Policy Analysis of Low-Carbon Energy Transition in Senegal Using a Multi-Criteria Decision Approach Based on Principal Component Analysis

Senegal has been investing in the development of its energy sector for decades. By using a novel multi-criteria decision analysis (MCDA) based on the principal component analysis (PCA) method, this paper develops an approach to determine the effectiveness of Senegal's policies in supporting low-carbon development. This was determined using six criteria (C1 to C6) and 17 policies selected from the review of Senegal's energy system. In order to determine the optimal weighting of the six criteria, a PCA is performed. In our approach, the best weighted factor is the normalized version of the best linear combination of the initial criteria with the maximum summarized information. Proper weighted factors are determined through the percentage of the information provided by the six criteria kept by the principal components. The percentage of information is statistically a fit of goodness of a principal component. The higher it is, the more statistically important the corresponding principal component is. Among the six principal components obtained, the first principal component (comp1) best summarizes the values of criteria C1 to C6 for each policy. It contains 81.15% of the information on energy policies presented by the six criteria and was used to rank the policies. Future research should take into account that when the number of criteria is high, the share of information explained by the first principal component could be lower (less than 50% of the total variance). In this case, the use of a single principal component would be detrimental to the analysis. For such cases, we recommend a higher dimensional visualization (using two or three components), or a new PCA should be performed on the principal components. This approach presented in our study can serve as an important benchmark for energy projects and policy evaluation.

COVID-19 policies and recommendations for foodservice reopening: An integrative review

This study aims to present and discuss the different COVID-19 policies and recommendations for food service reopening. We aimed to understand each plan's profile, showing the most prominent concerns and summarizing the strategies. This study was carried out using an integrative review strategy of documents written in English, Spanish, Portuguese, Italian, French, and German. We found 17 guides, 15 from the countries' health departments and institutions. The findings suggested four main categories reflecting the main concerns about safety regarding the resumption of food services during the pandemic: 1. Physical distancing;2. evironmental aspects and safety;3. personal hygiene and occupational health;and 4. educational and legal measures. Because COVID-19 is a new disease, the measures were designed and adapted to a scenario full of uncertainties and improved information for each discovery. All the categories are grounded on recent or late biomedical literature. Some minor recommendations are based on the precautionary principle. The practical and policy implications are discussed. Health agencies in countries that do not yet have their regulations or guidelines for operating food services can use the categories described here as a basis for suggestions.

Preliminary Findings on Long-Term Impacts of the COVID-19 Pandemic on Social Work Students

Impacts of the COVID-19 pandemic upon social work programs have been profound and institutions have struggled to identify appropriate policies and interventions to address students' educational, health, and safety needs. This manuscript reports the results of a survey administered to BSW and MSW students at three different timepoints during the pandemic: March 2020 (N = 157), November 2020 (N = 87), and March 2021 (N = 45). The survey explored students' experiences of online learning, pandemic-related stressors, mental health symptoms, and the department and institution's responses to the pandemic. Although initial comfort with online classroom learning was low, comfort levels increased significantly as the pandemic continued. Students in the MSW online program were the most comfortable with online learning and BSW students the least. Students reported high levels of pandemic-related stressors and behavioral health symptoms throughout the pandemic and approximately half the sample reported concerns about program continuance. Although students felt supported by the social work program, they felt institutional policies to address COVID-19 were inadequate. Useful interventions identified included access to mental health, financial supports, flexibility and support from faculty, clear and consistent communication, and a decrease in field hours during the pandemic. © 2023 Taylor & Francis.

Continuous Wavelet Transform of Time-Frequency Analysis Technique to Capture the Dynamic Hedging Ability of Precious Metals

Exploring the hedging ability of precious metals through a novel perspective is crucial for better investment. This investigation applies the wavelet technique to study the complicated correlation between global economic policy uncertainty (GEPU) and the prices of precious metals. The empirical outcomes suggest that GEPU exerts positive influences on the prices of precious metals, indicating that precious metals could hedge against global economic policy uncertainty, which is supported by the inter-temporal capital asset pricing model (ICAPM). Among them, gold is better for long-term investment than silver, which is more suitable for the short run in recent years, while platinum's hedging ability is virtually non-existent after the global trade wars. Conversely, the positive influences from gold price on GEPU underline that the gold market plays a prospective role in the situation of economic policies worldwide, which does not exist in the silver market. Besides, the effects of platinum price on GEPU change from positive to negative, suggesting that the underlying cause of its forward-looking effect on GEPU alters from the investment value to the industrial one. In the context of the increasing instability of global economic policies, the above conclusions could offer significant lessons to both investors and governments.

