2284-7995, Trends in Rural Tourism Development in Lviv Region Based on Forecasting

The rural tourism is a promising type of tourism in rural areas that increases the socio-economic level and well-being of the population. The entrepreneurial activity in villages provides an opportunity to expand employment, increase investment attractiveness and contribute to the improvement of rural infrastructure. Based on the analysis of data for the period 2012-2020, the forecast trends in the number of rural tourism farmsteads and their visitors in Lviv region are presented using trend analysis and the FORECAST.ETS.STAT function for the period 2021-2025. The Russian military aggression against Ukraine and the COVID-19 pandemic significantly affect the activities and prospects for the development of rural tourism in the country. Having built logarithmic, linear, exponential, power and polynomial trend models, the probable indicators for the specified period were forecasted. The current state and trends of rural tourism development during the COVID-19 pandemic and in the context of Russia's military aggression in Eastern Ukraine are considered. The formation and sale of quality products and services in the field of rural tourism involve providing a favorable environment and improving the quality of functioning of rural tourism estates. The development of rural tourism depends on the desires and demands of tourists, which form the demand in this area, which in turn creates supply in the market of tourist services and further development of business activities in the field of services in rural areas.

Corporate Restructuring Laws Under Stress

Bail-outs by way of loan have a similar effect (on the debtor: plainly, the cost of delivering relief is allocated differently as between a bail-out and a bail-in) in that they enable the debtor to meet current fixed costs through borrowing, in effect swapping shorter-term liabilities with a longer-term liability. The authors acknowledge the support of the Oxford Law Faculty in funding the Conference "Corporate Restructuring Laws Under Stress" (St Hugh's College, Oxford, 10 October 2022) at which the papers in this special issue were first presented, and the support of the Covid-19 Research Response Fund at Oxford University, which provided funding for the wider project of which the Conference formed one part. Most authors, however, express some concerns in relation to Covid-19 bail-out design, and in particular query whether some bail-outs may have been too generous. [Extracted from the article] Copyright of European Business Organization Law Review is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

The Benefits and Costs of Using Social Distancing to Flatten the Curve for COVID-19

We examine the net benefits of social distancing to slow the spread of COVID-19 in USA. Social distancing saves lives but imposes large costs on society due to reduced economic activity. We use epidemiological and economic forecasting to perform a rapid benefit–cost analysis of controlling the COVID-19 outbreak. Assuming that social distancing measures can substantially reduce contacts among individuals, we find net benefits of about $5.2 trillion in our benchmark case. We examine the magnitude of the critical parameters that might imply negative net benefits, including the value of statistical life and the discount rate. A key unknown factor is the speed of economic recovery with and without social distancing measures in place. A series of robustness checks also highlight the key role of the value of mortality risk reductions and discounting in the analysis and point to a need for effective economic stimulus when the outbreak has passed.

Forecasting of Indonesian Digital Economy based on Available New Start-up

Since the last 5 years, digital economy is growing steadily in Indonesia. Right now, the digital economy faces some potential problems and Covid-19 pandemic. This paper presents current data of the national Gross Domestic Product (GDP) and other GDPs (billion IDR) and the number of start-up, and predicts near some categories of future GDP and numbers of available new start-up for the next few years. The forecast will use Markov chain analysis. The results indicate that, while there are problems faced by the digital economy industry, the GDP and numbers of start-up are significantly increasing.

