Demand Factors Research Articles

Using AHP-Entropy method to explore the influencing factors of spatial demand of EVs public charging stations: A case study of Jinan, China

Using AHP-Entropy method to explore the influencing factors of spatial demand of EVs public charging stations: A case study of Jinan, China

Transformation of the farm segment of the aquaculture value chain in southern Bangladesh

Aquaculture in Bangladesh has experienced rapid growth over the last three decades, yet technological changes in the farming segment remains underexplored. To address this gap, we surveyed 721 farmers across seven districts in southern Bangladesh from November 2020 to February 2021. The result showed moderate growth in aquaculture, with an 11 % increase in farmers and a 15 % rise in waterbodies over the past decade. Farmers cultivated diverse aquatic species, with carp dominating production at 56 % followed by crustaceans (16 %) and tilapia (14 %). Shrimp are more highly susceptible to disease than fish and prawn but, only 19 % of farmers adopted measures to enhance survival. The study noted a shift from shrimp to prawn and from crustaceans to fish due to disease, market demand, and environmental factors, resulting in intensification and greater use of floating feed over the last decade. A mere 0.55 % of farmers reported losses, spoilage, or wastage during harvest or between harvest and sale of aquatic products, with the volume being minimal at just 0.52 %. Farming generated 401,536 full-time equivalent (FTE) on-jobs in the seven surveyed districts, predominantly for family labor. Aquaculture demonstrates strong profitability, with an average benefit-cost ratio (BCR) of 1.98, indicating nearly a twofold return on investment. This study highlights the evolving dynamics of aquaculture in southern Bangladesh, revealing modest growth, increased species diversification, and shifts in production practices, which underscore the sector's profitability and need for improved technological adoption on a global scale.

Decomposition analysis of influencing factors of GPU-centric supercomputing demand: LMDI-based approach

With the introduction of AI technology, the supercomputing industry is transitioning from CPU-centric to GPU-centric, and many countries are making efforts to build new GPU-centric resources. The purpose of this paper is to discover new factors in demand management for efficient construction and operation of future national supercomputing GPU resources. Reflecting industry characteristics, we decompose the factors affecting existing CPU use into intensity effect, structure effect, and production effect indicators targeting CPU-only resources and GPU-only resources, and compare and analyze the influence of each factor. To estimate the influence of each factor, the Logarithmic Mean Divisia Index methodology was used, and annual CPU usage data from the Republic of Korea's national supercomputing center was used. As a result of the analysis, it was confirmed that CPU resources show a similar trend every year, and that the effects of the intensity and production indicators are continuously increasing. In the case of GPU resources, all indicators had an influence in the direction of increasing demand, and it was confirmed that the information/communication field was overwhelmingly showing the greatest effect.

Exploring the evolution and future trajectory of transportation technologies

The need for people to relocate due to the environmental difficulties they experienced centuries ago gave birth to the concept of transportation; It has become one of the indispensable needs of people such as food, drink, health and shelter. In order to meet this displacement requirement, various tools have been developed and used according to the continental or marine characteristics of the Earth, and the need for transportation has continued to develop dynamically and become an integral part of our daily lives. In this study, the development of the need to relocate, which is among the basic needs, from past to present under the umbrella of transportation, its technological evolution and popular demand factors were investigated. As a result of the study, it has been reached that electric vehicles have become popular in the 21st century with the increase in urban population, people's demand for individual transportation vehicles and the development of sustainable energy-based fuel technologies. Urban air mobility solutions, including electric vertical take-off and landing (eVTOL) aircraft and drones, have the potential to relieve congestion on roadways and offer efficient transportation options for urban commuters. Additionally, autonomous and connected vehicles have the potential to optimize traffic flow and reduce congestion, thereby improving the energy efficiency of transportation systems. However, more concrete and quantitative comparisons are required to determine their impact on fuel consumption.

