Suchergebnisse

AbstractThis study investigates the effects of productivity, energy consumption, foreign direct investments, and urbanization on carbon dioxide emissions (CO2) in Finland during 2000–2020 using an autoregressive distributed lag (ARDL) model. The results show that (i) there is evidence of cointegration among variables; (ii) energy consumption has a positive effect on CO2 emissions in the long run; (iii) labor productivity and urbanization have a negative effect on CO2 emissions in the long run; (iv) foreign direct investments are not a significant explainer of CO2 emissions. The results are discussed with some policy implications and suggested future research.

The use of advanced ICT technologies and the support of new ways of thinking, acting and working in public administration, together with the increased provision of information and interactive services accessible through various channels, is the foundation of eGovernment. In recent years, there has been visible progress in all EU countries in terms of the general framework for e-government strategy, which is based on best practices and methodologies. The aim of our research is to discover the way in which the EU states are situated from the point of view of the digitalization of the administration. For this I used Gaussian models. The main research parameters were: accessibility; transparency, investments in information and communication technologies and investments in infrastructure related to public administrations in EU countries. The results show significant differences between state administrations. We applied Gaussian Mixture Model clustering in order to make an analysis of the national E-government situation in the European Union for 2018. The GMM algorithm estimated six clusters. We find that the first cluster, with Nordic countries, Netherlands and Austria, has the highest values of telecommunication infrastructure, citizens' access to e-government services and Transparency International's Corruption Perception Index. At the opposite pole, in cluster 2, Romania and Bulgaria have the lowest values of these three indicators, while their public investment levels are not significantly under EU averages. Our research provides not only an overview of the digitization of administrations, but also what are the main lags that state administrations have to recover in order to reach a digital system integrated into the EU's administrative space.

Sustainable economic growth is desired to be achieved by governments targeting economic, social, and environmental benefits. The idea of circular economy model is to consider feedback effects from proper waste management instead of one-way effects typical with the classical linear model. Several sectors of society contribute to circular economy and its monetary and environmental outputs in a sustainable way. The aim of this paper is to analyze the dependencies and causalities of circular economy and economic developments in the EU. The research objectives include testing (i) whether research and development (R&D) expenditure, GDP per capita and generation of municipal waste per capita influence the recycling rate of municipal waste, and (ii) whether R&D expenditure, generation of municipal waste per capita and the recycling rate of municipal waste influence the GDP per capita. The relevant indicators are obtained from Eurostat. The research methods of fixed effects and Tobit approach are used to study the statistical relevance of the two models. The pairwise causality of variables is tested by Dumitrescu–Hurlin causality test. One result of the study is that technology development, by a decreasing life of products, leads to an increase of waste generation. Therefore, environmentally friendly technologies should be produced.

AbstractThis research examines the impact of economic policy uncertainty, GDP, population and renewable energy consumption on CO2 emissions in BRIC countries from 1991 to 2023. The objective is to understand the long-term relationships among these variables and provide relevant insights. Using fully modified ordinary least squares and dynamic ordinary least squares econometric methods, the findings reveal that GDP and population growth significantly increase CO2 emissions, while renewable energy consumption reduces them. The panel autoregressive distributed lag results highlight the need for policies promoting renewable energy and managing population growth to mitigate environmental impacts. Notably, economic policy uncertainty also contributes to higher emissions, underscoring the importance of stable economic policies.

Objective: The objective of the article is to identify the reference group of countries with similar Covid strategies and other groups with their performance success, and to construct a composite Covid Mitigation Index for comparative purposes, thus, implying how to redesign the strategic policies. Research Design & Methods: Gaussian Mixture Modelling and Factor Analysis: the main design is quantitative, using Gaussian Mixture Modelling to find the optimal number of country clusters, and Factor Analysis with Principal Axis Factoring (FA-PAF) to build a composite index of governmental policies. Data includes eight mitigation policy variables and three supporting economic policy variables. Data are aggregated to form three periods and the cluster changes are identified by Gaussian Mixture Modelling. Then, the Covid Mitigation Index (CMI) is constructed by FA-PAF to obtain a comparative measure over the periods and the country clusters. The results were obtained by means of R studio and SPSS. Findings: The dynamic clustering leads to a decreasing number of clusters from nine clusters in the first period (January-February 2020), four clusters in the second period (March-April 2020), and two clusters in the third period (May-June 2020). In the first period, China (with CMI=48) took serious actions forming its own cluster, while 11 other countries (with CMI>10), e.g., early affected European countries such as Italy and Spain and large Asian countries such India and Indonesia, took moderate actions. In the second period all cluster averages were greater than China's in the first period, i.e., most world countries were dedicated to fight Covid-19. In Europe, Italy, San Marino and France showed the highest CMI values, similarly to Iraq and Palestine in the Middle East, Peru and Honduras in the Latin America, and China, India and Indonesia in Asia. In the third period, cluster averages showed even tighter policies even though 42 countries had lower CMI values than previously. Implications & Recommendations: The approach provided a big picture for decision makers both in business and in governments. The key idea was to reveal reference groups of countries which help governmental actors to design and adapt their strategies over time by learning by their own experience and the results of the better performing clusters. It was suggested that a multi-criteria approach accounting for individual government's preferences over health and economy is used along with the presented approach. Contribution & Value Added: Clustering with Gaussian Mixture Models and factor analysis based on Principal Axis Factoring for composite-index building were used. The methods are well-established, but they were applied in a novel way dynamically over time and for the composite CMI. CMI was built on two factors which identified the structure of mitigation policies and economic policies. The development of governmental polices over the first cycle of Covid-19 pandemic was described. ; publishedVersion ; Peer reviewed

