacts on CO₂ emissions. URB has significantly negative impacts on CO₂ emissions globally. The findings of this study hold the potential to provide valuable information to policymakers on carbon emissions and monitoring globally. In addition, results can help in addressing some of the sustainable development goals of the United Nation Development Programme.