Understanding Value Creation And Appropriation In A Disruptive Narrative: The Uk Ev Sector

This study explores the dynamics of value creation and appropriation in the emerging UK electric vehicle (EV) market through the technological, regulatory, economic, and consumer demand factors. The research further explores correlations between R&D expenditures, patent activity, and market variables specific to the UK with support through Porter’s Value Chain, the Invention-Based View with Teece’s Appropriability Framework, and Disruptive Innovation Theory. Findings show that innovation creates value on an international scale; however, it does not necessarily impact the UK’s immature market share. Tesla’s open-portfolio strategy suggests that pushing for market maturation, mixed with strategic infrastructure development and consumer engagement, is crucial to drive long-term value appropriation. The study concludes that aligning innovation with market readiness and regulatory frameworks is key to fostering a sustainable and competitive EV industry. While global innovation sets a foundation, the UK market requires targeted strategies to overcome its dependency on incentives and limited infrastructure. This research provides actionable insights for professionals and policymakers, emphasizing the importance of infrastructure investment, regulatory clarity, and consumer-focused initiatives to achieve a sustainable transition toward a matured UK EV market.

Determination of estimated electrical loads of charging infrastructure for electric vehicles integrated into electrical installations of residential and public buildings

RELEVANCE. The research aims to improve the regulatory framework governing the procedure for determining the estimated load of public buildings when integrating charging infrastructure for electric vehicles into the electrical installations of these buildings. THE PURPOSE. Determination of the schedule of electricity consumption, the coefficient of simultaneity and non-coincidence of maximums of the charging infrastructure for electric vehicles, with subsequent development of proposals for updating SP 256.1325800.2016 "Electrical installations of residential and public buildings. Design and installation rules" in terms of the methodology for determining the estimated load of public buildings when integrating charging infrastructure for electric vehicles into the electrical installations of the said buildings. METHODS. In achieving this goal, experimental, mathematical and statistical methods were used. RESULTS. A methodology for determining the coefficients of simultaneity and non-coincidence of maximums of electric vehicle charging stations has been developed, and their numerical values have been obtained depending on the type, power, and combination of simultaneously operating charging station connectors. Proposals have been developed for amending SP 256.1325800.2016 "Electrical installations of residential and public buildings. Design and installation rules" in terms of the methodology for determining the estimated electrical loads of the charging infrastructure for electric vehicles integrated into electrical installations of public buildings. CONCLUSION. The development of a methodology for calculating electrical loads simultaneously with the development of demand factors, simultaneity and the coefficient of mismatch of maximums of charging infrastructure for electric vehicles integrated into public buildings will contribute to the optimization of costs for technological connection to electrical networks.

The Assessment of the Potential of Russian Grain Trade in Asian and African Countries: A Gravity Model Approach

This study examines the potential of grain trade within the context of global commerce, highlighting its role as a primary component of the worldwide agricultural market and a crucial factor in ensuring global food security and price stability. To achieve the research objectives, we utilized panel data from Russia, a leading player in the grain market, and employed a gravity model along with Poisson pseudo-maximum likelihood (PPML) to analyze the impact of demand factors and the economic openness of importing countries, as well as the effects of distance, sea access, borders, language, trade unions, and tariffs on exports. Our research challenges the notion that an increase in the wealth of countries leads to a corresponding rise in trade between them. In the case of grain, evidence indicates a negative contribution of GDP per capita in importing countries. The gravity model and PPML enabled us to assess individual country effects and quantitatively evaluate the potential for increasing Russian grain exports to Asian and African nations. We propose recommendations for enhancing domestic grain production and improving the effectiveness of marketing strategies in countries with underutilized export potential.

