Economic Networks Research Articles

Digitalisation in European regions: unravelling the impact of relatedness and complexity on digital technology adoption and productivity growth

ABSTRACT Digital technologies are the cornerstone of platform, knowledge, and network economies, driving the interconnectedness, innovation, and efficiency that define these economic models. Consequently, digitalisation has recently become a key policy objective for regional economies. This paper provides empirical evidence on how the adoption of new digital web technologies is shaped by previous regional digital capabilities. The analysis is based upon an economic complexity and relatedness framework using novel data on digital web technologies’ adoption for 278 European NUTS-2 regions between the years 2000 and 2022. Results show that regions tend to adopt new digital web technologies when they already master related digital capabilities. This paper also shows how digital complexity is associated with labour productivity gains at the regional level. Conclusions shed light on how regions adopt digital web technologies and serve as a tool for policymakers.

Batik Marunda as Cultural Heritageand Role in Sustainable Development

Batik Marunda is a batik producer in Jakarta that has a role in supporting one of the Sustainable Development Goals (SDGs), such as empowering relocation communities in three flats in the North Jakarta area. The study analyzes how Batik Marunda's role in the cultural preservation program while still contributing to economic empowerment, waste management to batik and batik materials. Methods of research are field studies, interviews with batik producers, the concept of collaboration in three flats and their governance. The findings show that Batik Marunda empowers especially women in the three flats area, through various skills training that has an impact on increasing skills, insights and economic levels for local residents, to waste management to reduce textile waste and support one of the SDG goals, namely responsible production and consumption. The three flats collaboration in the North Jakarta area is expected to strengthen the creative, environmentally friendly economic network and improve the quality of empowerment in the surrounding community. In summary, this research sees the important role of governance integration between cultural preservation programs, community empowerment, innovation and governance to support the SDGs agenda in the aspects of decent work, economic growth, climate change in sustainable development efforts.

Slamsex as slow edgework: Sexualized drug use and harm reduction in post-AIDS Taiwan

BackgroundSince the mid-2010s, slamsex—sexualized drug use involving intravenously injecting meth—has rapidly emerged in metropolitan gay communities as a sociosexual practice necessitating harm reduction interventions. However, its impact on gay men's sociosexual lives and HIV technologies is not well understood. This paper addresses this gap by investigating two intertwined temporal dimensions of slamsex, viewing it as a form of slow edgework in post-AIDS Taiwan. MethodsEthnographic research was conducted in Taiwan between 2022 and 2024, involving 30 interviews with gay men who practice slamsex and 7 HIV/AIDS NGO representatives. Semi-structured interviews focused on slamming events, covering their edge, transition, and healthcare network. ResultsFindings reveal paradoxes within edgework, demonstrating that slamsex can be both extreme and risky, as well as slow and mundane. Participants navigated state surveillance and the materiality of syringes through meticulous risk calculations and digital-savvy strategies for acquiring clean syringes. The practice involved risk and a networked economy of care services provided by other injectors. In response to HIV technologies (PrEP and viral load disclosure), gay men in this study normalized HIV/AIDS while developing a sense of vulnerability to their own infection. ConclusionIn the era of HIV/AIDS normalization, slamsex as slow edgework underscores the need for temporally and spatially nuanced discussions on drug policy. This study highlights the importance of culturally tailored interventions that consider slow, networked interactions to provide harm reduction for men on the chemsex scene, as well as the needs for more evidence-based, non-discriminatory research on queer sexual health and drug studies.

Big Data in Tourism: A Bibliometric Analysis (2014-2024)

The emergence of big data and its related technologies has brought about novel economic models, industry phenomena, and relational networks, instigating revolutionary changes with significant value for tourism sustainability. This study conducts a bibliometric analysis of 212 articles (2014-2024) on big data in tourism from the Web of Science (WoS) Core Collection database, aiming to create a knowledge map based on big data in tourism. This study utilizes VOSviewer software to carry out citation analysis, co-citation analysis, co-authorship analysis, and keyword co-occurrence analysis, revealing trends in publications, national contributions, influential journals and authors, author collaborations, as well as the conceptual structure and research trends in the field of big data in tourism. The findings indicate a concentration of research in seven areas: machine learning, social network analysis, sustainability, tourism demand forecasting, artificial intelligence, smart tourism, and text mining techniques. The research has focused on emerging hot topics since 2022, including destination image, COVID-19, topic modeling, and urban tourism. This study maps the knowledge of big data in tourism, elucidates the academic evolution in this field, and offers future research directions for scholars in the domain.