Socioeconomic spillovers of COVID-19: case of Saudi Arabia

PurposeWith the global outbreak of COVID-19 that has made the economic activities standstill, countries have taken immediate measures to safeguard not only the human lives but also the economies. This study investigates empirically the lockdown impact of current pandemic on the Saudi economy.Design/methodology/approachThe study employs inoperability input–output model (IIOM) on the input–output table (IOT) of Saudi Arabia for the analysis.FindingsFindings show that with the closure of few sectors for the period of two months, the GDP declined to 6.49%. Findings also show a negative impact on consumption, investments and exports.Research limitations/implicationsOne limitation of current study is that it uses IOTs which lack primary and secondary income distribution that is vital for presenting complete interindustry connections in the analysis. The interindustry structures relate to the consumption structures which ultimately lead to the income distribution and affect the consumption behaviors of economic agents. Hence, the complete income circular flow is not incorporated in IIOM using IOT. The findings of current study would be well grounded if it endogenized the primary and secondary income distribution.Practical implicationsThe practical implication of this study is the use of IIOM for anticipating the potential loss against the backdrop of catastrophes and pandemics. The IIOM has the capability to predict the economic effects of disruptive events and hence the policy-makers can better predict and devise prudent policies to avoid the likely threats to the economy.Originality/valueThe current situation is unprecedented, and it is challenging for governments to forecast the economic repercussions. Several economic sectors have been inoperative due to lockdown implemented by the governments. This study empirically estimated the inoperability produced by the current pandemic. The findings are consistent with other estimated statistics, thereby proving the efficacy of IIOM to anticipate the economic repercussions of natural hazards.

Letter from the editors

Whilst the impact of COVID-19 on migration has receded to a large degree, the global economic situation forecasts the increased reliance on migration as a survival strategy and provides a promise of opportunity for many. The year 2022 witnessed the publication of the ' I Policies and Politics of Venezuelan Migration in Latin America i ' special section that brought together scholars from the region and beyond to reflect on the mass migration, diverse policies implemented across the region and the impact on hosting societies (60:1). The year 2022 has passed with migration continuing to occupy centrefold as a key issue of international and national concern around the world. [Extracted from the article] Copyright of International Migration is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Mathematical Modeling of Overcoming the COVID-19 Pandemic and Restoring Economic Growth

A mathematical model is proposed that not only generates various scenarios of development, but also forms specific management measures aimed at suppressing the pandemic and restoring economic growth. The developed model of the mutual influence of the pandemic and the economy is not only a tool for effective and adequate forecasting, but is also capable of simulating various scenarios that may well correspond to real epidemiological processes. An advantage of the model is that the dynamics of the pandemic and GDP can be managed in practice in order to stabilize socioeconomic development.

Integration of genetic algorithm with artificial neural network for stock market forecasting

Traditional statistical as well as artificial intelligence techniques are widely used for stock market forecasting. Due to the nonlinearity in stock data, a model developed using the traditional or a single intelligent technique may not accurately forecast results. Therefore, there is a need to develop a hybridization of intelligent techniques for an effective predictive model. In this study, we propose an intelligent forecasting method based on a hybrid of an Artificial Neural Network (ANN) and a Genetic Algorithm (GA) and uses two US stock market indices, DOW30 and NASDAQ100, for forecasting. The data were partitioned into training, testing, and validation datasets. The model validation was done on the stock data of the COVID-19 period. The experimental findings obtained using the DOW30 and NASDAQ100 reveal that the accuracy of the GA and ANN hybrid model for the DOW30 and NASDAQ100 is greater than that of the single ANN (BPANN) technique, both in the short and long term.

Boosters condemned as COVAX reins in supply forecast

See PDF] A day earlier, WHO director-general Tedros Adhanom Ghebreyesus said he was “appalled” by comments from an association of pharmaceutical manufacturers that G7 countries have enough vaccine supplies to fully cover all adults and teenagers, and offer booster shots to at-risk groups. [...]doses may be necessary for the most at-risk populations […] Anthony Fauci, director of the US National Institute of Allergy and Infectious Diseases, stressed the benefits of a third dose, in an online lecture, and said: ”We can do both: we can do a booster programme at the same time as dramatically increase the doses going to low- and middle- income countries, which is the reason why we [the US] have already given over 100 million doses to 90 countries and will be giving a half a billion doses by the time we get into 2022.”

