Public-Private Partnerships (PPPs) are mostly presented as a means to introduce efficient procurement methods and better value for money to taxpayers. However, the complexity of the PPP mechanism, their lack of transparency, accounting rules and implicit liabilities make it often impossible to perceive the amount of public expenditure involved and the long-run impact on taxpayers, providing room for fiscal illusion, i.e., the illusion that PPPs are much less expensive than traditional public investments. This psaper, thanks to a systematic review of the literature on the EU countries experience, tries to unveil the sources of this illusion by looking at the reasons behind the PPPs’ choice, their real costs, and the sources of fiscal risks. The literature suggests that PPPs are more costly than public funding, especially when contingent liabilities are not taken into account, and are employed as mechanisms to circumvent budgetary restrictions and to spend off-balance. The paper concludes that the public sector should share more risks with private sectors by reducing the amount of guarantees, and should prevent governments from operating through a sleight of hand that deflects attention away from off-balance financing, by applying a neutral fiscal recording system.

Resisting the adoption of medical artificial intelligence (AI), it is suggested that this opposition can be overcome by combining AI awareness, AI risks, and responsibility displacement. Through effective integration of public AI dangers and displacement of responsibility, some of these major concerns can be alleviated. The United Kingdom’s National Health Service has adopted the use of chatbots to provide medical advice, whereas heart disease diagnoses can be made by IBM’s Watson. This has the ability to improve healthcare by increasing accuracy, efficiency, and patient outcomes. The resistance may be due to concerns about losing jobs, anxieties about misdiagnosis or medical mistakes, and the consciousness of AI systems drifting more responsibility away from medical professionals. There is hesitancy among healthcare professionals and the general public about the deployment of AI, despite the fact that healthcare is being revolutionised by AI, its uses are pervasive. Participants’ awareness of AI in healthcare, AI risk, resistance to AI, responsibility displacement and ethical considerations were gathered through questionnaires. Descriptive statistics, chi-square tests and correlation analyses were used to establish the relationship between resistance and medical AI. The study’s objective seeks to collect data on primary and public AI awareness, perceptions of risk and feelings of displacement that the professionals have regarding medical AI. Some of these concerns can be resolved when AI awareness is effectively integrated and patients, healthcare providers, as well as the general public are well informed about AI’s potential advantages. Trust is built when, AI related issues such as bias, transparency, and data privacy are critically addressed. Another objective is to develop a seamless integration of risk management, communication and awareness of AI. Lastly to assess how this comprehensive approach has affected hospital settings’ ambitions to use medical AI. Fusing AI awareness, risk management, and effective communication can be used as a comprehensive strategy to address and promote the application of medical AI in hospital settings. An argument made by Chen et al. is that providing training in AI can improve adoption intentions while lowering complexity through the awareness of AI.

This study introduces a cross-country comparative analysis of the role of News Ombudsperson in the public media corporations in Spain and France. It investigates the specific media self-regulatory processes established to reduce reputational risks and increase the trust and credibility of the media organisations. It aims to fill in the gaps in prior research by applying a qualitative framework developed using indicators derived from scholarly work on regulation and governance and media management. The variables selected for the analysis are extracted from prior interdisciplinary research and focus on media self-regulatory processes, complaints management mechanisms, election, reporting procedures, checks and balances, roles, visibility and transparency of News Ombudspersons in two countries which represent the Polarised Pluralist media system category. Research questions are raised in relation to the main variables identified for the comparative analysis. Data were collected from multiple publicly available international sources, including public media organizations databases, national media regulatory authorities, and academic studies. Results reveal cross-country variations. The systematic investigation of different forms of self-regulatory procedures might lead to concrete recommendations and best practice models for media organizations beyond the European Union. Further research could address the role of media audiences as relevant stakeholders in media governance processes.

Fire hazard is often mapped as a static conditional probability of fire characteristics’ occurrence. We developed a dynamic product for operational risk management to forecast the probability of occurrence of fire radiative power in the locally possible near-maximum fire intensity range. We applied standard machine learning techniques to remotely sensed data. We used a block maxima approach to sample the most extreme fire radiative power (FRP) MODIS retrievals in free-burning fuels for each fire season between 2001 and 2020 and associated weather, fuel, and topography features in northwestern south America. We used the random forest algorithm for both classification and regression, implementing the backward stepwise repression procedure. We solved the classification problem predicting the probability of occurrence of near-maximum wildfire intensity with 75% recall out-of-sample in ten annual test sets running time series cross validation, and 77% recall and 85% ROC-AUC out-of-sample in a twenty-fold cross-validation to gauge a realistic expectation of model performance in production. We solved the regression problem predicting FRP with 86% r² in-sample, but out-of-sample performance was unsatisfactory. Our model predicts well fatal and near-fatal incidents reported in Peru and Colombia out-of-sample in mountainous areas and unimodal fire regimes, the signal decays in bimodal fire regimes.

The presence of a crisis has consistently been an inherent aspect of the Supply Chain, mostly as a result of the substantial number of stakeholders involved and the intricate dynamics of their relationships. The objective of this study is to assess the potential of Big Data as a tool for planning risk management in Supply Chain crises. Specifically, it focuses on using computational analysis and modeling to quantitatively analyze financial risks. The “Web of Science—Elsevier” database was employed to fulfill the aims of this work by identifying relevant papers for the investigation. The data were inputted into VOS viewer, a software application used to construct and visualize bibliometric networks for subsequent research. Data processing indicates a significant rise in the quantity of publications and citations related to the topic over the past five years. Moreover, the study encompasses a wide variety of crisis types, with the COVID-19 pandemic being the most significant. Nevertheless, the cooperation among institutions is evidently limited. This has limited the theoretical progress of the field and may have contributed to the ambiguity in understanding the research issue.

The cultivation of red chili in East Java, Indonesia, has significant economic and social impacts, necessitating proactive supply chain measures. This research aimed to identify priority risk agents, develop effective risk mitigation, and enhance supply chain resilience using the SCOR model, House of Risk, Interpretative Structural Modelling (ISM), and synthesis analysis. Examining 238 respondents—including farmers, collectors, wholesalers, retailers, home-agroindustries, and experts—the findings highlight farmers’ critical role in supply chain resilience despite risks from crop failures, weather fluctuations, and pest infestations. Simultaneous planting led to market oversupply and price drops, but accurate pricing information facilitated quick market adaptation. Wholesalers influenced pricing dynamics and income levels, impacting farmers directly. To improve resilience, three main strategies were developed through ten key elements: proactive strategies (real-time SCM tracking, Weather Early Warning Systems, risk management team formation, and training), resistance strategies (partnerships, chili stock reserves, storage and drying technologies, GAP implementation, post-harvest management, agricultural insurance, and Fair Profit Sharing Agreements), and recovery and growth strategies (flexible distribution channels and customizable distribution centers). Furthermore, the study delves into the mediating and moderating effects between variables within the model. This research not only addresses a knowledge gap but also provides stakeholders with evidence to consider new strategies to enhance red chili supply resilience.