Dynamic spatial price equilibrium, dynamic user equilibrium, and freight transportation in continuous time: A differential variational inequality perspective

In this paper we provide a statement of dynamic spatial price equilibrium (DSPE) in continuous time as a basis for modeling freight flows in a network economy. The model presented describes a spatial price equilibrium due to its reliance on the notion that freight movements occur in response to differences between the local and distant prices of goods for which there is excess demand; moreover, local and distant delivered prices are equated at equilibrium. We propose and analyze a differential variational inequality (DVI) associated with dynamic spatial price equilibrium to study the Nash-like aggregate game at the heart of DSPE using the calculus of variations and optimal control theory. Our formulation explicitly considers inventory and the time lag between shipping and demand fulfillment. We stress that such a time lag cannot be readily accommodated in a discrete-time formulation. We provide an in-depth analysis of the DVI's necessary conditions that reveals the dynamic user equilibrium nature of freight flows obtained from the DVI, alongside the role played by freight transport in maintaining equilibrium commodity prices and the delivered-price-equals-local-price property of spatial price equilibrium. By intent, our contribution is wholly theoretical in nature, focusing on a mathematical statement of the defining equations and inequalities for dynamic spatial price equilibrium (DSPE), while also showing there is an associated differential variational inequality (DVI), any solution of which is a DSPE. The model of spatial price equilibrium we present integrates the theory of spatial price equilibrium in a dynamic setting with the path delay operator notion used in the theory of dynamic user equilibrium. It should be noted that the path delay operator used herein is based on LWR theory and fully vetted in the published dynamic user equilibrium literature. This integration is new and constitutes a significant addition to the spatial price equilibrium and freight network equilibrium modeling literatures. Among other things, it points the way for researchers interested in dynamic traffic assignment to become involved in dynamic freight modeling using the technical knowledge they already possess. In particular, it suggests that algorithms developed for dynamic user equilibrium may be adapted to the study of urban freight modelled as a dynamic spatial price equilibrium. As such, our work provides direction for future DSPE algorithmic research and application. However, no computational experiments are reported herein; instead, the computing of dynamic spatial price equilibria is the subject of a separate manuscript.

Foreign Trade Representative Strategy In Building The Image Of Indonesian Products In Australia

Trade relations between Indonesia and Australia have been around for a long time. This is proven by the commitment of the two countries to improve trade relations. However, in 2020 trade between the two countries experienced a significant decline due to the Covid-19 pandemic. As representatives of Indonesian trade in Australia, the trade attaché and the Indonesia Trade Promotion Center (ITPC) are crucial institutions in bridging trade between the two countries. So, this research aims to discuss the strategy of Indonesian trade representatives in Australia in building the image of Indonesian products. This research uses qualitative research methods and data collection with literature studies such as previous research, books, and official documents, the concept used is economic diplomacy in analyzing problems. The results of this research show that the strategy of Indonesian trade representatives in Australia was carried out in several stages, namely economic salesmanship, economic networking, image building, and regulatory management.

Structural resilience disparities in regional city networks under the influence of Typhoon Fitow and COVID-19

The integration of regional city networks plays a pivotal role in the regional development. Assessing and analyzing the structural resilience of these networks holds significance in enhancing regional comprehensive disaster prevention. This study focuses on the Yangtze River Delta, examining its regional city networks encompassing information, economic, transportation, and innovation domains. Utilizing the social network analysis, projection pursuit model based on the genetic algorithm, coupling coordination degree model, coefficient of variation and spatial Gini coefficient, we scrutinized the structural resilience and evolution difference of these networks under different disasters. We found that the structural resilience of economic and transportation networks that depend on infrastructure construction are more limited by geographical distance, and information and innovation networks with weaker physical dependence have stronger structural resilience. The impact of Typhoon Fitow on the structural resilience of individual networks was mainly concentrated in coastal affected cities, while the impact after COVID-19 was mainly in marginal cities. The spatial pattern of the structural resilience of multiple networks shifted from low level equilibrium to agglomeration development. Finally, nodes were identified into three types according to the resilience performance. This research offers a scientific foundation for quantifying regional resilience and formulating network optimization strategies.

