Hasil untuk "Commercial geography. Economic geography"

PURCAREA LIVIU , RADULESCU CARMEN VALENTINA , MANESCU ANDREEA MARIA

Blockchain technology has rapidly transformed the way decentralized systems operate, offering new possibilities for transparency, security, and autonomy. However, these benefits come with a notable drawback: the significant environmental cost associated with blockchain consensus mechanisms — particularly Proof of Work (PoW) Möser et all (2021). This paper examines the environmental impact of blockchain and investigates more sustainable alternatives, such as Proof of Stake (PoS) and other energy-efficient models. Using Ethereum’s transition from PoW to PoS as a central case study, along with examples such as Algorand and Chia, we explore how different architectural decisions affect energy consumption. Our analysis, based on recent academic research and technical data, suggests that sustainable blockchain models are technically viable—but their success depends on broader systemic changes, including clear regulations, governance reforms, and industry-wide engagement. In conclusion, blockchain can evolve into a sustainable technology, but only through a responsible and coordinated effort

Meghan Borland, Evelien VanderKloet, Anja Samardzic et al.

A collaborative and holistic approach is essential to achieving a healthy and resilient aquatic ecosystem. Apoqnmatulti’k (Mi’kmaw for “we help each other”) is a partnership that involves the Unama’ki Institute of Natural Resources, the Confederacy of Mainland Mi’kmaq, commercial fisher Darren Porter, the Ocean Tracking Network, Acadia University, Dalhousie University, and Fisheries and Oceans Canada-Science. Apoqnmatulti’k is founded on the shared participation of Mi’kmaw, local, and Western scientific knowledge holders, aiming to better understand valued aquatic species in Pitu’pa’q (Bras d’Or Lake) and Pekwitapa’qek (Minas Basin). Guided by the principle of Etuaptmumk (Two-Eyed Seeing), Apoqnmatulti’k serves as a model for how the incorporation of diverse perspectives can enhance knowledge, ensure transparency and accessibility of information, and transform fisheries management and conservation. This paper focuses on the challenges, lessons learned, and achievements derived from collaboration and the development of a strong partnership.

ENE CEZAR CATALIN

This study investigates the role of artificial neural networks (ANN) and deep learning (DL) in the financial sector, focusing on their theoretical applications. ANN and DL have significantly reshaped financial analysis due to their capacity to identify complex patterns and improve market predictions. Mimicking the computational structure of the human brain, ANN processes interconnected data points enabling efficient analysis and forecasting. Deep learning, which is a specialized branch of machine learning, utilizes multiple layers of neural networks, offering enhanced capabilities to model relationships and process large datasets. This has led to advancements in predicting stock prices, market trends, and managing financial risks. The discussion covers key architectures, including feedforward neural networks, convolutional neural networks (CNN), and recurrent neural networks (RNN) with specific emphasis on advanced forms like Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRU). These models effectively capture dependencies in time-series data, making them valuable for market forecasting, sentiment analysis, and detecting anomalies. The use of deep learning approaches in finance extends beyond traditional prediction tasks, providing new insights for automated trading systems and risk assessment tools. Artificial neural networks and deep learning enhance modeling capabilities by improving accuracy and adaptability, thereby aiding decision-making within dynamic financial contexts. This analysis highlights their contributions to refining predictive performance and advancing the comprehension of intricate financial systems.

Ivan Kudrenko, Almas Mukhametov, Emin Shahin Aslanov

Abstract Global economic and environmental challenges in the development of contemporary society underscore the importance of addressing issues related to the structural modification of supply chains within the context of a circular economy. The goal of the study was to identify structural modifications in supply chains in a circular economy, as well as to assess the effectiveness of adapting them to new management approaches through the introduction of information technologies. To achieve this, the research employs a range of statistical methods, including Six Sigma, Pareto analysis, Theory of Constraints, and regression analysis, to identify and address weaknesses within the supply chain. Mathematical computations are utilized to evaluate the effectiveness and necessity of implemented technologies within the supply chain links. The findings of the study demonstrated that the adaptation of supply chains to the conditions of a circular economy significantly reduces operational errors and improves service quality. Specifically, the implementation of blockchain technologies substantially enhances process transparency, increases trust among supply chain participants, and reduces data management costs. However, the transition to circular models encounters several significant challenges, not only of an economic nature but also of social and legal dimensions. The scientific significance of the research lies in the systematization of data on the main directions of supply chain evolution within the framework of a circular economy and the development of a methodology for evaluating the effectiveness of supply chain adaptation to new economic and technological prospects for the development of economic systems. The proposed approaches will enable companies to manage business processes more efficiently, optimize resource management systems, and enhance their resilience to external environmental challenges. Addressing these tasks is a key factor in the successful adaptation of enterprises to dynamic changes in the global economy. The practical application of the results will allow companies not only to reduce operational costs but also to improve the quality of products and services, thereby enhancing their competitiveness in the global market.