Improved policy mechanisms for the promotion of future digital business economy during covid-19 pandemic

Recent advances in the field of digital technology have enabled the expansion of the business market to a new level. Digital business markets encompass eminent features like increased transparency, enhanced customer relationship, flexible business targets, customer participation, etc. It aids in enhancing traditional business quality by bridging the relationship between the suppliers and the customers. The massive development of internet technology in recent years has largely contributed to the emergence of digital business. The digitization of the business world helps small and medium enterprises create a new digital network, which allows overcoming large enterprises' dominance. Since small and medium enterprises play a major role in determining a country's economy, the digital market's emergence and development are very important. The self-organizing capability of the digital market is the main advantage for newly emerging enterprises. Recently, the Covid-19 pandemic has resulted in customers' migration from offline shopping to the online market. These changing business models mandate the need for policies and control mechanisms. Using these policies, digital business projects are developed using an open environment using which the small and medium enterprises can interact with each other. These policies and control mechanisms outline the rules to be followed to avoid the current digital market issues. These policies must be designed to improve and ensure the welfare of future digital business systems. Thus, this paper aims to explore the policies associated with regulating and promoting the digital business economy.

Government responses to COVID-19 and impact on GBV services and programmes: comparative analysis of the situation in South Africa, Kenya, Uganda, and Nigeria.

As governments impose restrictive policies to contain infectious disease outbreaks, pre-existing gender-based inequalities are often exacerbated, increasing the risk of gender-based violence (GBV). Despite international guidance on the need for continued provision of GBV services during emergencies, governments often de-prioritise GBV services and programmes. We conducted a rapid assessment in South Africa, Kenya, Uganda, and Nigeria to examine the impact of COVID-19 policies on the availability of GBV prevention and response services. The study team interviewed 80 stakeholders representing different GBV services in the four countries. The interviews revealed strikingly similar government mis-steps that disrupted the availability of comprehensive GBV services. In all four countries, the government's failure to exempt the provision of multi-sectoral GBV services from initial lockdown restrictions led to confusion and disrupted the provision of critical GBV services such as clinical management of rape, legal and judicial services, psychosocial services, availability of shelters, and community-based prevention activities. The government's imposition of curfews, stay-at-home orders, and transportation restrictions further diminished access to services. Governments must strengthen currently available GBV prevention and response services and be better prepared for future pandemics. Following international guidelines, governments should deem GBV services as essential from the beginning with clear implementation plans. Governments must invest in community-based solutions and the expansion of digital tools to ensure everyone, especially those likely to be structurally excluded, have access to critical services during an emergency.

Structural modeling of COVID-19 spread in relation to human mobility.

Human mobility is considered as one of the prominent non-pharmaceutical interventions to control the spread of the pandemic (positive effect from mobility to infection). Conversely, the spread of the pandemic triggered massive changes to people's daily schedules by limiting their movement (negative effect from infection to mobility). The purpose of this study is to investigate this bi-directional relationship between human mobility and COVID-19 spread across U.S. counties during the early phase of the pandemic when infection rates were stabilizing and activity-travel behavior reflected a fairly steady return to normal following the drastic changes observed during the pandemic's initial shock. In particular, we applied Structural Regression (SR) model to investigate a bi-directional relationship between COVID-19 infection rate and the degree of human mobility in a county in association with socio-demographic and location characteristics of that county, and state-wide COVID-19 policies. Combining U.S. county-level cross-sectional data from multiple sources, our model results suggested that during the study period, human mobility and infection rate in a county both influenced each other, but in an opposite direction. Metropolitan counties experienced higher infection and lower mobility than non-metropolitan counties in the early stage of the pandemic. Counties with highly infected neighboring counties and more external trips had a higher infection rate. During the study period, community mitigation strategies, such as stay at home order, emergency declaration, and non-essential business closure significantly reduced mobility whereas public mask mandate significantly reduced infection rates. The findings of this study will provide important insights to policy makers in understanding the two-way relationship between human mobility and COVID-19 spread and to derive mobility-driven policy actions accordingly.