This paper proposes two mixed models to study a consumer?s optimal saving in the presence of two types of risk: income risk and background risk. In the first model, income risk is represented by a fuzzy number and background risk by a random variable. In the second model, income risk is represented by a random variable and background risk by a fuzzy number. For each model, three notions of precautionary savings are defined as indicators of the extra saving induced by income and background risk on the consumer?s optimal choice. In conclusion, we can characterize the conditions that allow for extra saving relative to optimal saving under certainty, even when a certain component of risk is modelled using fuzzy numbers.

AbstractAI advancements are poised to substantially modify human abilities in the foreseeable future. They include the integration of Brain–Computer Interfaces (BCIs) to augment cognitive functions, the application of gene editing, and the utilization of AI‐powered robotic exoskeletons to enhance physical strength. This study employs a comprehensive analytical framework combining factor analysis, clustering, ANOVA, and logistic regression to investigate public attitudes toward these transformative technologies. Our findings reveal three distinct clusters of public opinion reflecting varying optimism and concern toward AI technologies. Cluster 1 (1574 participants) held a positive view with high excitement while Cluster 2 (1334 participants) showed a balanced stance. Cluster 3 (2199 participants) expressed heightened concern despite some excitement. Notably, regional disparities, particularly between urban and rural participants, emerge as a prominent factor influencing these attitudes (ANOVA, F = 15.2, p < 0.001). Furthermore, logistic regression identifies key influencers of public perception, highlighting the significant roles played by religion and regional factors. The implications of these findings extend beyond understanding public sentiment. They underscore the need for informed policies that promote education and awareness about AI technologies, address ethical concerns, and engage the public in decision‐making processes. As society navigates this transformative technological landscape, a nuanced understanding of public attitudes becomes paramount, guiding ethical regulation, innovation, and public engagement strategies. This study provides valuable insights into the intricate dynamics surrounding AI acceptance and highlights the importance of adapting measures to evolving perceptions and attitudes among the general public.