Carlo Bottai, Jacopo Di Iorio, Martina Iori

Economic growth results from countries' accumulation of organizational and technological capabilities. The Economic and Product Complexity Indices, introduced as an attempt to measure these capabilities from a country's basket of exported products, have become popular to study economic development, the geography of innovation, and industrial policies. Despite this reception, the interpretation of these indicators proved difficult. Although the original Method of Reflections suggested a direct interconnection between country and product metrics, it has been proved that the Economic and Product Complexity Indices result from a spectral clustering algorithm that separately groups similar countries or similar products, respectively. This recent approach to economic and product complexity conflicts with the original one and treats separately countries and products. However, building on previous interpretations of the indices and the recent evolution in spectral clustering, we show that these indices simultaneously identify two co-clusters of similar countries and products. This viewpoint reconciles the spectral clustering interpretation of the indices with the original Method of Reflections interpretation. By proving the often neglected intimate relationship between country and product complexity, this approach emphasizes the role of a selected set of products in determining economic development while extending the range of applications of these indicators in economics.

Vladimir Skavysh, Sofia Priazhkina, Diego Guala et al.

Computational methods both open the frontiers of economic analysis and serve as a bottleneck in what can be achieved. We are the first to study whether Quantum Monte Carlo (QMC) algorithm can improve the runtime of economic applications and challenges in doing so. We provide a detailed introduction to quantum computing and especially the QMC algorithm. Then, we illustrate how to formulate and encode into quantum circuits (a) a bank stress testing model with credit shocks and fire sales, (b) a neoclassical investment model solved with deep learning, and (c) a realistic macro model solved with deep neural networks. We discuss potential computational gains of QMC versus classical computing systems and present a few innovations in benchmarking QMC.

Jonghyun Kim, Donghyeon Yu, Hyoji Choi et al.

This study introduces a metric designed to measure urban structures through the economic complexity lens, building on the foundational theories of urban spatial structure, the Central Place Theory (CPT) (Christaller, 1933). Despite the significant contribution in the field of urban studies and geography, CPT has limited in suggesting an index that captures its key ideas. By analyzing various urban big data of Seoul, we demonstrate that PCI and ECI effectively identify the key ideas of CPT, capturing the spatial structure of a city that associated with the distribution of economic activities, infrastructure, and market orientation in line with the CPT. These metrics for urban centrality offer a modern approach to understanding the Central Place Theory and tool for urban planning and regional economic strategies without privacy issues.

Yingnan Zhang, H. Long, Li Ma et al.

Abstract In recent years, China's rural areas have undergone intense restructuring motivated by various flows derived from e-commerce, which has triggered a new wave of rural rejuvenation. Attempting to reveal the process and mechanism of rural economic restructuring driven by e-commerce, this paper takes Xiaying village in central China to conduct an empirical study by introducing the theory of “space of flows” and applying the method of semi-structured interview. The results show that e-commerce has become a technical catalyst to the variation of industry structure, employment pattern and household economy. Xiaying village has performed a leap from traditional agriculture to commercial service and constructed a complete e-commerce oriented industry chain, which is distinguished from the traditional path of rural modernization adhering to the gradual evolution of the primary, secondary and tertiary industry in China. In response, the employment pattern is diversified and tends to be de-agriculturalization, thus providing an economic advancement opportunity for rural households. As a matter of fact, rural elites, technology innovation (e-commerce platform), resource endowments and government support all contributed greatly to this restructuring process. What distinguishes it from others is the strong mobility and exchange of urban and rural elements, which functions as the initial engine. Essentially, this transition can be considered as the impact of the network on the geographic space restructuring rural economy.