Policy analysis and data mining tools for controlling COVID-19 policies.

Much research has been done on the efficacy of vaccines against the COVID-19 pandemic, but the claims have not yet been realized in the real world. This paper proposes three COVID-19 policy outcome analysis tools such as jpscore for scoring and revealing the best prefecture policy in Japan, scorecovid for scoring and revealing the best country policy in the world, and finally hiscovid for visualizing and identifying when policymakers made mistakes in time-series scores. Poorly scored countries or prefectures can learn good strategies from the best country or prefecture with excellent scores. Three tools are based on a single metric dividing the number of COVID-19 deaths by the population in millions. Three tools suggest us that the sustainable mandatory test-isolation strategy should be adopted in the world for mitigating the pandemic. This paper also addresses what is lacking in Japan for scientific evidence-based research for mitigating the pandemic. Visualization tools and sorted and time-series scores of policy outcomes help policymakers make the right decisions.

hiscovid for visualizing and identifying when policymakers made mistakes against COVID-19.

There are two types of policy outcome analysis tools: snapshot tool and time-series tool. hiscovid is a time-series policy outcome scoring tool of COVID-19 policies by country where the daily cumulative population mortality is used for scoring the outcomes of COVID-19 country policies to visualize and identify when policymakers made mistakes. hiscovid allows policymakers to observe the progress and transition of scores over time to learn lessons from the past decision-making mistakes for correcting the current policies to reduce unnecessary deaths. The lower the score, the better the policy. hiscovid attracted 1480 users worldwide.

The effects of us covid-19 policy responses on cryptocurrencies, fintech and artificial intelligence stocks: A fractional integration analysis

This paper assesses the impact of US policy responses to the Covid-19 pandemic on various technology-related assets such as cryptocurrencies, financial technology, and artificial intelligence stocks using fractional integration techniques. More precisely, it analyzes the behavior of the percentage returns in the case of nine major coins (Bitcoin-BITC, Stella-STEL, Litecoin-LITE, Ethereum-ETHE, XRP (Ripple), Dash, Monero-MONE, NEM, Tether-TETH) and two technology-related stock market indices (the KBW NASDAQ Technology Index-KFTX, and the NASDAQ Artificial Intelligence index-AI) over the period 1 January 2020-5 March 2021. The results suggest that fiscal measures such as debt relief and fiscal policy announcements had positive effects on the series examined during the pandemic, when an increased mortality rate tended instead to drive them down;by contrast, monetary measures and announcements appear to have had very little impact and the Covid-19 containment measures none at all.

scorecovid for scoring individual country COVID-19 policies in the world.

There are two types of policy analysis tools: snapshot tool and time-series tool. scorecovid is a snapshot tool to score individual COVID-19 policies in the world and sort a list of scores. The population mortality rate is used for evaluating the outcomes of COVID-19 country policies. The lower the score, the less the COVID-19 deaths. The lower the score, the better the policy. The scorecovid tool is intended for poorly scored countries to learn good strategies from countries with excellent scores where scorecovid attracted 15192 users worldwide.

Balancing Public & Economic Health in Japan during the COVID-19 Pandemic: A Descriptive Analysis.

Despite loose restrictions and a low mortality rate due to COVID-19, Japan faced the challenge of stabilizing its economy during the pandemic. Here, we analyzed how the Japanese government attempted to maintain a balance between the health of the population and the health of the economy. We used a mix of quantitative data, information from policy documents, and news agency publications. Features of the Japanese government's handling of the pandemic include the lack of constitutional authority to enforce a lockdown, the laxer restrictions compared with other countries in which citizens were advised only to exercise self-restraint and avoid close social contact, and the existence of expert panels that had only an advisory role. Our findings address the slow initial response of the government, which feared that the 2020 Tokyo Olympics would be canceled, and the increased testing when the Olympics were postponed, as well as the expansion of vaccination efforts after the Olympics. In addition, there was a targeted campaign to promote national travel to increase economic revenue in the tourism sector, but this led to an increase in COVID-19 cases.