Wood trade regions of Russia: clustering approach development

Network analysis of the Russian timber industry was based on the information about timber transactions between Russian companies in 2020. The data were collected from the Unified State Automated Information System Accounting Timber and Transactions with It. The method of graph clustering was developed. The first step is clustering of the hole graph by Leiden algorithm. The second step is drawing of each Leiden created cluster in abstract space using Fruchterman–Reingold layout and extraction of clusters from layout using meanshift algorithm. Such approach helped us to divide many Leiden-created clusters into components. This algorithm has a problem of resolution limit. It tends to combine some medium and small clusters into large ones. Also, a special method was developed to limit the uncertainty of clustering – both Leiden algorithm and Fruchterman–Reingold layout are non-deterministic algorithms. Wood trade clusters vary in size and content. They are rather autonomous. The average share of intra-cluster trade is 89%. The modularity score of our partition is 0.863. Each cluster has been described using open sources from the Internet. The content of the clusters does not contradict the logic of the wood industry processes. The clusters create rather compact areas on the map – wood trade regions. Their borders often overlap with existing administrative boundaries. Five types of network structure of clusters were defined: vertical monocentric, vertical polycentric, horizontal, dendritic, simple. There are three factors of clustering in wood industry of Russia: production chains (4 types – pulp, plywood, lumber, chipboard chains), demand chains (redistribution of raw material between manufacturers within the cluster, accumulation of wood volumes for some enterprises outside the cluster, wood trade between traders within the cluster), common holder. We have shown that existing approaches to cluster detection based on location quotients often misrepresent economic networks.

Research on multi-time scale Volt/VAR optimization in active distribution networks based on NSDBO and MPC approach

To explore the potential of all-round and multiangle voltage and reactive power control to reduce losses in a new power system with a high proportion of distributed resources connected to a distribution network, this study proposes a Volt/VAR optimization method for distribution networks using a nondominated sorting dung beetle optimizer (NSDBO) and model predictive control (MPC). According to the characteristics of the various adjustable resources in the network, they are divided into day-ahead and intraday optimizations. The optimization process in the day-ahead stage uses the NSDBO to create a discrete equipment scheduling plan. The optimization process in the intraday stage combines the discrete equipment scheduling plan with the MPC to create the scheduling plan for photovoltaics, energy storage, and vehicle charging stations. Two-stage optimization scheduling is achieved with the lowest discrete equipment regulation cost and minimization of network loss and node voltage deviation penalty cost as the optimization goal. The feasibility of this method for coordinating various resources in the network, processing discrete and continuous variables, and coping with the volatility and uncertainty of high-proportion distributed resources is verified through case analysis. The effectiveness of this method in improving the security and economy of distribution networks is demonstrated. The superiority of the solution speed and quality is also confirmed.

Low carbon energy industry and network economy prediction based on sensors and real-time data processing

The widespread use of sensors provides a large amount of real-time data for enterprises and decision-makers, providing more accurate information support for the prediction and decision-making of the network economy. With the help of Internet of Things technology, the data collected by sensors is transmitted in real time to data centers or cloud platforms. Real time data processing technology is used to clean, denoise, and analyze the data in real time, ensuring the accuracy and timeliness of the data. Perform pattern recognition and trend analysis on historical data, discover hidden patterns and correlations in the data, construct predictive and decision-making models to predict future economic trends and make reasonable decisions, continuously optimize and adjust the model to adapt to real-time data changes and dynamic changes in the economic environment, and improve the accuracy and efficiency of the model. The experimental results show that the network economy prediction and decision-making model based on sensor networks and Internet of Things technology can more accurately predict economic development trends, improve decision-making efficiency and accuracy. The large amount of data provided by sensor networks provides sufficient support for the construction and optimization of models.

