scholarly journals Impact of Sustainable Development and Environmental Protection on the Volume of Domestic Hard Coal Sales in Poland

Energies ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 443
Author(s):  
Aurelia Rybak ◽  
Ewelina Włodarczyk
Keyword(s):  
Arima Model ◽  
Hard Coal ◽  
Upward Trend ◽  
Factors Affecting ◽  
Explanatory Variables ◽  
Time Period ◽  
World Markets ◽  
Armax Model ◽  
The Impact ◽  
Significant Factors

The paper presents the results of an analysis of the impact of activities related to the implementation of Poland’s climate policy on the level of demand for hard coal. The authors used sets of indicators built by Eurostat during the analysis. The analysis was based on a set of indicators that had not previously been used for this purpose. The applied ARAMAX model made it possible to study the impact of the presented indicators on the volume of demand for hard coal in Poland. They were introduced to the ARMAX model as explanatory variables. The demand for hard coal in Poland was the dependent variable. The set of indicators was verified, and finally only statistically significant factors were used to build the model. The forecast of the demand for hard coal was made until 2022. It showed that the volume of coal sales would systematically fall as long as factors affecting demand remain constant. However, an additional factor was taken into account in the analysis, namely the increase in demand and prices for hard coal on world markets. The ARIMA model was used to forecast price levels for the next 12 months. The forecast indicates that the time series of prices should maintain an upward trend within the examined time period. Building an accurate and reliable forecast is the basis for effective planning of coal production and is adjusted to the demand for this fuel.

scholarly journals The Determinants Of Population Health Spatial Disparities

2014 ◽  
Vol 17 (4) ◽  
pp. 173-185 ◽  
Author(s):  
Emilia Modranka ◽  
Jadwiga Suchecka
Keyword(s):  
Social Development ◽  
Access To Health Care ◽  
Health Care Services ◽  
Empirical Studies ◽  
Factors Affecting ◽  
Explanatory Variables ◽  
Spatial Disparities ◽  
Care Services ◽  
Number Of Factors ◽  
The Impact

Health of the population is one of the basic factors of social development. The results of empirical studies indicate a number of factors determining the level of health of the population related to access to health care services, the level of environmental pollution and the wealth of society. It must be assumed that the observed disparities in the health depend on distributions of particular determinants. The aim of the article is to assess the significance of the main factors affecting the occurrence of spatial disparities in the level of social development districts NTS-4 in terms of health of the population. The analysis was based on estimates of the Spatial Durbin Model (SDM) which takes into account the impact of neighborhood spatial units on level of dependent variable and the explanatory variables. The size of the level of social development in terms of health of the population in the study was approximate by the aggregate value of the index, which is the local component of the Local Human Development Index LHDI.

scholarly journals Methods of assessing the impact of dollarization on economic development

VUZF Review ◽  
2021 ◽  
Vol 6 (1) ◽  
pp. 12-25
Author(s):  
Оlena Chukurna
Keyword(s):  
Economic Development ◽  
Exchange Rate ◽  
Exchange Rates ◽  
Arima Model ◽  
Machine Building ◽  
Building Industry ◽  
Factors Affecting ◽  
Domestic Prices ◽  
The Exchange Rate ◽  
The Impact

The article considers the transformation of the money function as a consequence of the impact of dollarization on the economic development of countries in the global context. The economic substantiation of the process of dollarization of the economy, which is connected with the function of money, is proved. The influence of dollarization on the macro – and macro levels of the economy is substantiated. Approaches to methods of estimating dollarization on the economic development of the country in the context of globalization are proposed. The article defines the degree of dependence of the machine-building industry of Ukraine on the processes of dollarization of the world economy through the use of the effect of transferring the dynamics of changes in exchange rates to the price dynamics in the machine-building industry. Using the ARIMA model, the effect of transferring the exchange rate to prices for mechanical engineering products is proved. The expediency of using the ARIMA forecasting model to predict the further spread of the effect of the change in exchange rates on prices. An approach is proposed to determine the sensitivity of domestic prices for the products of engineering enterprises to changes in the exchange rate through modified elasticity coefficients. It was determined factors affecting the size of the effect of transfer of the exchange rate on domestic prices for the products of machine-building enterprises.