UsING MOBILITY MEAsUREs TO EXPLAIN sHORT-RUN ECONOMIC PERFORMANCE DURING COVID-19's FIRsT WAVE

People's mobility behavior and self-correcting economic forces are shown to be key variables in explaining the observed contraction and recovery of the Bolivian economy during COVID-19's first wave. ARMAX models are used to explain the growth rate of the monthly index of economic activity in terms of changes in the rate of mobility measures produced by Google. These measures are argued to capture the effect of the complex dynamics of COVID-19 epidemiology and COVID-19 policies on people's decisions when balancing health risks with inevitable economic sacrifice, thus affecting aggregate economic activity.

COVID-19-Related Stressors and Mental Health Disorders Among US Adults.

OBJECTIVES: The COVID-19 pandemic has led to severe mental health repercussions. We examined rates of anxiety and depression in the United States during the pandemic by demographic characteristics, individual stressors, and COVID-19 infection rates and policy contexts. METHODS: We merged data from the April 2020-March 2021 US Household Pulse Survey with state-level data on COVID-19 rates and mitigation policies, including stay-at-home orders, face mask mandates, and restaurant closures. We estimated weighted logistic regression models to assess correlates of anxiety and depression. RESULTS: Rates of anxiety and depression peaked in late 2020 at 39% and 32%, respectively. Food insecurity and disrupted medical care were associated with more than twice the odds of anxiety and depression (food insecurity: odds ratio [OR] = 2.58 for anxiety and 2.61 for depression; disrupted medical care: OR = 2.40 and 2.27). Being not employed (OR = 1.32 for anxiety and 1.45 for depression), uninsured (OR = 1.30 and 1.38), housing insecure (OR = 1.41 and 1.34), and experiencing disruptions in education (OR = 1.28 and 1.25) were linked to 25% to 45% increased odds of anxiety and depression. Increases in state COVID-19 infection rates were associated with significantly heightened odds of anxiety and depression (OR = 1.01 for anxiety and depression), but state mitigation policies were not. CONCLUSIONS: Levels of anxiety and depression rose during the pandemic, particularly among economically vulnerable individuals and those experiencing economic and service disruptions. Future research should assess the effectiveness of policies targeting COVID-19 economic and service disruptions.

Culture, Institution, and Policy Response to COVID-19

In a matter of two years, COVID-19 has affected almost all countries and wrought havoc on economic, social, and human life worldwide. Some countries have been successful in limiting the COVID-19 cases while others have floundered. What explains the substantial country-to-country variations in the cases of the same COVID-19 crisis? This article tries to examine this by incorporating standard variables with informal institutions such as culture and political idiosyncrasy at the center of analysis. It builds and estimates a parsimonious single equation model using the ordinary least squares technique. It relies on data from multiple sources such as the WHO, World Values Survey, World Bank, etc. The empirical estimates show that countries that have more people in the age 65 and over the category that do not lessen mobility during the study period experienced higher cases. It does not find population density or stringency measures linked to controlling cases. It shows that the countries that display cultural tightness, collectivism, and trust in government, and are not hesitant to sacrifice civil liberties during the crisis experienced fewer cases. In short, culture matters in dealing with COVID-19. © 2022, Journal of Economic Issues / Association for Evolutionary Economics.

COVID-19 Spatio-Temporal Evolution Using Deep Learning at a European Level.

COVID-19 evolution imposes significant challenges for the European healthcare system. The heterogeneous spread of the pandemic within EU regions elicited a wide range of policies, such as school closure, transport restrictions, etc. However, the implementation of these interventions is not accompanied by the implementation of quantitative methods, which would indicate their effectiveness. As a result, the efficacy of such policies on reducing the spread of the virus varies significantly. This paper investigates the effectiveness of using deep learning paradigms to accurately model the spread of COVID-19. The deep learning approaches proposed in this paper are able to effectively map the temporal evolution of a COVID-19 outbreak, while simultaneously taking into account policy interventions directly into the modelling process. Thus, our approach facilitates data-driven decision making by utilizing previous knowledge to train models that predict not only the spread of COVID-19, but also the effect of specific policy measures on minimizing this spread. Global models at the EU level are proposed, which can be successfully applied at the national level. These models use various inputs in order to successfully model the spatio-temporal variability of the phenomenon and obtain generalization abilities. The proposed models are compared against the traditional epidemiological and Autoregressive Integrated Moving Average (ARIMA) models.