Hybrid LSTM-ARMA Demand-Forecasting Model Based on Error Compensation for Integrated Circuit Tray Manufacturing

Demand forecasting plays a crucial role in a company’s operating costs. Excessive inventory can increase costs and unnecessary waste can be reduced if managers plan for uncertain future demand and determine the most favorable decisions. Managers are demanding increasing accuracy in forecasting as technology advances. Most of the literature discusses forecasting results’ inaccuracy by suspending the model and reloading the data for model retraining and correction, which is extensively employed but causes a bottleneck in practice since users do not have the sufficient ability to correct the model. This study proposes an error compensation mechanism and uses the individuals and moving-range (I-MR) control chart to evaluate the requirement for compensation to solve the current bottleneck using forecasting models. The approach is validated using the case companies’ historical data, and the model is developed using a rolling long short-term memory (LSTM) to output the predicted values;then, five indicators are proposed for screening to determine the prediction statistics to be subsequently employed. Root mean squared error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) compare the LSTM, rolling LSTM combined index, and LSTM-autoregressive moving average (ARMA) models. The results demonstrate that the RMSE, MAPE, and MAE of LSTM-ARMA are smaller than those of the other two models, indicating that the error compensation mechanism that is proposed in this study can enhance the prediction’s accuracy.

Rapid assessment of disaster damage and economic resilience in relation to the flooding in Zhengzhou, China in 2021

The timely and accurate assessment of flooding disasters and economic resilience is significant for post-disaster reconstruction and recovery. In July 2021, the National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) Day/Night Band (DNB) daily data were explored as a proxy to assess the flooding damage caused by heavy rainfall in Zhengzhou City, China. A combination of the night-time light (NTL) changes and the radiation normalization method was used to rapidly identify affected areas and extract populations following the flooding disaster. A daily gross domestic product (GDP) prediction model was developed to evaluate the economic resilience of Zhengzhou City using multi-temporal DNB daily and monthly NTL data. The severity of the disaster was estimated by the extent of power outages, flooding crisis regions, and affected populations. It has been predicted that the Zhengzhou economy is unlikely to be restored to its normal level before the end of 2021 owing to the dual impact of the coronavirus outbreak and flooding disaster;the revised recovery-time prediction is late April 2022. We concluded that our NTL data provided new, simple, and effective insights into the post-flooding assessment of the affected areas, populations, GDP forecast, and economic recovery.

Business forecasting during the pandemic.

The COVID-19 pandemic shock represents a once-in-a-generation challenge to both the global economy and to business forecasting, contributing to elevated economic uncertainty through today. In this article, we perform a retrospective evaluation of some of the workhorse statistical models used by business economists to see which approaches were most resilient during the pandemic shock. We find projection-based approaches were more resilient to the pandemic shock than iteration-based forecasts in the cases we studied. We also find that the pandemic induced significant variation in forecast accuracy among the models which incorporate macroeconomic data. Incorporating alternative high-frequency data which gained currency during the pandemic into these models did not necessarily improve forecast performance, however more research is needed to assess the extent to which these indicators improved business planning.

Editorial Special Section on COVID-19 Impact on Electrical Grid Operation: Analysis and Mitigation

COVID-19-related shutdowns have significantly impacted the electrical grid operation worldwide, as governments put strict measures in place to manage the global pandemic. The global electrical demand plummeted around the planet in March, April, and May 2020, with countries such as Spain and Italy experiencing more than 20% decrease in their usual electric consumption. On the other hand, countries like Canada experienced unusually high summer peaks due to the increase in demand for the residential HVAC systems. Electricity network operators are facing unprecedented challenges in scheduling energy resources;for example, energy forecasting systems struggle to provide an accurate demand prediction given massive changes in patterns of electricity consumption induced by COVID-19 restrictions.