scholarly journals Factors Affecting the Performance of Agri Small and Medium Enterprises with Evidence from Kosovo

2021 ◽  
Vol 13 (2) ◽  
pp. 297-313
Author(s):  
Ekrem Gjokaj ◽  
Diana Kopeva ◽  
Nol Krasniqi ◽  
Henrietta Nagy
Keyword(s):  
Rural Economy ◽  
Small And Medium Enterprises ◽  
International Standards ◽  
Production Potential ◽  
Factors Affecting ◽  
Direct Payments ◽  
Medium Enterprises ◽  
The Impact ◽  
Agriculture Development ◽  
Significant Factors

Abstract The agri SMEs in Kosovo are facing challenges that are reducing competitiveness and preventing it from fulfilling their production potential. The main constraints in increasing productivity and improving competitiveness are the low use of modern techniques and technologies in both production and management of enterprises, lack of funds, the low use of inputs, and the limited ability to meet international standards of food safety. This paper is focused on the analysis of the impact of agricultural SMEs in the rural economy of the country and the problems related to the impact. The data used for this analysis are the data conducted for the Farm Structure Survey (FSS) which includes the farmers’ list from Agricultural Records compiled by the Kosovo Agency of Statistics (KAS) in 2014, as well as the lists of beneficiaries for both direct payments/subsidies and for grants for the period of 2014 to 2017 received by the Agency for Agriculture Development. From the research results, significant factors having an effect on the annual income of agris SMEs are the following: income from the sale of agricultural products, income from subsidies, income from non-agricultural activities, income from salaries, remittances, and income from other activities.

scholarly journals Key factors affecting people’s unwillingness to be confined during the COVID-19 pandemic in Spain: a large-scale population study

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Marina Martinez-Garcia ◽  
Alejandro Rabasa ◽  
Xavier Barber ◽  
Kristina Polotskaya ◽  
Kristof Roomp ◽  
...  
Keyword(s):  
Large Scale ◽  
Psychological Impact ◽  
Important Variable ◽  
Policy Makers ◽  
Factors Affecting ◽  
Time Period ◽  
Economic Support ◽  
Gender And Age ◽  
Scale Population ◽  
The Impact

AbstractPopulation confinements have been one of the most widely adopted non-pharmaceutical interventions (NPIs) implemented by governments across the globe to help contain the spread of the SARS-CoV-2 virus. While confinement measures have been proven to be effective to reduce the number of infections, they entail significant economic and social costs. Thus, different policy makers and social groups have exhibited varying levels of acceptance of this type of measures. In this context, understanding the factors that determine the willingness of individuals to be confined during a pandemic is of paramount importance, particularly, to policy and decision-makers. In this paper, we study the factors that influence the unwillingness to be confined during the COVID-19 pandemic by the means of a large-scale, online population survey deployed in Spain. We perform two types of analyses (logistic regression and automatic pattern discovery) and consider socio-demographic, economic and psychological factors, together with the 14-day cumulative incidence per 100,000 inhabitants. Our analysis of 109,515 answers to the survey covers data spanning over a 5-month time period to shed light on the impact of the passage of time. We find evidence of pandemic fatigue as the percentage of those who report an unwillingness to be in confinement increases over time; we identify significant gender differences, with women being generally less likely than men to be able to sustain long-term confinement of at least 6 months; we uncover that the psychological impact was the most important factor to determine the willingness to be in confinement at the beginning of the pandemic, to be replaced by the economic impact as the most important variable towards the end of our period of study. Our results highlight the need to design gender and age specific public policies, to implement psychological and economic support programs and to address the evident pandemic fatigue as the success of potential future confinements will depend on the population’s willingness to comply with them.

