Hasil untuk "Commercial geography. Economic geography"

NECULA ADINA-THEODORA, TĂNASE ANDREEA GABRIELA, MĂLDĂREANU ANDREEA et al.

Business and production systems can embrace the circular economy’s principles if they are combined with advanced technologies from the Fourth Industrial Revolution — Industry 4.0. This paper discusses and shows how deployment of the IoT, AI and analytics technologies resolves important challenges of effective resource management, recovering waste, and optimizing product life cycles. However, it is also important to analyze what kind of advantages circular economy can provide beyond just the environmental ones. These advantages include, but are not limited to, cost savings, differentiation from competitors and even supply chain resilience. Circular economy principles are not just an add-on; they can be successfully integrated with Industry 4.0 practices and technologies to create a more sustainable and cost-effective business model.

Abdulrazaq Kamal Daudu, Sidiqat Adeyemi Aderinoye-Abdulwahab, Oyedola Waheed Kareem et al.

Abstract Rapid urbanization in Nigeria presents challenges to urban agriculture, particularly in market access and transportation, which are further shaped by gendered barriers to digital technology use. This study explores how gender influences access to e-logistics platforms and the resulting impact on farm sales in Southwest Nigeria. Drawing on a survey of 420 urban farming households, our findings reveal significant gender gaps: male-headed households are more likely to use e-logistics and benefit from them than female-headed households. Key factors influencing access include digital literacy, mobile phone ownership, farm size, and market proximity, while women face additional constraints such as limited credit and mobility. E-logistics platforms improved farm sales overall, with greater relative benefits for poorer farmers, though gains for women remained smaller than for men. These results highlight the transformative potential of e-logistics in improving market efficiency and reducing inequality, while underscoring the need for gender-sensitive training, improved credit access, and inclusive platform design to ensure equitable benefits.

ShuiDe Wen

In the study by Chen et al. (2023) [01], the large language model GPT demonstrated economic rationality comparable to or exceeding the average human level in tasks such as budget allocation and risk preference. Building on this finding, this paper further incorporates specialized agents, such as biotechnology experts and economists, for a horizontal comparison to explore whether specialization can enhance or maintain economic rationality equivalent to that of GPT in similar decision-making scenarios. The results indicate that when agents invest more effort in specialized fields, their decision-making behavior is more prone to 'rationality shift,' specifically manifested as increased violations of GARP (Generalized Axiom of Revealed Preference), decreased CCEI (Critical Cost Efficiency Index), and more significant decision deviations under high-risk conditions. In contrast, GPT and more generalized basic agents maintain a more stable and consistent level of rationality across multiple tasks. This study reveals the inherent conflict between specialization and economic rationality, providing new insights for constructing AI decision-making systems that balance specialization and generalization across various scenarios.

Timothy F Leslie, Hossein Amiri, Andreas Züfle

We present a comprehensive dataset capturing patterns of human mobility across the United States from January 2019 to January 2023, based on anonymized mobile device data. Aggregated weekly, the dataset reports visits, travel distances, and time spent at public locations organized by economic sector for approximately 12 million Points of Interest (POIs). This resource enables the study of how mobility and economic activity changed over time, particularly during major events such as the COVID-19 pandemic. By disaggregating patterns across different types of businesses, it provides valuable insights for researchers in economics, urban studies, and public health. To protect privacy, all data have been aggregated and anonymized. This dataset offers an opportunity to explore the dynamics of human behavior across sectors over an extended time period, supporting studies of mobility, resilience, and recovery.

Sara Baldoni, Salim Benhamadi, Federico Chiariotti et al.

With the unprecedented diffusion of virtual reality, the number of application scenarios is continuously growing. As commercial and gaming applications become pervasive, the need for the secure and convenient identification of users, often overlooked by the research in immersive media, is becoming more and more pressing. Networked scenarios such as Cloud gaming or cooperative virtual training and teleoperation require both a user-friendly and streamlined experience and user privacy and security. In this work, we investigate the possibility of identifying users from their movement patterns and data traffic traces while playing four commercial games, using a publicly available dataset. If, on the one hand, this paves the way for easy identification and automatic customization of the virtual reality content, it also represents a serious threat to users' privacy due to network analysis-based fingerprinting. Based on this, we analyze the threats and opportunities for virtual reality users' security and privacy.