Assessment of Economic Sustainability in the Construction Sector: Evidence from Three Developed Countries (the USA, China, and the UK)

The construction sector plays a significant role in contributing to uplifts in economic stability by generating employment and providing standardized social development. Economic sustainability in the construction sector has been less addressed despite its wide applicability in the economy. This study aimed to perform a comparative analysis to determine the application of a circular economy in the construction sector toward economic sustainability, along with its long-term forecasting. A time series analysis was used on the construction sector of the United States of America (USA), China, and the United Kingdom (UK) from 1970 to 2020, by taking into account individual effects to propose a framework with global validity. Statistical analysis was performed to analyze the dependence of the construction sector and determine its short- and long-term contributions. The results revealed that the construction sectors in these countries tend to bounce back to equilibrium in the case of short-term effects;however, the construction sector behaves differently with respect to each sector after experiencing long-term effects. The results show that the explanatory power of the forecasting model (R2) was found to be 0.997, 0.992, and 0.996 for the USA, China, and the UK. Based on the concept of the circular economy, it was concluded that the USA will become a leader in attaining sustainability in construction owing to its ability to recover quickly from shocks, and that the USA will become the largest construction sector in terms of GDP, with a USD 0.3 trillion higher GDP than that of the Chinese sector. Meanwhile, there will be no significant change in the construction GDP of the UK up to the end of 2050. Moreover, the speeds of the construction sector toward equilibrium in the long run in the USA, China, and the UK, and regaining of their original positions, is 0.267%, 1.04%, and 0.41% of their original positions, respectively. This study has a significance in acting as a guideline for introducing economic and environmental sustainability in construction policies, because of the potential of the construction sectors to recover from possible recessions in their respective countries.

The COVID-19 Impact on Supply Chain Operations of Automotive Industry: A Case Study of Sustainability 4.0 Based on Sense–Adapt–Transform Framework

The COVID-19 pandemic has exposed the vulnerability of global manufacturing companies to their supply chains and operating activities as one of the significant disruption events of the past two decades. It has demonstrated that major companies underestimate the need for sustainable and resilient operations. The pandemic has resulted in significant disruptions especially in the automotive industry. The goal of the study is to determine impact of the COVID-19 on supply chain operations in a Turkish automotive manufacturer and to develop a framework for improving operational activities to survive in the VUCA (volatility, uncertainty, complexity, and ambiguity) environment. The study identifies how the case study company has been affected by the COVID-19 outbreak and what challenges the company faced during the pandemic. A diagnostic survey and semi-structured interviews were used as data sources with qualitative and quantitative analysis. The results showed that the pandemic led to significant disruptions through various factors explained by shortage of raw materials/spare parts, availability of transportation, availability of labors, demand fluctuations, increase in sick leaves, new health and safety regulations. Findings also show the necessity to re-design resilience supply chain management by providing recovery plans (forecasting, supplier selection, simulation, monitoring) which consider different measures in different stages. In addition, the best practices were recommended for the case study by considering internal, external, and technological challenges during the COVID-19 pandemic. Some of the given targeted guidelines and improvement for the automotive company might be applicable in the industrial practices for other organizations. The article concludes with future research directions and managerial implications for successful applications.

Does Google Trends Show the Strength of Social Interest as a Predictor of Housing Price Dynamics?

A recently emerged sustainable information society has ceased to be only a consumer and has become a web-based information source. Society’s online behaviour is tracked, recorded, processed, aggregated, and monetised. As a society, we are becoming a subject of research, and our web behaviour is a source of information for decision-makers (currently mainly business). The research aims to measure the strength of social interest in the housing market (Google Trends), which will then be correlated with the dynamics of housing prices in Poland in the years 2010–2021. The vector autoregressive model was used to diagnose the interrelationships (including Granger causality) and to forecast housing prices. The research showed that web searching for the keyword “dwelling” causes the dynamics of dwelling prices and is an attractive alternative to the classical variables used in forecasting housing market prices.

