Visualising the Optimistic, Realistic, and Pessimistic Financial Distress Outlooks for Airport Operations in Malaysia

This paper aims to visualise three financial distress outlooks using computer simulations. The financial distress exposure for airport operations in Malaysia between 1991 and 2021 is given by Altman Z”-score and modelled by the multivariate generalized linear model (MGLM). Seven determinants contributing to the financial distress from literature are examined. The determinant series are fitted individually by using linear model with time series components and autoregressive integrated moving average models to forecast values for the next 10 financial years. Future short- to long-term memory effects following COVID-19 are apparent in time series plots. In the simulations, the MGLM procedure utilised Gaussian, gamma, and Cauchy probability distributions associated with expectations and challenges of doing business as well as uncertainties in the economy. The underlying trends of realistic, optimistic, and pessimistic financial distress outlooks insinuate that the increasing risk of financial distress of airport operations in Malaysia is expected to continue for the next decade.

Can We Detect Urban‐Scale CO2 Emission Changes Within Medium‐Sized Cities?

The COVID‐19 pandemic resulted in a widespread lockdown during the spring of 2020. Measurements collected on a light rail system in the Salt Lake Valley (SLV), combined with observations from the Utah Urban Carbon Dioxide Network observed a notable decrease in urban CO2 concentrations during the spring of 2020 relative to previous years. These decreases coincided with a ∼30% reduction in average traffic volume. CO2 measurements across the SLV were used within a Bayesian inverse model to spatially allocate anthropogenic emission reductions for the first COVID‐19 lockdown. The inverse model was first used to constrain anthropogenic emissions for the previous year (2019) to provide the best possible estimate of emissions for 2020, before accounting for emission reductions observed during the COVID‐19 lockdown. The posterior emissions for 2019 were then used as the prior emission estimate for the 2020 COVID‐19 lockdown analysis. Results from the inverse analysis suggest that the SLV observed a 20% decrease in afternoon CO2 emissions from March to April 2020 (−90.5 tC hr−1). The largest reductions in CO2 emissions were centered over the northern part of the valley (downtown Salt Lake City), near major roadways, and potentially at industrial point sources. These results demonstrate that CO2 monitoring networks can track reductions in CO2 emissions even in medium‐sized cities like Salt Lake City.Alternate :Plain Language SummaryHigh‐density measurements of CO2 were combined with a statistical model to estimate emission reductions across Salt Lake City during the COVID‐19 lockdown. Reduced traffic throughout the COVID‐19 lockdown was likely the primary driver behind lower CO2 emissions in Salt Lake City. There was also evidence that industrial‐based emission sources may of had an observable decrease in CO2 emissions during the lockdown. Finally, this analysis suggests that high‐density CO2 monitoring networks could be used to track progress toward decarbonization in the future.

Estimates of excess mortality during the COVID-19 pandemic strongly depend on subjective methodological choices.

Excess mortality is often used to assess the health impact of the COVID-19 pandemic. It involves comparing the number of deaths observed during the pandemic with the number of deaths that would counterfactually have been expected in the absence of the pandemic. However, published data on excess mortality often vary even for the same country. The reason for these discrepancies is that the estimation of excess mortality involves a number of subjective methodological choices. The aim of this paper was to summarize these subjective choices. In several publications, excess mortality was overestimated because population aging was not adjusted for. Another important reason for different estimates of excess mortality is the choice of different pre-pandemic reference periods that are used to estimate the expected number of deaths (e.g., only 2019 or 2015-2019). Other reasons for divergent results include different choices of index periods (e.g., 2020 or 2020-2021), different modeling to determine expected mortality rates (e.g., averaging mortality rates from previous years or using linear trends), the issue of accounting for irregular risk factors such as heat waves and seasonal influenza, and differences in the quality of the data used. We suggest that future studies present the results not only for a single set of analytic choices, but also for sets with different analytic choices, so that the dependence of the results on these choices becomes explicit.