Evaluation and convergence analysis of science and technology innovation efficiency in China’s national central cities based on the super-efficiency SBM-ML model

ABSTRACT Scientific and Technological Innovation (STI) is an important driving force for economic development and social progress. National central cities are the main connection points of China’s economic activity network and play a leading role in the national economic development of the country. In order to explore the efficiency of STI and the convergence characteristics of STI in China’s national central cities, this paper selects S&T innovation input-output indicators and constructs a system of indicators for high-quality economic development. It measures the STI innovation efficiency of China’s national central cities by combining with the super-efficiency SBM-ML model and then constructs a model to analyze its convergence situation. The results of the study show that: from 2011 to 2021, the STI efficiency of China’s national central cities has reached an effective level, and the overall trend has increased; the total factor productivity of STI in China’s national central cities has obvious differences, with Beijing having the highest total factor productivity, while Shanghai has the lowest; the STI innovation efficiency of China’s national central cities exhibits both absolute β-convergence and conditional β-convergence, but there is no significant α convergence, and regional differences are obvious.

Collaboration and resource sharing in the multidepot time-dependent vehicle routing problem with time windows

Concerns about energy conservation and emission reduction have highlighted the importance of environmentally sound logistics networks in urban areas. These networks are deeply intertwined with urban traffic systems, where variations in transit speeds can significantly increase the energy consumption and carbon emissions of delivery vehicles, compromising the environmental sustainability of urban deliveries. To address this, we propose a multidepot time-dependent vehicle routing problem with time windows, enhancing route planning flexibility and resource configuration. Our approach begins with a route spatiotemporal decomposition method to estimate vehicle travel times and emissions based on varying vehicle speeds. We then develop a multiobjective mixed integer linear programming model that aims to minimize total operating costs, the number of vehicles, and carbon dioxide emissions. A hybrid heuristic algorithm combining spectral clustering, multiobjective ant colony optimization, and variable neighborhood search is proposed to solve the model. This algorithm incorporates collaboration and resource sharing strategies, a pheromone initialization mechanism, a novel heuristic operator that accounts for time dependency, and a self-adaptive update mechanism, enhancing both solution quality and algorithm convergence. We compare the performance of our algorithm with that of the CPLEX solver, multiobjective ant colony optimization, non-dominated sorting genetic algorithm-Ⅲ, and multiobjective particle swarm optimization. The results demonstrate the superior convergence, uniformity, and spread of our proposed algorithm. Furthermore, we apply our model and algorithm to a real-world case in Chongqing, China, analyzing optimized results for different time intervals and vehicle speeds. This study offers robust methodologies for theoretically and practically addressing the multidepot time-dependent vehicle routing problem with time windows, contributing to the development of economical, efficient, collaborative, and sustainable urban logistics networks.

Artificial neural networks in economics: mathematical tool, model or methodology?

The purpose of this article is to assess the current interaction between artificial intelligence (AI) and economic science and to identify promising interdisciplinary areas of research that can significantly influence the methodology of understanding economic phenomena. To achieve this goal, the vague and partly even mystical term AI was replaced with а more scientific term «artificial neural networks» (ANN). The article uses methods of scientometric, epistemological and comparative analysis of the processes of ANN penetration into economics and other academic disciplines. The authors reveal the epistemological commonality and difference between AI and ANN and justify the shift in epistemological focus in research from general AI to ANN. The paper systematizes the use of ANN in economics: 1) ANN as a mathematical tool for solving economic problems; 2) ANN as a model of economic phenomena; 3) ANN as a methodology for understanding economic patterns. It shows the interaction of economics with neurosciences which occurs in two significantly different directions: from neurobiology, i.e. real nerve networks in living organisms, and, on the other hand, from ANN theory. The first direction is associated with neuroeconomics, the second has not yet been articulated, but shows an exponential growth in publications and is associated primarily with forming a new economic paradigm. The ANN paradigm in economics (and not only in economics) changes both the subject of cognition, introducing radically new forms/types of evidence and new research methods, and the object of cognition, changing the focus of study from individual economic behavior to the collective economic behavior of mega-subjects.