Accounting the factor of randomity of social processes in prediction of demand for electric energy

Мета. Урахування фактору випадковосл сощальних процесш при прогнозуванш попиту на електричну енерпю для зменшення похибки. Методика. Апарат математично! статистики, методш лшшного програмування, теорп нечггких множин i методiв експертного оцшювання, теорй' шкал, Байесовський п1дх1д до моделей прогнозування, комп'ютерне моделювання. Результаты. Проаналiзована динамiка споживання електрично! енергп за рiзнi перiоди часу, встановлено вплив фактору пандемп на процес формування попиту на електричну енерпю. Розроблена вербально-числова шкала для комплексного оцшювання впливу на попит на електричну енерпю такого складного сощального явища, як пандемш. Сформована модель прогнозування попиту на електричну енерпю з використанням Байесовського подходу та ощнки експерта, що дозволила використати ретроспективш данi споживання електрично! енергп та врахувати невизначенiсть соцiального фактору впливу пандемп. Наукова новизна. Набула подальшого розвитку модель прогнозування попиту на електричну енерпю, яка, на вщмшу в1д iнших, ураховуе фактор випадковостi соцiальних процеив i вербально-числову шкалу, що дозволяе зменшити похибку прогнозування споживання електрично! енергп. Практична значимтсть. Результата дослщження кориснi для пщприемств, що спецiалiзуються на генерацй', передачi й розподшу електрично! енергп споживачам. Представленi результата надають можливють зменшити похибку прогнозування попиту на електричну енерпю при врахуванш фактору випадковосл сощальних процешв.Alternate :Purpose. Taking into account the factor of randomness of social processes when forecasting the demand for electric energy to reduce the error. Methodology. Apparatus of mathematical statistics, linear programming methods, fuzzy set theory and expert assessment methods, scale theory, Bayesian approach to forecasting models, computer modeling. Findings. The dynamics of consumption of electric energy for different periods of time is analyzed, the influence of the pandemic factor on the process of formation of demand for electric energy is established. A verbal-numerical scale has been developed for a comprehensive assessment of the impact on the demand for electric energy of such a complex social phenomenon as a pandemic. A model for forecasting the demand for electrical energy was formed using the Bayesian approach and an expert's assessment, which made it possible to use retrospective data on electrical energy consumption and take into account the uncertainty of the social factor influencing the pandemic. Originality. The model for forecasting the demand for electrical energy has been further developed, which, unlike others, takes into account the factor of randomness of social processes and a verbal-numerical scale, which makes it possible to reduce the error in predicting the consumption of electrical energy. Practic l value. The research results are useful for enterprises specializing in the generation, transmission and distribution of electrical energy to consumers. The presented results make it possible to reduce the error in forecasting the demand for electric energy, taking into account the factor of randomness of social processes.

The role of healthcare supply chain management in the wake of COVID-19 pandemic: hot off the press

Purpose>The study aims to examine the role of health-care supply chain management during the COVID-19 pandemic in a cross-section of 42 selected sub-Saharan African (SSA) countries.Design/methodology/approach>The study used cross-sectional robust least square regression for parameter estimates.Findings>The results confirmed the N-shaped relationship between the health-care logistics performance index (HLPI) and COVID-19 cases. It implies that initially HLPI increases along with an increase in COVID-19 cases. Later down, it decreases COVID-19 cases by providing continued access to medical devices and personal protective equipment. Again, it increases due to resuming economic activities across countries.Practical implications>The continuing health-care supply chain is crucial to minimize COVID-19 cases. The international support from the developed world in providing health-care equipment, debt resettlement and resolving regional conflicts is deemed desirable to escape the SSA countries from the COVID-19 pandemic.Originality/value>The importance of the health-care supply chain during the COVID-19 pandemic is evident in the forecasting estimates, which shows that from August 2021 to April 2022, increasing the health-care supply chain at their third-degree level would reduce coronavirus registered cases. The results conclude that SSA countries required more efforts to contain coronavirus cases by thrice increasing their health-care logistics supply chain.

