Does Environmental, Social, and Governance Risk Impede Economic Growth?

This article aims to explore the connection between Environmental, Social, and Governance related risks and economic growth. For this purpose, I performed two panel-data regression Models by utilizing the Gross domestic product as a dependent variable and ESG scores and degree of ESG-related risk exposures as independent variables in presence of five control variables. The ESG scores and degree of ESG-related risks exposures were collected from the Risk Indexes for the sample period 2019 to 2021. This research found that the country’s high ESG-related risk exposures negatively influence GDP. This study provides policymakers with important implications of the country’s ESG-related risk exposures in the best interests of the world’s stakeholders including Foreign institutional investors (FIIs)

The Legal Relationship between Universities and their Students – How Accountable and to what Extent in Law are Universities Liable for Student Suicides?

This article addresses how the relationship in law of universities and their students is to be characterised. Its main purpose is to establish where legal liability lies in the case of a student committing suicide, whilst undergoing an educational qualification. Characterisation of the relationship between universities and their students has been included within a number of models which are set out and assessed, the aim being to show the range of characterisation and at the same time point out the defect(s) in each characterisation. This analysis is undertaken in searching for a more meaningful and coherent model of the legal relationship between the university and the student. A legal framework of accountability is essential. Exploration of more conventional ways of addressing this, and alternatives, is required. The article therefore includes for consideration a less fashionable potential classification of a fiduciary relationship between universities and their students. It explores, too, the possibility in public law of founding a breach of a substantive legitimate expectation challenge where promises of safeguarding have been made to the now deceased student. This phenomenon goes beyond the United Kingdom, so reference is made to case law in other jurisdictions, such as Canada and USA

The Effect of Firm-Specific and Industry-Specific Determinants on Automobile Industry Export Performance: The Mediating Role of Supply Chain Performance

This paper analyzes the effect of firm-specific (FSD) and industry-specific determinants (ISD) on supply-chain-performance (SCP), export performance (EP) and SCP’s mediating effect on the relationship between FSD, ISD, and EP. It develops a theoretical framework from literature and empirically validates using the Indian automobile industry segments (IAIS) data. The sample frame consists of firms in ISIS between 2010–11 and 2020–21. The paper employs factor analysis for construct validity, panel-data-fixed-effect models to analyze the relationships, and bootstrap for cross-validation. It reveals that FSD and ISD directly influence both SCP and EP. SCP completely mediates the relationship between FSD, ISD, and EP

Operational Intelligence and the Spanish Guardia Civil’s Carteia Plan: An Exploratory Thematic Analysis of Police Officer Perceptions and Experiences

Introduction: This exploratory study examines the effectiveness of intelligence services in combating drug trafficking in southern Spain, with a focus on the integrated efforts of the Spanish Guardia Civil’s Carteia Plan and its Regional Analysis and Intelligence Center against Drug Trafficking (CRAIN). The purpose is to assess, through the police officers’ experience, how these initiatives enhance operational information collection and intelligence to counteract the pervasive threat of drug trafficking in the Campo de Gibraltar region.Methods: Building on previous research, a qualitative approach was employed to conduct this paper, incorporating a survey and interviews with both law enforcement and intelligence personnel. The study also involved a comprehensive review of policy documents and operational reports from the Guardia Civil and CRAIN to evaluate their integrated intelligence model.Results: The findings highlight that the integrated intelligence and operational strategies conducted by CRAIN in close collaboration with the Guardia Civil’s GAR special operation group significantly improve the efficacy of not only drug interdiction efforts, but also of any strategy against organized crime. The plan’s coordinated initiatives have led to a notable increase in drug seizures and arrests, demonstrating the effectiveness of enhanced intelligence gathering and inter-agency cooperation.Conclusions: The research concludes that an integrated intelligence framework is essential for effectively combating organized crime. The implementation of the Carteia Plan and CRAIN’s functioning underscores the importance of comprehensive and adaptive approaches. Policy recommendations include fostering stronger inter-agency collaboration and promoting a transversal intelligence culture across different operational units, essential to address the dynamic and multifaceted nature of drug trafficking

An Operational Analysis of County Lines and Serious Organised Crime Data From the Police National Database Using i2 Analyst’s Notebook

The Police National Database (PND) is an invaluable National source of cross-border intelligence, yet its data has historically been underexploited for core tasks of network and criminal business analysis. This operational analysis by the National County Lines Coordination Centre explores the investigative scale, depth, results and efficiency benefits of importing and analysing PND data in i2 Analyst’s Notebook (ANB). PND exports of East Midlands, West Midlands and Police Scotland data were imported, merged and deconflicted. The resulting analysis produced several findings using existing data that, while adequately recorded, had not previously been understood in a Regional and National context. These results included identifying vulnerable children and adults, the criminal business model responsible for exploiting them, and the best opportunities for intervention and disruption – as well as identifying previously unknown links between Organised Crime Groups (OCG) and County Lines, improving Police understanding of their criminality and  exploitation. The analysis highlights the investigative opportunities afforded when the value of existing intelligence is maximized using appropriate tools and tradecraft. The i2 tools and tradecraft for PND described herein have been recommended as best practice to all Analysts in the National County Lines Coordination Centre Network