Evaluating How the Social Restriction, the Government Response, the Health, and Economic Indices Affected the Prediction of the Number of Deaths Provoked by COVID-19 in Brazil Using Classical Statistical and Machine Learning Models

The COVID-19 death predictions are helpful for the formulation of public policies, allowing the use of more effective social isolation strategies with less economic and social impact. This article evaluates a wide range of forecasting methods to identify the best models for predicting cumulative and daily deaths caused by COVID-19 in Brazil, considering a forecast for a seven-day horizon. With the seven-day horizon, the predictions have more accuracy. The dataset is from Oxford Covid-19 Government Response Tracker. The jackknife resampling technique was implemented, thus providing an accurate estimate for evaluating the predictive capacity of the models. Each model was fitted with 266 jackknife samples considering 30-day training bases. The comparison between predictions was made using the average results, considering R-2, MAPE, RMSE, and MAE. Models from different classes were adopted: 1 ETS, 4 ARIMA, 18 regression models, and 7 machine learning algorithms. The cumulative death models produce better results than daily deaths, as the cumulative death models are less influenced by time series components: cycle and seasonality. The best results for predicting daily deaths were attained by the Ridge regression method. The best results for predicting cumulative deaths were obtained by the Cubist regression method.

Using Machine Learning to Explore the Risk Factors of Problematic Smartphone Use among Canadian Adolescents during COVID-19: The Important Role of Fear of Missing Out (FoMO)

The goal of the present study was to use machine learning to identify how gender, age, ethnicity, screen time, internalizing problems, self-regulation, and FoMO were related to problematic smartphone use in a sample of Canadian adolescents during the COVID-19 pandemic. Participants were N = 2527 (1269 boys;Mage = 15.17 years, SD = 1.48 years) high school students from the Lower Mainland of British Columbia, Canada. Data on problematic smartphone use, screen time, internalizing problems (e.g., depression, anxiety, and stress), self-regulation, and FoMO were collected via an online questionnaire. Several different machine learning algorithms were used to train the statistical model of predictive variables in predicting problematic smartphone use. The results indicated that Shrinkage algorithms (lasso, ridge, and elastic net regression) performed better than other algorithms. Moreover, FoMO, emotional, and cognitive self-regulation made the largest relative contribution to predicting problematic smartphone use. These findings highlight the importance of FoMO and self-regulation in understanding problematic smartphone use.

Application of GIS-Based AHP Model for the Impact Assessment of COVID-19 Lockdown on Environment Quality: The Case of Kabul City, Afghanistan

The COVID-19 pandemic has negatively impacted the industrial, financial, and social aspects of our daily life due to the implementation of lockdown to protect against the spread of the virus. In addition, the lockdown deduced by COVID-19 has promising positive impacts on air quality and environmental pollution. This study aims to monitor the effects of lockdown on environmental degradation during the pandemic in Kabul city, the capital of Afghanistan, using geospatial data and a statistical model of the Analytical Hierarchy Process (AHP). To achieve the purpose of the study, the most essential influencing factors on air quality were generated from different sources using Google Earth Engine (GEE) and GIS environment;Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Moisture Index NDMI) were calculated using Sentinel-2MSI, Carbon Monoxide (CO) was obtained from Sentinel-5P TROPOMI, and land surface temperature was retrieved from MODIS data. The generated thematic layers (before COVID-19, and during a lockdown of COVID-19) were weighted and rated using the AHP analysis. The weighted layers were spatially overlayed to obtain the final output. Consequently, the environmental quality degradation maps before and during COVID-19 were generated to assess the differences over the 22 districts of Kabul city. The findings of the study show that Kabul city is covered by the very low, low, moderate, high, and very high degradation of the environment by 3.17%, 5.33%, 20.54%, 26.63%, 44.32% before COVID-19 in 201,9 respectively, while the percentages are changed to 4.37%, 8.99%. 16.55%, 37.47%, and 32.62% during the lockdown caused by COVID-19 in 2020. The changes in the percentage of environmental degradation in Kabul city particularly in high and very high zones confirm the positive impact of the lockdown of COVID-19.

Consumers' Behavior in the Field of Organic Agriculture and Food Products during the COVID-19 Pandemic in the Czech Republic: Focus on a Comparison of Hyper-, Super- and Farmers' Markets and Direct Purchases from Producers

The aim of this paper is to assess Czech food consumers' behavior when buying organic products during the COVID-19 pandemic, with an emphasis on the place of purchase of organic agriculture and food products—especially those purchases with the shortest logistics value chain, i.e., purchase at farmers' markets, or directly from the producer—and a comparison with the current most common places of purchase of organic products in the Czech Republic, supermarkets and hypermarkets. Categorical data analysis methods were used to create a profile of the consumer according to the most frequent purchase locations. To create mathematical–statistical models and interpretations, the methods of logistic regression, correspondence analysis and contingency table analysis were chosen. According to the results of the survey, respondents under 25 years of age are the least likely to make purchases at farmers' markets or directly from the producer. Consumers aged 26–35 and with a university degree are the most likely to buy organic agriculture and food products at this location, followed closely by older respondents in the categories 36–45 and 46+ and with a secondary education. It is important for manufacturers to have an overview of where, in what quantities, and for what reasons consumers buy their products, especially for reasons of production optimization and planning, ecological concerns, rural development, and the impact on local areas and the value chain.