R. Ávila-Sosa, G. Nevárez-Moorillón, C. Ochoa-Velasco et al.

This review aims to evaluate the state of saffron’s main bioactive compounds and their relationship with its commercial quality. Saffron is the commercial name for the dried red stigmas of the Crocus sativus L. flower. It owes its sensory and functional properties mainly to the presence of its carotenoid derivatives, synthesized throughout flowering and also during the whole production process. These compounds include crocin, crocetin, picrocrocin, and safranal, which are bioactive metabolites. Saffron’s commercial value is determined according to the ISO/TS3632 standard that determines their main apocatotenoids. Other techniques such as chromatography (gas and liquid) are used to detect the apocarotenoids. This, together with the determination of spectral fingerprinting or chemo typing are essential for saffron identification. The determination of the specific chemical markers coupled with chemometric methods favors the discrimination of adulterated samples, possible plants, or adulterating compounds and even the concentrations at which these are obtained. Chemical characterization and concentration of various compounds could be affected by saffron’s geographical origin and harvest/postharvest characteristics. The large number of chemical compounds found in the by-products (flower parts) of saffron (catechin, quercetin, delphinidin, etc.) make it an interesting aromatic spice as a colorant, antioxidant, and source of phytochemicals, which can also bring additional economic value to the most expensive aromatic species in the world.

Oleg V. Pavlov, Jason M. Sardell

This chapter develops a feedback economic model that explains the rise of the Sicilian mafia in the 19th century. Grounded in economic theory, the model incorporates causal relationships between the mafia activities, predation, law enforcement, and the profitability of local businesses. Using computational experiments with the model, we explore how different factors and feedback effects impact the mafia activity levels. The model explains important historical observations such as the emergence of the mafia in wealthier regions and its absence in the poorer districts despite the greater levels of banditry.

Yan Luo, Haoyi Duan, Ye Liu et al.

Location recommendation plays a vital role in improving users' travel experience. The timestamp of the POI to be predicted is of great significance, since a user will go to different places at different times. However, most existing methods either do not use this kind of temporal information, or just implicitly fuse it with other contextual information. In this paper, we revisit the problem of location recommendation and point out that explicitly modeling temporal information is a great help when the model needs to predict not only the next location but also further locations. In addition, state-of-the-art methods do not make effective use of geographic information and suffer from the hard boundary problem when encoding geographic information by gridding. To this end, a Temporal Prompt-based and Geography-aware (TPG) framework is proposed. The temporal prompt is firstly designed to incorporate temporal information of any further check-in. A shifted window mechanism is then devised to augment geographic data for addressing the hard boundary problem. Via extensive comparisons with existing methods and ablation studies on five real-world datasets, we demonstrate the effectiveness and superiority of the proposed method under various settings. Most importantly, our proposed model has the superior ability of interval prediction. In particular, the model can predict the location that a user wants to go to at a certain time while the most recent check-in behavioral data is masked, or it can predict specific future check-in (not just the next one) at a given timestamp.

Aviraj Sinha, Elena R. Henderson, Jessie M. Henderson et al.

Random number generators (RNG) are essential elements in many cryptographic systems. True random number generators (TRNG) rely upon sources of randomness from natural processes such as those arising from quantum mechanics phenomena. We demonstrate that a quantum computer can serve as a high-quality, weakly random source for a generalized user-defined probability mass function (PMF). Specifically, QC measurement implements the process of variate sampling according to a user-specified PMF resulting in a word comprised of electronic bits that can then be processed by an extractor function to address inaccuracies due to non-ideal quantum gate operations and other system biases. We introduce an automated and flexible method for implementing a TRNG as a programmed quantum circuit that executes on commercially-available, gate-model quantum computers. The user specifies the desired word size as the number of qubits and a definition of the desired PMF. Based upon the user specification of the PMF, our compilation tool automatically synthesizes the desired TRNG as a structural OpenQASM file containing native gate operations that are optimized to reduce the circuit's quantum depth. The resulting TRNG provides multiple bits of randomness for each execution/measurement cycle; thus, the number of random bits produced in each execution is limited only by the size of the QC. We provide experimental results to illustrate the viability of this approach.