A Novel Multi-Factor Three-Step Feature Selection and Deep Learning Framework for Regional GDP Prediction: Evidence from China

Gross domestic product (GDP) is an important index reflecting the economic development of a region. Accurate GDP prediction of developing regions can provide technical support for sustainable urban development and economic policy formulation. In this paper, a novel multi-factor three-step feature selection and deep learning framework are proposed for regional GDP prediction. The core modeling process is mainly composed of the following three steps: In Step I, the feature crossing algorithm is used to deeply excavate hidden feature information of original datasets and fully extract key information. In Step II, BorutaRF and Q-learning algorithms analyze the deep correlation between extracted features and targets from two different perspectives and determine the features with the highest quality. In Step III, selected features are used as the input of TCN (Temporal convolutional network) to build a GDP prediction model and obtain final prediction results. Based on the experimental analysis of three datasets, the following conclusions can be drawn: (1) The proposed three-stage feature selection method effectively improves the prediction accuracy of TCN by more than 10%. (2) The proposed GDP prediction framework proposed in the paper has achieved better forecasting performance than 14 benchmark models. In addition, the MAPE values of the models are lower than 5% in all cases.

Economic Effects of the Coronavirus (Covid 19) Epidemic on Businesses: A Study on Adıyaman Park AVM.

This study was carried out in order to reveal how the businesses in Adıyaman Park AVM operating in Adıyaman were economically affected by the Covid 19 epidemic, their perspectives on the applied economic policies and their post-pandemic economic predictions. In this context, a questionnaire consisting of 31 questions was applied to measure the opinions of business managers in Adıyaman Park AVM. The data obtained after the survey study was analyzed in the statistical program. Within the scope of the study, frequency and percentage analyzes were used to determine the demographic characteristics of the participants. On the other hand, t-test and one-way Anova analyzes were used to determine participant views on the economic effects of the Covid 19 epidemic on businesses. Looking at the data obtained, it was seen that 77.5% of the participants had significant concerns about the future. Again, 50% of the participants are afraid of closing their workplaces, while 93.8% think that small-scale businesses will face the risk of closing. On the other hand, 71.9% of the participants stated that they would reduce the number of employees in their workplaces. As a result of this, it was seen that the number of those who stated that the unemployment rate would increase throughout the country was very high. With this study, it was concluded that the Covid 19 epidemic had very negative economic effects on businesses. (English) [ FROM AUTHOR] Bu çalışma, Adıyaman ilinde faaliyet gösteren Adıyaman Park AVM’deki işletmelerin ekonomik olarak Covid 19 salgınından nasıl etkilendikleri, uygulanan ekonomi politikalarına bakış açıları ve pandemi sonrası ekonomik öngörülerini ortaya koymak amacıyla yapılmıştır. Bu kapsamda, Adıyaman Park AVM’deki işletme yöneticilerinin konuyla ilgili görüşlerini ölçmek üzere toplam 31 soruluk bir anket uygulanmıştır. Yapılan anket çalışmasından sonra elde edilen veriler istatistik programında analize tabi tutulmuştur. Çalışma kapsamında, katılımcıların demografik özelliklerinin tespit edilmesi için frekans ve yüzde analizleri kullanılmıştır. Diğer taraftan, Covid 19 salgınının işletmeler üzerindeki ekonomik etkilerine yönelik katılımcı görüşlerini tespit etme noktasında t-testi ve tek yönlü Anova analizlerinden yararlanılmıştır. Elde edilen verilere bakıldığında, katılımcıların %77,5’inin gelecekle ilgili önemli kaygılar duyduğu görülmüştür. Yine katılımcıların %50’si kendi işyerlerini kapatma korkusu yaşarken, %93,8’i ise küçük ölçekli işletmelerin kapanma riskiyle karşı karşıya kalacağını düşünmektedir. Öte yandan katılımcıların %71,9’u iş yerlerindeki çalışan sayısını azaltacağını belirtmiştir. Bunun neticesi olarak ta, ülke çapında işsizlik oranının artacağını ifade edenlerin sayısının çok yüksek düzeyde olduğu görülmüştür. Yapılan bu çalışmayla, Covid 19 salgınının işletmeler üzerinde oldukça olumsuz ekonomik etkiler meydana getirdiği sonucuna ulaşılmıştır. (Turkish) [ FROM AUTHOR] Copyright of Ekev Academic Review is the property of Ekev Academic Review and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)