Presión autoritaria y resistencia democrática en Guatemala

The results of the first and second round of elections in Guatemala in 2023 stopped the accelerated steps towards an authoritarian and corrupt prolongation that the country was going through. This unforeseen turn has given rise to the emergence of signs of a possible opening of a new cycle that would make possible the recovery of the democratization process. But also the emergence of contradictions and antagonisms that threaten the necessary changes. When the year began, the general elections to be held in June, maintained a low level of enthusiasm and public attention. There was a coincidence of analysis that it would be an election dominated by pessimism and probably with the least hope for change in the last four decades. Citizen disillusionment extended everywhere, appreciating as a distant memory the expectations generated in 2015, when anti-corruption movements gave rise to possibilities of containing the repetition of numerous abuses and enrichment of officials, politicians and businessmen, colluded in illicit networks. On this occasion, in the context of these general elections in 2023, the recurrence of official dispositions that have shown a clear tendency to close democratic freedoms and rights and the beginning of a new and prolonged authoritarian period in the country, led by illicit economic political networks, has been a cause for concern. Now, is the recovery and sustainable advance of a new democratization, repeatedly challenged by actors who seek to prevent it, viable? ECA Estudios Centroamericanos, Vol. 79, No. 774, 2024: 47-64.

THE CURRENT LEARNING METHODOLOGY AND IT’S FUTURE TRENDS

The future of any country's development the main determining factor is education. The main goal of education is for student’s successful training, intellectual, moral, with appropriate physical capabilities and humaneness respect, study, work, and live independently is to develop into a capable individual. Nowadays, the development of technology changes people's lives technology while making it easier and easier of a person who wants to receive services based on progress arouses interest. It is also in education and depends. [1]Technological innovation fundamentally transforms education, reshaping the needs and skills required for present and future workplaces. Building a future-ready education system requires consistent delivery of 21st-century curriculum and inclusive models that provide a strong foundation for lifelong adaptation and new skills development, with specialized education focusing on skills that are in demand in the real world. This disconnect between employer needs and talent pools must be addressed.This study on where and how modern learning methods and trends are headed in today's Industry 4.0 transition is based on the predictions of a wide range of experts from the World Economic Forum's expert network and the Organization for Economic Co-operation and Development (OECD)'s Education and It was developed based on the initiative of the Future of Skills 2030 project.

Moving on: The overrepresentation of Black and minoritised households in out of area housing placements in England

Based on freedom of information request data supplied by English local authorities, this paper identifies significant under-reporting of out of area housing placements in government statistics alongside an overrepresentation of Black and minoritised households amongst such placements. In a context of neoliberalism, housing policy has become abstracted from contextual factors that shape experiences of housing. With housing increasingly commodified, this paper explores housing challenges beyond economic factors to include historical legacies and exclusion. Housing is therefore positioned as a conduit between people and place, with place understood as a spatial representation of social and economic networks. The fracturing of these networks illustrates a geographical manifestation of housing policy, crisis, and exclusion. This paper reflects on the social and economic context that drives such experiences and situates the overrepresentation of out of area housing for Black and minoritised households.

The early medieval origins of copper ore extraction in the Carpathian Mountains

The case study addresses the question of the early medieval revival of copper production in Europe. The focus of the research area is the Carpathian Basin, located on the eastern periphery of the zone influenced by European early medieval processes. The research of where and when early medieval metal production started in the region in our case also provides answers to the question of how the region was linked to the European continental economic network, which is until recently an unresolved problem. Ore extraction and the production of various non-ferrous metals (copper, silver, gold) had important centres in the Carpathian Mountains throughout the Middle Ages until the Modern Era. Concerning the Avar period, our study comprises lead isotope and chemical analyses of ‘Late Avar’ (c. 8th century AD) copper rivets, used for certain purposes alongside the copper alloys predominant in the period. As rivets were selected from the entire Avar cultural area, expanding virtually to all lowland and hilly areas in the Carpathian Basin, results are representative of the entire region. It shows that Avar craftsmen producing large quantities of copper alloy objects in the 8th and the early 9th century were supplied with copper from a single mining district, from the ore deposits of the Slovak Ore Mountains in the today Central Slovakia, a region which became an important metal-processing centre in the 11th century AD.