scholarly journals Decision making by NICE: examining the influences of evidence, process and context

2013 ◽  
Vol 9 (2) ◽  
pp. 119-141 ◽  
Author(s):  
Karin H. Cerri ◽  
Martin Knapp ◽  
Jose-Luis Fernandez
Keyword(s):  
Decision Making ◽  
New Technologies ◽  
Multinomial Logistic Regression ◽  
Clinical Excellence ◽  
Outcome Variable ◽  
Data Set ◽  
Factors Affecting ◽  
Explanatory Variables ◽  
Relative Contribution ◽  
The Impact

AbstractThe National Institute for Health and Clinical Excellence (NICE) provides guidance to the National Health Service (NHS) in England and Wales on funding and use of new technologies. This study examined the impact of evidence, process and context factors on NICE decisions in 2004–2009. A data set of NICE decisions pertaining to pharmaceutical technologies was created, including 32 variables extracted from published information. A three-category outcome variable was used, defined as the decision to ‘recommend’, ‘restrict’ or ‘not recommend’ a technology. With multinomial logistic regression, the relative contribution of explanatory variables on NICE decisions was assessed. A total of 65 technology appraisals (118 technologies) were analysed. Of the technologies, 27% were recommended, 58% were restricted and 14% were not recommended by NICE for NHS funding. The multinomial model showed significant associations (p ⩽ 0.10) between NICE outcome and four variables: (i) demonstration of statistical superiority of the primary endpoint in clinical trials by the appraised technology; (ii) the incremental cost-effectiveness ratio (ICER); (iii) the number of pharmaceuticals appraised within the same appraisal; and (iv) the appraisal year. Results confirm the value of a comprehensive and multivariate approach to understanding NICE decision making. New factors affecting NICE decision making were identified, including the effect of clinical superiority, and the effect of process and socio-economic factors.

Impact of Saudi's Membership in Global and Arab Economic Blocs on its Agricultural Foreign Trade

2021 ◽  
Vol 9 (4) ◽  
pp. 230-245
Author(s):  
Rehab Said Ibrahem ◽  
Eman Aish Al brahim
Keyword(s):  
Foreign Trade ◽  
Secondary Data ◽  
Economic Stability ◽  
Kingdom Of Saudi Arabia ◽  
Factors Affecting ◽  
Economic Relations ◽  
Agricultural Exports ◽  
Time Period ◽  
International Economic Relations ◽  
The Impact

In recent years, the world has witnessed many changes in international economic relations, and the global system has crystallized into major economic blocs aimed at strengthening the position of these blocs in the framework of global international trade and economic stability. Accordingly, the research aims to study the impact of the accession of the Kingdom of Saudi Arabia to the membership of some economic blocs on Saudi agricultural foreign trade during the time period (1995-2019). By depend on both descriptive and quantitative statistical analysis. To study the evolution of the value of agricultural exports and imports and the most important factors affecting each of them, used of some indicators of export competitiveness such as a measure of geographical and commodity concentration. Also used the simultaneous equations method to build an econometric model to analyze the structure of agricultural foreign trade and its estimation by Two-Stage least squares (2SLS) method, the paper predicts the future of Saudi agricultural foreign trade. The research relied on secondary data. The results of the study expect that the average per capita share of agricultural exports, imports and agricultural Balance Deficit will reach about 600, 3.600 and 2.900 thousand riyals, respectively, during the year 2026.