Are English Football Betting Markets Efficient in the Presence of an Exogenous Shock? Testing the Semi-Strong Form of the Efficient Market Hypothesis Given the Effect of the Covid-19 Induced Ghost Games on the English Premier League and the English Football League Championship Football Betting Markets

Football betting markets can be used to test the Efficient Market Hypothesis. This paper adds to literature in this field by investigating the effect of an exogenous shock to the English football betting markets. We analyse whether market expectations were correctly priced during the Covid-19 induced ghost game period. We find that the loss of home advantage is not fully incorporated into the betting odds. Hence, we find evidence of a violation to the semi-strong form of the Efficient Market Hypothesis. Given our analysis we are able to demonstrate some simple betting strategies that bettors could have used to yield high profits. Moreover, we carry out robustness checks with five additional betting providers and produce concordant findings

Machine Learning Models Comparison for Bankruptcy Predication for Indian Companies: A study based on India’s Insolvency and Bankruptcy Code (IBC ‘2016)

It is essential to recognize that dynamics of bankruptcy events vary across regions and legal frameworks. In this context, the paper aims to fill the critical gap in literature by presenting an analysis of machine learning (ML) models for early detection of bankruptcy probability among Indian companies operating under the Insolvency and Bankruptcy Code (IBC) of 2016. This study distinguishes itself by leveraging an extensive dataset covering the period from FY 2016 to FY 2022, encompassing 65,583 entries for 7,008 unique corporations, including 257 bankrupt entities. This paper employs various predictive variables, including traditional financial ratios, Altman Z-scores, and comprehensive financial statement data, employing a scenario-based approach over a one-year forecasting horizon. The findings support the notion that ML models, particularly XGBoost, outperform traditional logistic regression models and Altman Z-scores in accurately predicting bankruptcy among Indian corporates. These findings align with the trend in the literature favoring ML models for enhanced predictive power, offering valuable insights for financial institutions and policymakers in India’s corporate landscape

Price Prediction of S&P 500 Using Machine Learning

Forecasting trends in stock indices is considered a difficult task in financial time series forecasting. Accurate forecasts of stock price trends can generate profits for investors. Due to the complexity of stock market data, developing effective forecasting models is very challenging. We are trying to predict stock prices for the next few days. This will become the basis for knowing the right time to invest or exit positions and generate profits. With the introduction of artificial intelligence and the increase in computing capacity, programmed forecasting methods have proven to be more effective at predicting stock prices. In this work, we used supervised machine learning algorithms such as linear regression model, SVR, XGBoost, and random forest. Thus, these models are evaluated using standard strategic indicators such as the EMR. A low value of the indicator shows that the models are effective in predicting stock prices

Towards a Universal Climate Justice through a Human Rights-Based Approach

The United Nations’ historical recognition of the right to a safe, clean, healthy, and sustainable environment while strengthening the acknowledgement of the link between the protection of human rights and the environment under international law, highlights the urgency of the escalating effects of climate change on people’s lives and their fundamental rights. Along with widespread pollution and biodiversity reduction, climate change is now, in fact, one of the most serious threats to people’s health and their living environment, making it a significant obstacle to the UN 2030 Agenda’s Sustainable Development Goals (SDGs). In light of relevant scientific evidence on the current global warming status and trends, the Intergovernmental Panel on Climate Change (IPCC) has recently emphasised the need to prioritise rights-based approaches in addressing climate change, including mitigation and adaptation measures. Hence, the study aims to explore how advancing a Human Rights-Based Approach (HRBA) towards climate environmental issues may be instrumental in supporting international and national efforts to reduce greenhouse gas emissions, protect people’s rights, and achieve sustainable development. By presenting rights-based climate litigation, it will be further possible to demonstrate how international human rights and climate change law have recently evolved while offering various insights into its impacts on creating a pathway towards universal climate justice

AI and Artists: Navigating Ethics, Regulation, and the Impact of AI on Artistic Practice

Advances in Artificial Intelligence (AI) have captured public interest, especially with the advent of generative AI technologies like ChatGPT and Dall-E. These tools, which create text, images, and videos rapidly and often for free, promise transformative impacts on society, economy, and culture. However, for artists, generative AI raises significant practical, ethical, and philosophical questions. A 2023 survey by DACS revealed artists’ concerns about AI’s impact on their work, data privacy, and the spread of misinformation. While some see AI as a positive tool, many demand safeguards and regulations, emphasising the need for consent and compensation when AI uses their work