L. Allen, S. Wigley, H. Holmer

OBJECTIVE Non-communicable diseases (NCDs) are the leading cause of global death and disability. Tobacco, alcohol, and unhealthy foods are major contributing risk factors. WHO Member States have unanimously endorsed a set of 12 policies designed to constrain the sale of these commodities, however, there are myriad case studies of commercial entities seeking to undermine effective legislation in order to protect their profits. We set out to quantify the association between corporate financial influence and implementation of commercial policies. METHODS We generated policy implementation scores for all 194 WHO Member States using data from the 2015, 2017, and 2020 WHO NCD Progress Monitor Reports. We used publicly available data to create a novel Corporate Financial Influence Index (CFII) that quantifies the opportunity for corporations to use their financial resources to directly influence policymaking in each country. We reported policy implementation trends over time and used random effects multivariate regression to test the association between policy implementation and CFII for each country, while controlling for broad set of economic, cultural, historical, geographic, and demographic factors. FINDINGS Implementation of the 12 WHO-backed commercial policies has risen over time, but remains low at approximately 40%. Progress is reversing for alcohol policies. CFII explains around a fifth of the variance in global implementation. For every 10% rise in CFII, implementation falls by approximately 2% (95%CI 0.90 to 3.5, p < 0.001). CONCLUSION Our quantitative global analysis suggests that financial corporate influence is negatively associated with implementation of policies that seek to restrict the marketing, sale, and consumption of unhealthy (but profitable) commodities. In the context of anemic international progress tackling NCDs, greater attention should be paid to managing regulatory opportunities for overt and covert corporate financial influence as a core plank of the global NCD response.

A. Murad, F. Faruque, Ammar A. Naji et al.

Type-2 diabetes is a growing lifestyle disease mainly due to increasing physical inactivity but also associated with various other variables. In Saudi Arabia, around 58.5% of the population is deemed to be physically inactive. Against this background, this study attempts explore the spatial heterogeneity of Type-2 diabetes prevalence in Jeddah and to estimate various socio-economic and built environment variables contributing to the prevalence of this disease based on modelling by ordinary least squares (OLS), weighted regression (GWR) and multi-scale geographically weighted (MGWR). Our OLS results suggest that income, population density, commercial land use and Saudi population characteristics are statistically significant for Type-2 diabetes prevalence. However, by the GWR model, income, commercial land use and Saudi population characteristics were significantly positive while population density was significantly negative in this model for 70.6%, 9.1%, 26.6% and 58.7%, respectively, out of 109 districts investigated; by the MGWR model, the corresponding results were 100%, 22%, 100% and 100% of the districts. With the given data, the corrected Akaike information criterion (AICc), the adjusted R2, the log-likelihood and the residual sum of squares (RSS) indices demonstrated that the MGWR model outperformed the GWR and OLS models explaining 29% more variance than the OLS model, and 10.2% more than the GWR model. These results support the development of evidence-based policies for the spatial allocation of health associated resources for the control of Type-2 diabetes in Jeddah and other cities in the Arabian Gulf.

Ronghao Jiang, G. Lin

Abstract Existing theorization of the processes of urban transformation in different world regions has been based upon diverse interpretations of the interplay between the state and market forces. Studies of the growth and transformation of Chinese cities are characterized by continuing debates between those who insisted on the pivotal role played by the Chinese Party-state and others who envisioned urban China as moving decisively toward capitalism. This research examines the pattern and process of land marketization in urban China as a case to understand the interaction between the state and market forces in China’s ongoing urban transformation. Statistical analysis of the data for the Chinese cities at and above the prefectural level for the years of 2003–2017 has identified an uneven geography of land marketization effectively shaped by a localization of state-market interplay. A significant and positive correlation is found between the degree of land finance and extent of land marketization. The uneven geography of land marketization is significantly influenced by the legacy of the socialist planned economy as measured by the dominance of state-owned enterprises. However, the strength and direction of the correlation between the state and market are found to be contingent upon the level of urban economic growth. Spatial variation in land marketization is also shaped by the degree of openness, local state policies toward industrial vis-a-vis commercial and residential land supply, population density, and the size of the city. Findings of this research call for a theoretical reconsideration of the state-market relationship in urban transformation more attentive to local conditions.

Seyyed Ali Zeytoon Nejad Moosavian

Macroeconomics essentially discusses macroeconomic phenomena from the perspectives of various schools of economic thought, each of which takes different views on how macroeconomic agents make decisions and how the corresponding markets operate. Therefore, developing a clear, comprehensive understanding of how and in what ways these schools of economic thought differ is a key and a prerequisite for economics students to prosper academically and professionally in the discipline. This becomes even more crucial as economics students pursue their studies toward higher levels of education and graduate school, during which students are expected to attain higher levels of Bloom's taxonomy, including analysis, synthesis, evaluation, and creation. Teaching the distinctions and similarities of the two major schools of economic thought has never been an easy task to undertake in the classroom. Although the reason for such a hardship can be multi-fold, one reason has undoubtedly been students' lack of a holistic view on how the two mainstream economic schools of thought differ. There is strong evidence that students make smoother transition to higher levels of education after building up such groundwork, on which they can build further later on (e.g. Didia and Hasnat, 1998; Marcal and Roberts, 2001; Islam, et al., 2008; Green, et al., 2009; White, 2016). The paper starts with a visual spectrum of various schools of economic thought, and then narrows down the scope to the classical and Keynesian schools, i.e. the backbone of modern macroeconomics. Afterwards, a holistic table contrasts the two schools in terms of 50 aspects. Not only does this table help economics students enhance their comprehension, retention, and critical-thinking capability, it also benefits macroeconomic instructors to ...