Mercantile Elites and the Making of Global Capitalism

AbstractGlobal trade is a topic that is well suited for conceptualizing global social history as it allows the opportunity to challenge the notion that global markets were primarily congruent with imperial territories. Businessmen were regularly able to establish economic networks that transgressed state borders and challenged imperial aims for territorial control. This may be evidence for the fact that capitalism and imperialism were two different, although sometimes converging, spatial structures, each with a distinct logic of its own. Even in the colonial period, and despite the prevalence of imperial racism, co-operation between metropolitan capitalists and businessmen from peripheries was possible – and, in fact, the rule rather than the exception. This co-operation was facilitated by similar business practices and a similar mercantile culture, which is why the two constituencies have been described as joint members of a globally connected bourgeoisie in several studies. The ability of economic elites to establish transregional business structures is highly relevant for conceiving global social history as a distinct approach. It reveals that the activities of these actors were crucial for establishing global capitalism, and allows scholars to examine the embeddedness of mercantile elites in their socio-economic environment and in particular to study the relation between capital and labour.

Research on the correlation network of carbon emissions and economic between Chinese urban agglomerations

Urban agglomerations are currently at the center of China's efforts to reduce carbon emission and sustainable economic development. It is critical to investigate the carbon emissions and economic correlation between urban agglomerations in order to assist their carbon emission reduction and economic coordinated development. This paper is based on an environmentally extended multi-regional input-output model, and studies the embodied carbon emissions and economic two-dimensional correlation network between major urban agglomerations in China from 2012 to 2017. The research results indicate that the production-based carbon emissions of the production and supply of electricity and heat industry in the Yangtze River Delta urban agglomeration were higher than the consumption-based carbon emissions, with a significant gap. However, the construction and service industries were the opposite. The Hohhot-Baotou-Ordos-Yulin urban agglomeration experienced carbon emission losses and economic benefits. The Yangtze River Delta urban agglomeration achieved a win-win situation for carbon emissions and economy. The Pearl River Delta urban agglomeration benefited from carbon emissions but suffered economic losses. The Central Plains urban agglomeration faced a loss-loss situation for carbon emissions and economy. Major urban agglomerations in China should implement differentiated emission reduction measures based on their own characteristics. For example, the Hohhot-Baotou-Ordos-Yulin urban agglomeration should utilize natural resources to develop green industries and high value added industries.

The Beneficial Role of Silicon Valley's Technological Innovations and Venture Capital in Strengthening Global Financial Markets

This research explores the substantial influence of Silicon Valley's technology environment on worldwide financial markets. It looks at how Silicon Valley's technology developments and venture capital improve market efficiency, boost economic growth, and help to stabilize world financial institutions. The results highlight the importance of Silicon Valley's venture capital firms in not just providing vital financing but also in strategically selecting and growing entrepreneurs to include into a larger economic network. The increase in productivity in different industries is thanks to the groundbreaking advancements in artificial intelligence and microprocessor technology in Silicon Valley. These progressions have not just improved operational effectiveness greatly but also encouraged the emergence of fresh markets. Indoor air quality challenges emphasize the need for innovation in building design. (Ashayeri, M., Piri, S., & Abbasabadi, N, 2024). Dimensionality reduction in multimodal deep learning enhances accuracy and reduces computational costs, critical for efficient AI applications. (M Bodaghi, M Hosseini, R Gottumukkala, 2024) Silicon Valley, a prime example of innovation, still showcases its significant impact on shaping present financial environments as it continues to prosper. Despite the fact that the total quantity of money invested in various countries throughout the globe is always growing, this is the case. The accomplishments of this area may serve as a model for other regions that are interested in promoting economic growth via the deployment of cutting-edge technology and creative investment methods. These places can learn from this region's successes.