Maxim Chupilkin

Would artificial intelligence (AI) cut interest rates or adopt conservative monetary policy? Would it deregulate or opt for a more controlled economy? As AI use by economic policymakers, academics, and market participants grows exponentially, it is becoming critical to understand AI preferences over economic policy. However, these preferences are not yet systematically evaluated and remain a black box. This paper makes a conjoint experiment on leading large language models (LLMs) from OpenAI, Anthropic, and Google, asking them to evaluate economic policy under multi-factor constraints. The results are remarkably consistent across models: most LLMs exhibit a strong preference for high growth, low unemployment, and low inequality over traditional macroeconomic concerns such as low inflation and low public debt. Scenario-specific experiments show that LLMs are sensitive to context but still display strong preferences for low unemployment and low inequality even in monetary-policy settings. Numerical sensitivity tests reveal intuitive responses to quantitative changes but also uncover non-linear patterns such as loss aversion.

BOUHMIDI Souhila

Sovereign Wealth Funds SWFs play a crucial role in global financial markets, particularly during periods of crisis. This paper delves into the impact of SWF interventions on inflation rates amidst financial crises. By analyzing historical data of some emerging country how possessed a SWFs, the paper examines how SWFs actions affect inflation dynamics. Additionally, the paper investigates the relationship between SWF interventions and inflation rate. Through empirical analysis, the paper provides insights into the extent to which SWF interventions contribute to inflation volatility and stability during times of crisis. Understanding the interplay between SWFs and inflation is essential for macroeconomic stability in the face of financial turmoil.

BABUCEA ANA-GABRIELA

Databases are data collections organized for easy access and management, available simultaneously to multiusers. Access to the data can be controlled through various permissions, ensuring that users have the appropriate level of access based on their roles. This structure not only enhances data security but also improves collaboration among users, making it easier to share and analyze information efficiently. Databases are controlled through database management systems (DBMS) that allow users to add, modify, delete, locate, and view database data using specific languages provided in most database management software packages. The ability to query the database for particular information is the most powerful capability of any database management system. If we mean that databases have evolved to facilitate tasks such as searching, sorting, or reporting data and updating it based on user requirements, these tasks may be simple in essence, but when vast volumes of data are handled that are frequently accessed by multi users or stand-alone systems, the databases themselves become quite complex. This paper aims to compare the different database systems (SQL, NoSQL, and NewSQL), respectively, and the database models implemented by them from the perspective of the characteristics, similarities, and differences, as well as the advantages and disadvantages of their use in a concrete situations. Since each database type has advantages and disadvantages based on the particular context of use, selecting a database type is never simple and always necessitates a thorough examination of the application requirements and long-term benefits. This paper concentrated on evaluating the three main database types, SQL, NoSQL, and NewSQL, by comparing their essential features, assessing their benefits and limitations, and exploring their most suitable applications across various use cases. The study's conclusions could help choose the best database based on the business process requirements for the enterprise's tasks.

Xiaoyu Hou, Tianyi Zhou, Xianyuan Chang et al.

The co-evolution of economic and ecological activities represents one of the fundamental challenges in the realm of sustainable development. This study on the word trends in mainstream newspapers from the UK and China reveals that both early-industrialised countries and latecomers follow three modes of economic and ecological co-evolution. First, both economic and ecological words demonstrate an S-shaped growth trajectory, and the mode underscores the importance of information propagation, whilst also highlighting the crucial role of self-organisation in the accept society. Second, the co-occurrence of these two type words exhibits a Z-shaped relationship: for two-thirds of the observed period, they display synergistic interactions, while the remaining time shows trade-offs. Lastly, the words related to ecological degradation follow M-shaped trajectories in parallel with economic growth, suggesting periodic disruptions and reconstructions in their interrelationships. Our findings contribute to a more nuanced understanding of the co-evolutionary mechanisms that govern collective behaviours in human society.

Dorinel Bastide, Stéphane Crépey

Based on supermodularity ordering properties, we show that convex risk measures of credit losses are nondecreasing w.r.t. credit-credit and, in a wrong-way risk setup, credit-market, covariances of elliptically distributed latent factors. These results support the use of such setups for computing credit provisions and economic capital or for conducting stress test exercises and risk management analysis.