The Harvesting Effect on a Fish Population

2016 ◽  
Vol 11 (2) ◽  
pp. 191-204 ◽  
Author(s):  
А.И. Абакумов ◽  
A.I. Abakumov
Keyword(s):  
Fish Population ◽  
Dimensional Structure ◽  
Population Stability ◽  
Criterion Function ◽  
Factors Affecting ◽  
Time Period ◽  
Fishery Impact ◽  
Dimensional Distribution ◽  
Environmental Variations ◽  
The Impact

Fishery is one of the main environmental factors affecting fish population. In this paper we study the dynamics of population’s dimensional distribution under the influence of environmental variations with and without a fishery impact. The characteristics of population under consideration correspond to pelagic or semi-pelagic population, such as the populations of pollack (Theragra chalcogramma) inhabiting the northern seas of Pacific Ocean. To study the effect of fishery upon a population the problem of harvesting optimization with the amount of profit for a given time period as a criterion function is solved. The impact of population replenishment on the population and catch dynamics was studied and the smoothing influence of dimensional structure on the changes of environment and fishery was revealed. The seasonal changes of environment only increase the population stability.

COVID-19 ARIMA Model Forecast: Confirmed, Mortality & Recovered Cases in 13 Caribbean Countries (Preprint)

2020 ◽  
Author(s):  
Debjyoti Talukdar ◽  
Dr. Vrijesh Tripathi
Keyword(s):  
Time Series ◽  
Autocorrelation Function ◽  
Moving Average ◽  
Arima Model ◽  
Information Criterion ◽  
Local Health ◽  
Health Administration ◽  
Model Forecast ◽  
Time Period ◽  
The Impact

BACKGROUND Rapid spread of SARS nCoV-2 virus in Caribbean region has prompted heightened surveillance with more than 350,000 COVID-19 confirmed cases in 13 Caribbean countries namely Antigua and Barbados, Bahamas, Barbados, Cuba, Dominica, Dominican Republic, Grenada, Haiti, Jamaica, Saint Kitts and Nevis, Saint Lucia, Saint Vincent and the Grenadines, Trinidad and Tobago. OBJECTIVE The aim of our study is to analyze the impact of coronavirus (SARS nCoV-2) in 13 Caribbean countries in terms of confirmed cases, number of deaths and recovered cases. Current and projected forecasts using advanced autoregressive integrated moving average (ARIMA) models will enable local health organisations to plan future courses of action in terms of lockdown and managing essential public services. METHODS The study uses the auto regressive integrated moving average (ARIMA) model based upon time series pattern as per data retrieved from John Hopkins University, freely accessible on public domain and used for research and academic purposes. The data was analyzed using STATA 14 SE software between the time period - Jan 22, 2020 till May 27, 2020 using ARIMA time series analysis. It involves generalizing an autoregressive moving average model to better understand the data and predict future points in the time series until June 15, 2020. RESULTS The results show the predicted trend in terms of COVID-19 confirmed, mortality and recovered cases for 13 Caribbean countries. The projected ARIMA model forecast for the time period - May 25, 2020 to May 31, 2020 show 20278 (95% CI 19433.21 - 21123.08) confirmed cases, 631 (95% CI 615.90 - 646.51) deaths and 11501 (95% CI 10912.45 - 12089) recovered cases related to SARS nCoV-2 virus. The final ARIMA model chosen for confirmed COVID-19 cases, number of deaths and recovered cases are ARIMA (4,2,2), ARIMA (2,1,2) and ARIMA (4,1,2) respectively. All chosen models were compared with other models in terms of various factors like AIC/BIC (Akaike Information Criterion/Bayesian Information Criterion), log likelihood, p-value significance, coefficient < 1 and 5% significance. The autocorrelation function (ACF) and partial autocorrelation function (PACF) graphs were plotted to reduce bias and select the best fitting model. CONCLUSIONS As per the results of the forecasted COVID-19 models, there is a steady rise in terms of confirmed, recovered and mortality cases during the time period March 1, 2020 until May 27, 2020. It shows an increasing trend for confirmed and recovered COVID-19 cases and slowing of the number of mortality cases over a period of time. The predicted model will help the local health administration to devise public policies in terms of awareness measures, lockdown and essential health services accordingly.