A Model Statistical during Covid-19 Future E-Commerce Revenue for Indonesia Aviation

Until today, Information Technology (IT) has been felt by aviation industry showed by positive growth of operating revenue before Covid-19 pandemic. The pandemic of Covid-19 changes the world especially the aviation industry by slowing down the business transaction. This study presents statistical model on recent e-commerce revenue of aviation, the number of passengers and the IT investments then predicts future of e-commerce revenue, the number of passengers and the IT spending using Neural Networks. This method is useful to predict the future because it follows the time being. The chosen variables are intended whether IT has an impact during the pandemic for passenger generation year by year. The results show that for the next few years, the revenue, the number of passengers and the IT spending are significantly increasing, while there are problems faced in aviation industry because of Covid-19. This model also can be applied for other industry.

A New Extension of the Kumaraswamy Exponential Model with Modeling of Food Chain Data

Statistical models are useful in explaining and forecasting real-world occurrences. Various extended distributions have been widely employed for modeling data in a variety of fields throughout the last few decades. In this article we introduce a new extension of the Kumaraswamy exponential (KE) model called the Kavya–Manoharan KE (KMKE) distribution. Some statistical and computational features of the KMKE distribution including the quantile (QUA) function, moments (MOms), incomplete MOms (INMOms), conditional MOms (COMOms) and MOm generating functions are computed. Classical maximum likelihood and Bayesian estimation approaches are employed to estimate the parameters of the KMKE model. The simulation experiment examines the accuracy of the model parameters by employing Bayesian and maximum likelihood estimation methods. We utilize two real datasets related to food chain data in this work to demonstrate the importance and flexibility of the proposed model. The new KMKE proposed distribution is very flexible, more so than numerous well-known distributions.

Performance of Bayesian Model Averaging (BMA) for Short-Term Prediction of PM10 Concentration in the Peninsular Malaysia

In preparation for the Fourth Industrial Revolution (IR 4.0) in Malaysia, the government envisions a path to environmental sustainability and an improvement in air quality. Air quality measurements were initiated in different backgrounds including urban, suburban, industrial and rural to detect any significant changes in air quality parameters. Due to the dynamic nature of the weather, geographical location and anthropogenic sources, many uncertainties must be considered when dealing with air pollution data. In recent years, the Bayesian approach to fitting statistical models has gained more popularity due to its alternative modelling strategy that accounted for uncertainties for all air quality parameters. Therefore, this study aims to evaluate the performance of Bayesian Model Averaging (BMA) in predicting the next-day PM10 concentration in Peninsular Malaysia. A case study utilized seventeen years' worth of air quality monitoring data from nine (9) monitoring stations located in Peninsular Malaysia, using eight air quality parameters, i.e., PM10, NO2, SO2, CO, O3, temperature, relative humidity and wind speed. The performances of the next-day PM10 prediction were calculated using five models' performance evaluators, namely Coefficient of Determination (R2), Index of Agreement (IA), Kling-Gupta efficiency (KGE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). The BMA models indicate that relative humidity, wind speed and PM10 contributed the most to the prediction model for the majority of stations with (R2 = 0.752 at Pasir Gudang monitoring station), (R2 = 0.749 at Larkin monitoring station), (R2 = 0.703 at Kota Bharu monitoring station), (R2 = 0.696 at Kangar monitoring station) and (R2 = 0.692 at Jerantut monitoring station), respectively. Furthermore, the BMA models demonstrated a good prediction model performance, with IA ranging from 0.84 to 0.91, R2 ranging from 0.64 to 0.75 and KGE ranging from 0.61 to 0.74 for all monitoring stations. According to the results of the investigation, BMA should be utilised in research and forecasting operations pertaining to environmental issues such as air pollution. From this study, BMA is recommended as one of the prediction tools for forecasting air pollution concentration, especially particulate matter level.