Aduwo Olola Olayeye

The study examines the effect of International Financial Reporting Standard (IFRS) on performance of quoted manufacturing firms in Nigeria. Secondary source of data was employed through which data were collected from annual report of the selected firms. The study made use of panel data regression. The study covers the period of twelve years from 2011 to 2022 and purposive sampling technique was used to select thirteen consumer goods firms that are constantly trading on the Nigerian Group of Exchange. The study found that IFRS adoption has negative and insignificant effect on financial performance of manufacturing companies in Nigeria. The explanation for this could be as a result of significant adoption cost which will invariably affect the financial performance of the manufacturing companies. The study concluded that the cost of introducing IFRS should be reduced in order to enhance performance of quoted manufacturing firm. In view of this, the study recommends that the management of consumer goods manufacturing firm should consider the cost of implementation and fluctuation in reported values due to change in valuation.

Sam Laskaris

In 1995, the Cando Economic Developer of the Year Award was created to recognize and promote recent or longstanding Indigenous economic development initiatives throughout Canada. All winners, past and present, share a desire to advance their communities as each pursues a vision of sustainable economic self-sufficiency

Christian Diem, András Borsos, Tobias Reisch et al.

To estimate the reaction of economies to political interventions or external disturbances, input-output (IO) tables -- constructed by aggregating data into industrial sectors -- are extensively used. However, economic growth, robustness, and resilience crucially depend on the detailed structure of non-aggregated firm-level production networks (FPNs). Due to non-availability of data little is known about how much aggregated sector-based and detailed firm-level-based model-predictions differ. Using a nearly complete nationwide FPN, containing 243,399 Hungarian firms with 1,104,141 supplier-buyer-relations we self-consistently compare production losses on the aggregated industry-level production network (IPN) and the granular FPN. For this we model the propagation of shocks of the same size on both, the IPN and FPN, where the latter captures relevant heterogeneities within industries. In a COVID-19 inspired scenario we model the shock based on detailed firm-level data during the early pandemic. We find that using IPNs instead of FPNs leads to errors up to 37% in the estimation of economic losses, demonstrating a natural limitation of industry-level IO-models in predicting economic outcomes. We ascribe the large discrepancy to the significant heterogeneity of firms within industries: we find that firms within one sector only sell 23.5% to and buy 19.3% from the same industries on average, emphasizing the strong limitations of industrial sectors for representing the firms they include. Similar error-levels are expected when estimating economic growth, CO2 emissions, and the impact of policy interventions with industry-level IO models. Granular data is key for reasonable predictions of dynamical economic systems.

Iram Gleriaa, Sergio Da Silvab, Leon Brenig et al.

In this study, we analyze the relationship between human population growth and economic dynamics. To do so, we present a modified version of the Verhulst model and the Solow model, which together simulate population dynamics and the role of economic variables in capital accumulation. The model incorporates support and foraging functions, which participate in the dynamic relationship between population growth and the creation and destruction of carrying capacity. The validity of the model is demonstrated using empirical data.

TIRAU ADRIAN, COZMA ION

The transfer pricing case is a method that applies to those economic entities that carry out transactions between related parties, as is the case of the economic entity under review in the auto parts industry, in the context of tax authorities not wanting to allow companies to evade paying taxes, thereby taking unfair advantage of low-income jurisdictions or so-called tax havens. Their tax base suffers as a result of companies' use of questionable transfer pricing policies. In the present research we conducted an empirical analysis at a company in the automotive components industry, where we aimed to make a forecast of the gross trade margin based on the volume of expenses and revenues achieved in the first half of the analyzed year, we want to calculate the MoTC forecast until the end of the year.

Ahmed Benmihoub, Samia Akli, Fatah Ameur et al.

The saffron growing has emerged over the last decade in Algeria and has diffused in different agroclimatic regions, especially in the M'Zab valley and its periphery in the Saharan region of the country. This study sets out to analyze the emerging saffron value chain in the M’Zab valley and suggests strategic options for strengthening its development. The analysis was based on a case study. The methodology applied the tools of value chain analysis in its positive and normative dimensions: a combination of qualitative and quantitative approaches based on the triangulation of data collection methods (interviews, secondary data collection, direct observations). A participatory approach was used to make a SWOT analysis and to propose strategies for developing the value chain. The results showed that the local saffron value chain has strengths (proftabilty and quality) and opportunities (growing demand of healthy and natural products), but its performance is limited by weaknesses (producers skills, marketing) and threats (Fraud or counterfeiting related to imported saffron) mainly related to the deficiencies in the institutional environment. Recommendations are made for elaborating a national saffron export strategy and building a competitive value chain able to take advantage of the expected growth in world demand.