Geographical Determinants of Quantity and Rating of Online Hotel Reviews in Central Poland

2019 ◽  
Vol 53 ◽  
pp. 5-5 ◽  
Author(s):  
Tomasz Napierała ◽  
Katarzyna Leśniewska-Napierała
Keyword(s):  
Social Media ◽  
Control Variable ◽  
Media Management ◽  
Factors Affecting ◽  
Explanatory Variables ◽  
Average Value ◽  
Media Type ◽  
Central Poland ◽  
Geographical Factors ◽  
The Impact

Purpose. The aim of this enquiry is to determine the impact of various geographical factors on the rating and popularity of hotels in selected social media. Method. Research was conducted for all 193 star-ranked hotels operating in July 2017, in central Poland. However, analysis covered only those establishments which used all considered online review sources (118 star-ranked hotels). The criteria for the selection of social media were their popularity and the ability to rate the hotel by the guests. The analysed social media channels include: Booking.com, GoogleMaps, Facebook, TripAdvisor and Trivago. To achieve the research objective, 15 semi-logarithmic models were estimated to explain the variability of: 1) number of hotel reviews posted on social media sites, 2) average hotel ratings in social media, and 3) multiplied effect of quantity and average value of online hotel ratings; separately for each type of analysed social media. The following geographical explanatory variables were considered: 1) type of location (urban or rural), 2) distance from closest transport modes (road, rail and air), 3) distance to the nearest tourist attractions, 4) distance from the closest administrative centre, 5) distance from the nearest competing hotels. The hotel star-rank was a control variable included in the study. Findings. Among the analysed explanatory variables, the most important geographical factor conditioning the number of hotel service quality assessments in central Poland on social media sties was communication accessibility, and then, the closeness of competing hotels. Regardless of the type of social media, no significant influence of practically any of the analysed geographical determinants on the average value of ratings in social media was identified. Research and conclusions limitations: The models explained the observed variability of variables, finding hotel ratings in social media to be at an insignificant level, between 8% and 33%. Practical implications. Social media management is currently one of the key areas of marketing communication run by hotel enterprises. This article should make those who are responsible for managing social media in business practice more focused on geographical context. Originality. The article is the first known attempt to the authors to assess geographical factors affecting the rating and popularity of hotels in social media. Type of paper. The article presents findings from empirical research.

The impact of digitalization and integration processes on the cost accounting in sausage manufacture

2021 ◽  
Vol 22 (5) ◽  
pp. 490-508
Author(s):  
Natal'ya V. MALINOVSKAYA
Keyword(s):  
Cost Accounting ◽  
Meat Processing ◽  
Factors Affecting ◽  
Production Methods ◽  
Eurasian Economic Union ◽  
Management Concepts ◽  
Analysis And Synthesis ◽  
The Cost ◽  
The Impact ◽  
Significant Factors

Subject. This article explores the impact of integration factors, modern management concepts, as well as digitalization on the most important elements of accounting and calculation tools in sausage production. Objectives. The article aims to identify current factors affecting the organization of cost accounting and choice of methods for calculating the production expenses at meat processing enterprises for sausage production. Methods. The study relies upon analysis and synthesis, comparison, generalization, and abstraction. Results. The article identifies new factors that affect the organization of cost accounting and choice of methods for calculating the production expenses in sausage production in modern conditions. Integration within the framework of the Eurasian Economic Union and digitalization are the new most significant factors that change both the production process and accounting practices. Conclusions. The rise of digitalization in all areas of activity of meat processing enterprises, development of the regulatory framework within the Eurasian economic integration have become significant factors that have a material impact on the most important elements of accounting and calculation tools in sausage production. These factors along with industry characteristics should be taken into account when organizing cost accounting and choosing methods for calculating the production expenses in sausage production. The results obtained can help further develop theoretical aspects of cost accounting and calculation in sausage production.

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