Public Health Shock, Intervention Policies, and Health Behaviors: Evidence from COVID-19

In response to the COVID-19 pandemic, many countries, including the U.S., adopted intervention policies aimed at averting the spread. However, these policies may have led to significant changes in public health behaviors. We use Google search queries to examine how state government actions are associated with people’s internet searches (internet browsing habits) related to health behaviors. We employ the differences-in-differences method to determine the link between disease outbreak, associated intervention policies, and changes in health behavior related searches. Our findings show that school closures, restaurant restrictions, and stay-at-home orders lead to a significant rise in searches for workout, physical activity, exercise, takeout, liquor, and wine. Moreover, people’s concerns regarding weight loss, diet, nutrition, restaurant, and fast food substantially decline following stay-at-home orders. Our event-study results indicate that changes in health behaviors began weeks before stay-at-home orders were implemented contemporaneously with emergency declarations and other partial closures. These findings suggest that people’s health behaviors are notably affected by state government’s intervention policies. © 2022 Samira Hasanzadeh, and Modjgan Alishahi.

The role of digital technologies to unleash a green recovery: pathways and pitfalls to achieve the European Green Deal

PurposeThe article aims to concern identification and development of pathways for a green recovery process post pandemic taking into account the role of digital technologies for unleashing the policies planned within the European Green Deal (EGD).Design/methodology/approachThe study is based on a systematic literature review (SLR). The electronic databases Scopus and Web of Science (WoS) were surveyed. The authors followed the SLR guidelines laid down by Tranfield et al. (2003) and the Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) framework and 65 articles were found eligible after thorough reading and inclusion in the analysis.FindingsThe article presents an innovative framework containing the digital technologies and their roles in enabling the achievement of the EGD policies and the barriers to their adoption.Originality/valueThe proposed framework would guide organizations and policymakers' decisions to pursue a pathway in which a green recovery is possible, mainly after the consequences of the current pandemic, considering the pitfalls of the journey. The article is original as it provides an up-to-date guidance toward an emerging theme, which is a green recovery economy including a net-zero carbon worldwide target.

Aligning the achievement of SDGs with long-term sustainability and resilience: An OOBN modelling approach

This research utilizes an Object-Oriented Bayesian Network (OOBN) to model the relationships between the Sustainable Development Goal (SDGs) and resilience and sustainability at national, regional, and global levels. The ability of the OOBN to learn the parameters, i.e., the conditional probability distributions between the variables included in the network, was exploited to explore the impacts of progress of SDGs on the sustainability and resilience of nations. The resulting OOBN is used to examine different situations pertinent to policy analysis and design at the times of disasters, particularly in the wake of the COVID-19 pandemic. Three case studies are used to illustrate the step by step process of using the proposed OOBN as well as the expected results of its application in policy analysis and evaluation contexts. The proposed is able to provide insight regarding which SDGs will have more significant impacts on both resilience and sustainability as well as their constituent components. The results of this research indicate how data induced OOBNs can be utilised by policy makers to prioritize new policies and evaluate the impacts of existing policies on both the resilience and sustainability of societies.

The impact of repeated mass antigen testing for COVID-19 on the prevalence of the disease.

In the absence of effective vaccination, mass testing and quarantining of positive cases and their contacts could help to mitigate pandemics and allow economies to stay open. We investigate the effects of repeated mass testing on the COVID-19 pandemic caused by the SARS-CoV-2 virus, using data from the first ever nationwide rapid antigen testing implemented in Slovakia in autumn 2020. After the first round of testing, only districts above an ex ante unknown threshold of test positivity were re-tested. Comparing districts above and below the threshold, we provide evidence that repeated mass antigen testing can temporarily reduce the number of new infections. Our results suggest that mass testing coupled with the quarantining of positive cases and their contacts could be an effective tool in mitigating pandemics. For lasting effects, re-testing at regular intervals would likely be necessary.