Epidemic Prediction using Machine Learning and Deep Learning Models on COVID-19 Data

A catastrophic epidemic of Severe Acute Respiratory Syndrome-Coronavirus, commonly recognised as COVID-19, introduced a worldwide vulnerability to human community. All nations around the world are making enormous effort to tackle the outbreak towards this deadly virus through various aspects such as technology, economy, relevant data, protective gear, lives-risk medications and all other instruments. The artificial intelligence-based researchers apply knowledge, experience and skill set on national level data to create computational and statistical models for investigating such a pandemic condition. In order to make a contribution to this worldwide human community, this paper recommends using machine-learning and deep-learning models to understand its daily accelerating actions together with predicting the future reachability of COVID-19 across nations by using the real-time information from the Johns Hopkins dashboard. In this work, a novel Exponential Smoothing Long-Short-Term Memory Networks Model (ESLSTM) learning model is proposed to predict the virus spread in the near future. The results are evaluated using RMSE and R-Squared values.

Risk of SARS-CoV-2 infection in a large cohort of Ontario workers

IntroductionWork is a key determinant of COVID-19 outcomes, however occupational surveillance is a critical information gap in many countries, including Canada. Understanding the risk of SARS-CoV-2 by occupation can identify high risk groups that can be targeted for prevention strategies.Materials and MethodsThe cohort includes 1,205,847 former workers compensation (non-COVID-19) claimants (aged 15–65) linked to health databases in Ontario, Canada. Incident cases were defined as either having a confirmed positive polymerase chain reaction (PCR) test in the Ontario Laboratory Information System (OLIS), or an International Classification of Diseases (ICD-10-CA) diagnostic code of U07.1 in hospitalization or emergency department records (February 2020-December 2021). Workers were followed until diagnosis, death, emigration, age 65 or end of follow-up. Sex- and age-adjusted Cox proportional hazards models were used to estimate hazards ratios (HR) and 95% confidence intervals (CI) by occupation, compared to all other cohort members. Analyses were also conducted to examine occupational trends in testing and diagnosis during waves of infection.ResultsOverall, 80,740 COVID-19 cases were diagnosed among workers during follow-up, of those, 80% were diagnosed with a positive PCR test. Associations were identified between COVID-19 diagnosis and employment in nursing (HR=1.44, CI95%=1.40–1.49), air transport operating (HR=1.61, CI95%=1.47–1.77), textile/fur/leather products fabricating, assembling, and repairing (HR=1.38, CI95%=1.25–1.54), apparel and furnishing services (HR=1.38, CI95%=1.19–1.60), and janitor and cleaning services (HR=1.11, CI95%=1.06–1.16). Restricted analyses where health care workers were omitted from the comparison group strengthened some associations for other high-risk workers. Test positivity ranged between 4–16% across major occupation groups. Risks varied over time and with changes in protective measures in workplaces and in broader communities.ConclusionsElevated risk of SARS-CoV-2 infection in health care, manufacturing, transportation, and service workers were identified, underscoring the importance of including occupational data in COVID-19 surveillance. Occupational trends in severe outcomes and vaccination are also being explored.

Risk for hospitalization due to COVID-19 in relation to occupational exposures as estimated by two different job-exposure matrices

IntroductionThe COVID-19 pandemic has profoundly challenged occupational safety and health. We assessed risk for hospitalization for COVID-19 in relation to potential routes and degree of occupational exposure.Material and MethodsThe study includes 1 105 042 subjects in the county of Stockholm of age 18–64 years, with an occupational code, followed regarding hospitalization from 1 March 2020 until 15 September 2022. We used two different job-exposure matrices (JEMs), based on survey data (Office for National Statistics 2020) and expert assessment (Oude Hengel et al 2022, module for Denmark), respectively. Hazard ratios (HRs) and 95% confidence intervals (CI) were obtained with Cox´s proportional hazards models. Fully adjusted models included age, sex, vaccination (time-dependent), household size, living space per person, income quintile, proportion of smokers in the occupation, and country of birth.ResultsWe observed 6523 hospitalizations with COVID-19 as the main diagnosis. HRs increased incrementally with the exposure dimensions in both JEMs and were increased already from the low-exposed categories.The fully adjusted HRs (95% CI) for the highest exposure category were for the survey-based JEM: Closeness to other people (very close, almost touching): 1.51 (1.42–1.59);Exposure to other people´s diseases (daily): 1.41 (1.33–1.50). Similarly, we found for the expert-based JEM: Number of co-workers in close vicinity (>30/day): 1.47 (1.39–1.57);Nature of contact with other people (regular contact with COVID-19 patients): 1.51 (1.40–1.63);Location of work (>4h/day indoors): 1.25 (1.19–1.31);Inability to keep social distancing (can never maintain >1m): 1.42 (1.33–1.51).ConclusionsDimensions of potential occupational exposure in both the survey- and expert-based JEMs were consistently associated with hospitalization for COVID-19 and may thus guide risk assessment. Increased risks observed already in the lower exposure categories indicate a need for enhanced preventive measures also in those settings.