Nancy Bouranta, Evangelos Psomas, Nicola Casolani Carmen Jaca et al.

The purpose of the study is to investigate and compare consumers' food safety perceptions in three Mediterranean countries (Greece, Italy, and Spain). A survey was carried out based on a structured questionnaire focusing on food safety-related issues concerning food characteristics, the labeling of systems implemented by food companies such as the Quality Management System and the Food Safety Management System, consumer trust in the food supply chain, and consumer illusion of food control. Information was collected from individuals located in those three countries (2,664 respondents), which share common characteristics. The results indicate that there is a significant heterogeneity in consumers' food safety perceptions in the three countries. The Spanish sample has the greatest level of trust in the supply chain in terms of food safety and the highest level of illusion of food control. The Italians evaluate the food characteristics and the QMS-FSMS's labeling higher than the Spanish and the Greeks. This multinational study brings to light the different types of food safety concerns of consumers from three Mediterranean countries.

J. Holguín-Veras, L. Kalahasthi, Diana Ramírez-Ríos

Abstract Commercial traffic in urban areas has not received the level of attention it deserves. Notwithstanding recent research on freight trip generation, other components of commercial traffic, such as commercial service traffic, have been largely overlooked. This is ironic, as the service sector represents a major and growing portion of urban and metropolitan economies. The research reported in this paper intends to help fill an important research gap through analyses of unique survey data collected by the authors. To this effect, the research comprehensively characterizes service visits to commercial establishments—in terms of frequency, purpose, duration, time of day, and other characteristics—by industry sector for two metropolitan areas. In addition, the authors estimated econometric models that express the number of service trips to commercial establishments as a function of the economic characteristics of the establishment and assessed the geographic transferability of the models obtained. To gain insight into the overall magnitude of service-related traffic, the models were applied to publicly available data to estimate the service activity in American cities of various sizes. The resulting service traffic are then used to estimate of parking requirements of service and freight vehicles for the most congested ZIP codes at these cities. The paper ends with a discussion of chief findings and policy implications.

A. Pandey, P. Malav, M. Rai et al.

L. Coyne, H. Kendall, R. Hansda et al.

Abstract Livestock production is under increasing scrutiny regarding its impacts on the environment and its wider role in climate change. Consequently, there are a growing number of private agri-environmental schemes (AES) now operating alongside public AES that offer farmers economic rewards to maintain and enhance the environment. This study focused exclusively on a small number of commercial dairy producers located in the North West of England who were all suppliers of a global food producer and members of the producer’s own private AES. The study explored the economic and societal drivers of adoption of agri-environmental behaviours and perceptions of the private processor AES. The study adopted a mixed-method approach. In Stage 1 a structured questionnaire was used to explore the role of an AES offered by the global food processor in the financial stability and environmental sustainability of dairy farms (n = 20). The survey sought to understand the range of interventions adopted, explore future adoption intentions and identify possible ways in which AESs could be extended. The results from the questionnaire were explored further in Stage 2, through qualitative in-depth interviews (n = 12). A thematic analysis approach was taken to describe the key themes that motivated farmer engagement in agri-environmental schemes. Overall, farmers felt that income from the private AES provided stability and resilience to their businesses, permitting them to have greater confidence in business planning and budgeting for the upcoming year. The majority of the farmers were not part of a public AES, but were already undertaking some agri-environmental behaviours and were motivated to join the private scheme primarily by financial incentives and by a desire to maintain the natural environment. A minority of respondents identified that the financial incentives offered had directly motivated a behaviour change. Decisions over which agri-environmental behaviours to adopt were driven by the existing animal management practices, geography and landscape of the farm. Farmers compared the private scheme favourably to available public AESs, which they perceived as more restrictive and providing insufficient reward for the “red tape” involved. In contrast, private scheme membership was perceived to have been beneficial for both their farm business and the local environment, and many reported personal satisfaction from engagement in agri-environmental behaviours. It is important that the design of future public AES does not “crowd out” private schemes, giving farmers increasing AES choice and increasing the overall amount of funding available for the delivery of public goods from agriculture.