Lukas Schwenkel, Alexander Hadorn, Matthias A. Müller et al.

In this work, we study economic model predictive control (MPC) in situations where the optimal operating behavior is periodic. In such a setting, the performance of a standard economic MPC scheme without terminal conditions can generally be far from optimal even with arbitrarily long prediction horizons. Whereas there are modified economic MPC schemes that guarantee optimal performance, all of them are based on prior knowledge of the optimal period length or of the optimal periodic orbit itself. In contrast to these approaches, we propose to achieve optimality by multiplying the stage cost by a linear discount factor. This modification is not only easy to implement but also independent of any system- or cost-specific properties, making the scheme robust against online changes therein. Under standard dissipativity and controllability assumptions, we can prove that the resulting linearly discounted economic MPC without terminal conditions achieves optimal asymptotic average performance up to an error that vanishes with growing prediction horizons. Moreover, we can guarantee practical asymptotic stability of the optimal periodic orbit under the additional technical assumption that dissipativity holds with a continuous storage function. We complement these qualitative guarantees with a quantitative analysis of the transient and asymptotic average performance of the linearly discounted MPC scheme in a numerical simulation study.

Tarek Ben Hassen, Hamid EL BILALI

Innovation plays an essential role in addressing the interlinked environmental, social, and economic challenges facing the agri-food sectors in the North Africa region. This systematic review analyses the state of research on innovation in North African agri-food sector and investigates whether sustainabil-ity is addressed in the research strand. The analysis shows an increasing interest in the research field, although many publications are authored by scholars based in institutions outside North Africa. Most of the selected documents deal with crops and the production stage of the food chain. The focus is generally on technical innovations while social, organizational, and marketing ones are overlooked. There are growing attempts to connect innovation to sustainability and sustainable development by moving towards the concept of ‘sustainable innovation’. Factors hindering agri-food innovation relate to policy, research, institutional environment, extension, and human capital. The promotion of innovation in the North Afri-can agri-food sector is crucial to unlock the sector’s potential and improve its competiveness, resilience, and sustainability.

Kazuhiro Seki, Yusuke Ikuta, Yoichi Matsubayashi

This paper presents an approach to measuring business sentiment based on textual data. Business sentiment has been measured by traditional surveys, which are costly and time-consuming to conduct. To address the issues, we take advantage of daily newspaper articles and adopt a self-attention-based model to define a business sentiment index, named S-APIR, where outlier detection models are investigated to properly handle various genres of news articles. Moreover, we propose a simple approach to temporally analyzing how much any given event contributed to the predicted business sentiment index. To demonstrate the validity of the proposed approach, an extensive analysis is carried out on 12 years' worth of newspaper articles. The analysis shows that the S-APIR index is strongly and positively correlated with established survey-based index (up to correlation coefficient r=0.937) and that the outlier detection is effective especially for a general newspaper. Also, S-APIR is compared with a variety of economic indices, revealing the properties of S-APIR that it reflects the trend of the macroeconomy as well as the economic outlook and sentiment of economic agents. Moreover, to illustrate how S-APIR could benefit economists and policymakers, several events are analyzed with respect to their impacts on business sentiment over time.

Petra Maresova, Ivan Soukal, Ruzena Stemberkova et al.

Abstract For a successful economy, the interaction between research and its application in practice plays a key role. The aim of this paper is to analyze the institutional factors of patent activity at universities in the Czech Republic. The following five factors were selected for detailed investigation: budget from government, number of employees: professors, associate professor, and assistant professor, number of researchers and other employees, number of students (PhD, master, and bachelor), technology transfer workers. The patent activity analysis used methods of retrospective data analysis in the field of R&D activities, unstructured interviews, and statistical data processing. Following from the basic characteristics of the collected data, patent activity is related to the nature of universities, where technical universities are in leading positions in the monitored period of industrial-legal protection. However, the importance of encouraging innovation activity across all disciplines is crucial. The result of monitoring the dependency shows that a relationship of efficiencies and patent output is influenced by the size of a university. Secondly, there can be observed that technology transfer efficiency contributes more to the patent output that research staff efficiency.