[Forecasting models to guide intensive care COVID-19 capacities in Germany]. / Prognosemodelle zur Steuerung von intensivmedizinischen COVID-19-Kapazitäten in Deutschland.

BACKGROUND: Time-series forecasting models play a central role in guiding intensive care coronavirus disease 2019 (COVID-19) bed capacity in a pandemic. A key predictor of future intensive care unit (ICU) COVID-19 bed occupancy is the number of new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections in the general population, which in turn is highly associated with week-to-week variability, reporting delays, regional differences, number of unknown cases, time-dependent infection rates, vaccinations, SARS-CoV­2 virus variants, and nonpharmaceutical containment measures. Furthermore, current and also future COVID ICU occupancy is significantly influenced by ICU discharge and mortality rates. METHODS: Both the number of new SARS-CoV­2 infections in the general population and intensive care COVID-19 bed occupancy rates are recorded in Germany. These data are statistically analyzed on a daily basis using epidemic SEIR (susceptible, exposed, infection, recovered) models using ordinary differential equations and multiple regression models. RESULTS: Forecast results of the immediate trend (20-day forecast) of ICU occupancy by COVID-19 patients are made available to decision makers at various levels throughout the country. CONCLUSION: The forecasts are compared with the development of available ICU bed capacities in order to identify capacity limitations at an early stage and to enable short-term solutions to be made, such as supraregional transfers.

Assessing COVID-19-Related Excess Mortality Using Multiple Approaches-Italy, 2020-2021.

INTRODUCTION: Excess mortality (EM) is a valid indicator of COVID-19's impact on public health. Several studies regarding the estimation of EM have been conducted in Italy, and some of them have shown conflicting values. We focused on three estimation models and compared their results with respect to the same target population, which allowed us to highlight their strengths and limitations. METHODS: We selected three estimation models: model 1 (Maruotti et al.) is a Negative-Binomial GLMM with seasonal patterns; model 2 (Dorrucci et al.) is a Negative Binomial GLM epidemiological approach; and model 3 (Scortichini et al.) is a quasi-Poisson GLM time-series approach with temperature distributions. We extended the time windows of the original models until December 2021, computing various EM estimates to allow for comparisons. RESULTS: We compared the results with our benchmark, the ISS-ISTAT official estimates. Model 1 was the most consistent, model 2 was almost identical, and model 3 differed from the two. Model 1 was the most stable towards changes in the baseline years, while model 2 had a lower cross-validation RMSE. DISCUSSION: Presently, an unambiguous explanation of EM in Italy is not possible. We provide a range that we consider sound, given the high variability associated with the use of different models. However, all three models accurately represented the spatiotemporal trends of the pandemic waves in Italy.

Risk preference, risk perception, and purchase recovery period: Empirical evidence from salmon contamination of COVID-19 in China

This paper investigates the recovery period of consumer salmon purchase intention after food scares at the Xinfadi wholesale market in China during the COVID-19 pandemic and examines the impact mechanism of risk preference and risk perception on the period duration. Our empirical analysis is based on a survey of 655 salmon consumers in Beijing, Tianjin, and Shanghai. We estimate that the purchase intention recovery period lasts 21 weeks among the surveyed consumers after the shock. Although the epidemic risk levels of the three cities are different, there is a significant difference only in the recovery period from 5 to 7th weeks. The Cox proportional hazards model results further show that consumers with less risk-averse are more active in resuming purchase intention, and the effect of risk perception is just the opposite. Moreover, risk perception has a moderating effect on risk preference and recovery period. Finally, we put forward three possible policy implications: attaching nucleic acid detection certificate, strengthening cold chain management, and diversifying cooking methods.