The International Conferences on Economics and Social Sciences (ICESS)organized by Bucharest University of Economic Studies provides an opportunity for all those interested in Economics and Social Sciences to discuss and exchange research ideas. The papers presented at the Conference are available online in the Conference Proceedings series (ISSN 2704-6524): Volume 2019 Collaborative Research for Excellence in Economics and Social Sciences, ISBN 9788366675322 Volume 2020 Innovative Models to Revive the Global Economy, ISBN 9788395815072 This conference provides an opportunity for all those interested in Economics and Social Sciences to discuss and exchange research ideas. We welcome both empirical and theoretical work that is broadly consistent with the conference' general theme. Especially, researchers, PhD students and practitioners are invited to submit papers on the topics related to new models in entrepreneurship and innovation, sustainability and education, data science and digitalization, marketing and finance, Fintech & Insurtech etc. that will develop innovative instruments for countries, businesses and education. The innovative models for sustainable development aim to ensure simultaneous economic development, social development, and environmental protection, to achieve a higher quality of life for all people and protect all living beings and the planet. The main topics of the conference are focused on but not limited to the following sections: Fintech & Insurtech - towards a sustainable financial environment The role of innovation in public and private organizations Financial perspectives in turbulent times Global Challenges for Agri-Food Systems and Sustainable Development Economic Policies for Non-Cyclical Crises Education for Sustainable Development: impact of universities on society Marketing and Sustainability The role of accounting in Sustainable Development Global world after crisis: towards a new economic model Sustainability for future business Current challenges within demographic data: measurement, collection, retrieval, analysis and reporting We welcome you to join us for two intensive days of plenary speeches and specialized parallel sessions debates that will result in high quality practical insights and networking. Scientific CommitteeACELEANU Mirela, Bucharest University of Economic Studies, RomaniaALBU Lucian, Academia Romana, RomaniaANGHEL Ion, Bucharest University of Economic Studies, RomaniaARROYO GALLARDO Javier, Complutense University of Madrid, SpainAUSLOOS Marcel, Leicester University, United KingdomBEGALLI Diego, University of Verona, ItalyBELLINI Francesco, Sapienza University of Rome, ItalyBRATOSIN Ștefan, Universite Montpellier 3, FranceCABANIS Andre, Universite Toulouse 1 Capitole, FranceCASTERAN Herbert, EM Strasbourg University, FranceCENȚIU Silvian, Retina Communications, San Francisco, USACERQUETI Roy, Sapienza University of Rome, ItalyCHAVEZ Gilbert, Globis University Tokyo, JapanCOSTICÃ Ionela, Bucharest University of Economic Studies, RomaniaCOX Michael, London School of Economics, England, UKD'ASCENZO Fabrizio, Sapienza University of Rome, ItalyDIMA Alina Mihaela, Bucharest University of Economic Studies, RomaniaDÂRDALÃ Marian, Bucharest University of Economic Studies, RomaniaDUMITRESCU Dan Gabriel, Bucharest University of Economic Studies, RomaniaDUMITRU Ovidiu, Bucharest University of Economic Studies, RomaniaFELEAGÃ Liliana, Bucharest University of Economic Studies, RomaniaFONSECA Luis Miguel, Polytechnic of Porto, PortugalGARCÍA-GOÑI Manuel, Universitad Complutense de Madrid, SpainGIUDICI Paolo, The University of Pavia, ItalyGRUBOR Aleksandar, University of Novi Sad, SerbiaHÄRDLE Wolfgang Karl, Humboldt University of Berlin, GermanyHURDUZEU Gheorghe, Bucharest University of Economic Studies, RomaniaISTUDOR Nicolae, Bucharest University of Economic Studies, RomaniaKOKUSHO Kyoko, IBM Tokyo, JapanLOMBARDI Mariarosaria, University of Foggia, ItalyMEHMANPAZIR Babak, EM Strasbourg University, FranceMIRON Dumitru, Bucharest University of Economic Studies, RomaniaNABIRUKHINA Anna Vadimovna, Saint Petersburg State University, RussiaNICA Elvira, Bucharest University of Economic Studies, RomaniaNIJKAMP Peter, Jeronimus Academy of Data Science Den Bosch, NetherlandsNOVO CORTI Maria Isabel, Universidade da Coruña, SpainORDÓÑEZ MONFORT Javier, Jaume I University, SpainPANETTA Roberto, Bocconi University, ItalyPARASCHIV Dorel Mihai, Bucharest University of Economic Studies, RomaniaPICATOSTE Xose, Universidad Autonoma de Madrid, SpainPIROȘCÃ Grigore, Bucharest University of Economic Studies, RomaniaPOINT Sébastien, EM Strasbourg University, FrancePOPA Ion, Bucharest University of Economic Studies, RomaniaPROFIROIU Marius Constantin, Bucharest University of Economic Studies, RomaniaRICHMOND Peter, Trinity College Dublin, IrelandSÂRBU Roxana, Bucharest University of Economic Studies, RomaniaSINGER Slavica, J.J. Strossmayer University of Osijek, CroatiaSMEUREANU Ion, Bucharest University of Economic Studies, RomaniaSTAMULE Tãnase, Bucharest University of Economic Studies, RomaniaSTATE Radu, University of Luxembourg, LuxembourgSTOIAN Mirela, Bucharest University of Economic Studies, RomaniaSTRAT Vasile Alecsandru, Bucharest University of Economic Studies, RomaniaSTREET Donna, University of Dayton, USATEIXEIRA DOMINGUES José Pedro, University of Minho, PortugalȚIGU Gabriela, Bucharest University of Economic Studies, RomaniaVALDEBENITO Carlos Ramirez, University of Chile, Santiago de Chile, ChileVEGHEȘ Cãlin Petricã, Bucharest University of Economic Studies, RomaniaVERHOEF Peter, University of Groningen, NetherlandsVOLKMANN Christine Katharina, Schumpeter School of Business and Economics, Bergische Universität Wuppertal, GermanyWALTER FARKAS Erich, University of Zurich, SwitzerlandWIERENGA Berend, Rotterdam School of Management, NetherlandsWOODS Michael, University of Aberystwyth, Wales, UKZIMMERMANN Klaus F., Bonn University (em.) end Global Labor Organization, Germany Open Access Statement These conference proceedings are Open Access proceedings that allow a free unlimited access to all its contents without any restrictions upon publication to all users. Open Access License These conference proceedings provide immediate open access to its content under the Creative Commons BY-NC-ND 4.0. Authors who publish with these proceedings retain all copyrights and agree to the terms of the above-mentioned CC BY-NC-ND 4.0 license. ABSTRACTING & INDEXING Innovative Models to Revive the Global Economy is covered by the following services: Directory of Open Access Books (DOAB) EBSCO Discovery Service Google Scholar Naviga (Softweco) Primo Central (ExLibris) ReadCube Summon (ProQuest) TDOne (TDNet) WorldCat (OCLC)