Statistical modelling of the impact of online courses in higher education on sustainable development

Purpose>The concept of sustainable development (SD) is a popular response to society's need to preserve and extend the life span of natural resources. One of the 17 goals of the SD is "education quality” (Fourth Goal of Sustainable Development [SDG-4]). Education quality is an important goal because education is a powerful force that can influence social policies and social change. The SDG-4 must be measured in different contexts, and the tools to quantify its effects require exploration. So, this study aims to propose a statistical model to measure the impact of higher education online courses on SD and a structural equation model (SEM) to find constructs or factors that help us explain a sustainability benefits rate. These proposed models integrate the three areas of sustainability: social, economic and environmental.Design/methodology/approach>A beta regression model suggests features that include the academic and economic opportunities offered by the institution, the involvement in research activities and the quality of the online courses. A structural equation modelling (SEM) analysis allowed selecting the key variables and constructs that are strongly linked to the SD.Findings>One of the key findings showed that the benefit provided by online courses in terms of SD is 62.99% higher than that of offline courses in aspects such as transportation, photocopies, printouts, books, food, clothing, enrolment fees and connectivity.Research limitations/implications>The SEM model needs large sample sizes to have consistent estimations. Thus, despite the obtained estimations in the proposed SEM model being reliable, the authors consider that a limitation of this study was the required time to collect data corresponding to the estimated sample size.Originality/value>This study proposes two novel and different ways to estimate the sustainability benefits rate focused on SDG-4, and machine learning tools are implemented to validate and gain robustness in the estimations of the beta model. Additionally, the SEM model allows us to identify new constructs associated with SDG-4.

A comparative study for COVID-19 cases forecasting with loss function as AIC and MSE in RNN family and ARIMA

Forecasting COVID-19 incidents is a trending research study in today's world. Since Machine learning models have been occupied in forecasting recently, this study focus on comparing statical and machine learning models such as ARIMA, RNN, LSTM, Seq2Seq, and Stacked LSTM. The performances were evaluated using two loss functions, namely, AIC and RMSE. The results showed that RNN performs with the lowest RMSE with-49.5% compared with the ARIMA. Seq2Seq scored the highest correlation of determination (R2) with 0.92. © 2022 IEEE.

Model training periods impact estimation of COVID-19 incidence from wastewater viral loads.

Wastewater-based epidemiology (WBE) has been deployed broadly as an early warning tool for emerging COVID-19 outbreaks. WBE can inform targeted interventions and identify communities with high transmission, enabling quick and effective responses. As the wastewater (WW) becomes an increasingly important indicator for COVID-19 transmission, more robust methods and metrics are needed to guide public health decision-making. This research aimed to develop and implement a mathematical framework to infer incident cases of COVID-19 from SARS-CoV-2 levels measured in WW. We propose a classification scheme to assess the adequacy of model training periods based on clinical testing rates and assess the sensitivity of model predictions to training periods. A testing period is classified as adequate when the rate of change in testing is greater than the rate of change in cases. We present a Bayesian deconvolution and linear regression model to estimate COVID-19 cases from WW data. The effective reproductive number is estimated from reconstructed cases using WW. The proposed modeling framework was applied to three Northern California communities served by distinct WW treatment plants. The results showed that training periods with adequate testing are essential to provide accurate projections of COVID-19 incidence.

ESG Controversies: A Quantitative and Qualitative Analysis for the Sociopolitical Determinants in EU Firms

Environmental, Social, and Governance (ESG) criteria are novel and exciting tools of corporate disclosure for decision making. Using quantitative and qualitative analyses, the present study examined the key characteristics and trends of ESG controversies in the European market. At the same time, it identified the controversies’ determinants. A bibliometric analysis was the qualitative method employed on the data derived from Scopus using Biblioshiny software, an R package. The quantitative analysis involved an international sample of 2278 companies headquartered in Europe from 2017–2019 being studied using a Generalized Linear Model. The findings of this research highlighted the role of the “S” and the “G” dimensions of the ESG controversies as the most crucial in affecting controversies. Women are under-represented in the business hierarchy, but their natural characteristics such as friendliness and peaceability lead to a low level of illegal business practices. However, independent of gender, executives have personal gains that they want to satisfy. Thus, executives may become involved in unethical practices and harm their colleagues and the business’s reputation. On the other hand, democracy emerged as one of the most disputed factors. Democracy gives people the voice to express themselves and publicly support their ideas without restrictions. Although, the regression results showed that democracy is not always operated as the “pipe of peace” and can affect, to some extent, controversies.