Digital Twin Technology for Real-Time Monitoring of Energy Supply Chains

Digital Twin technology has emerged as a powerful tool for real-time monitoring and optimization in various industries, including the energy sector. By creating a virtual replica of physical assets, processes, or systems, Digital Twins enable real-time data analysis and simulation, offering unprecedented visibility into the operations of energy supply chains. This paper explores the application of Digital Twin technology in enhancing the efficiency, reliability, and sustainability of energy supply chains. Digital Twins integrate data from IoT sensors, machine learning algorithms, and advanced analytics to provide dynamic, real-time insights into the performance of key supply chain components, including production, transportation, storage, and distribution. These virtual models enable predictive maintenance, process optimization, and immediate response to disruptions, significantly reducing downtime and operational costs. The ability to simulate different scenarios and test potential solutions in a virtual environment is particularly beneficial in managing complex and dynamic energy supply chains, which are often subject to fluctuations in demand, supply constraints, and environmental factors. Digital Twins allow energy companies to monitor asset health, optimize inventory, and track logistics in real-time, thereby improving decision-making and operational agility. Additionally, by simulating various operational scenarios, energy companies can identify inefficiencies, enhance resource management, and improve overall supply chain resilience. Furthermore, Digital Twin technology supports sustainability initiatives by enabling energy optimization, waste reduction, and the monitoring of environmental impact in real-time. By providing accurate data on energy usage and emissions, Digital Twins help organizations meet regulatory compliance and sustainability targets more effectively. As the energy sector increasingly embraces digital transformation, Digital Twin technology will play a central role in enabling smart, resilient, and sustainable energy supply chains. This paper discusses the potential benefits, challenges, and future trends of Digital Twin technology in the energy sector, highlighting its transformative impact on supply chain management.

Impact of internal and network-side demand factors in network-serving load management systems

Impact of internal and network-side demand factors in network-serving load management systems

Analysis of China’s New Energy Vehicle Export Competitiveness in a Dual-Carbon Context

In the context of the dual-carbon goals (carbon peaking and carbon neutrality), China’s new energy vehicle industry, as a key component of the green economy, has increasingly attracted international attention for its export competitiveness. Drawing on the Porter Diamond Model, this paper selects four major variables—R&D investment, production factors, demand factors, and spare parts services—to establish a linear regression equation. Through this model, the trade competitiveness of China’s new energy vehicles is analyzed in detail, and corresponding suggestions are proposed to enhance the competitiveness index. It is anticipated that Chinese new energy vehicle enterprises can strengthen overseas competitiveness by advancing core technology innovation, improving comprehensive industrial chain management, and diversifying sales channels.

Global datasets of geospatial-AI-resolved energy consumers including climate-driven energy demands, geographical and socioeconomic realities for a transition reset

Traditional models deliberately simplify millions of consumers into a single, homogeneous, representative agent with perfect market knowledge and rational expectations, limiting their capacity to capture real-world complexities. To address this limitation in mainstream models, this article provides global datasets to parametrise energy consumers within climate-energy-economy models considering climate-driven energy demand, socioeconomic and demographic factors. The datasets emerge from applying geospatial artificial intelligence, machine learning and big data analytics on a range of geospatial parameters at 1 km2 resolution. Twenty distinctive energy consumers are defined using three heterogeneous geospatial features, eight diverse and two evolving parameters. This parametrisation of consumers strengthens the applicability of climate-energy-economy models to guide effective, equitable and just climate policy design. This comprehensive analysis of complex interactions between climate, socioeconomic and demographic factors supports more realistic decision-making for a sustainable transition reset. This research emphasises the geospatial distribution of energy consumers to enhance technoeconomic assessment, understanding consumer dynamics for consumer-led resource allocation and informed policy implementation. These datasets can be used in climate-energy-economy models to parametrise consumers beyond traditional approaches.

Dynamic optimal lane management strategy for multi-lane urban expressway with bi-class connected vehicles

Traffic flow mobility on expressway plays an important role in urban development. With the emergent technologies, connected vehicles, including both connected automated vehicles (CAVs) and connected regular vehicles (CRVs), are equipped with connectivity features to improve efficiency of urban expressway in a mixed traffic scenario. Existing research indicates that without targeted management, the integration of CAVs and CRVs into regular vehicles (RVs) traffic can lead to a series of congestion issues. A potential solution lies in the implementation of dedicated lanes, each designated for specific vehicle types, which could alleviate traffic flow complications. Therefore, this paper proposes a dynamic optimal lane management strategy for multi-lane mixed traffic urban expressway, aimed at maximizing the whole discharge flow. To begin with, we present an analytical method to mathematically derive discharge flow of each lane type including mixed traffic lane, CAV/CRV dedicated lane, and RV dedicated lane, from the perspective of both traffic demand and traffic capacity. Then, taking a three-lane mixed traffic urban expressway as an application, we conduct a numerical analysis of the dynamic optimal lane management strategy, based on comparisons among eight distinct strategies, under varying factors of traffic demands, penetration rates of CAVs and CRVs, and allocation proportions of vehicles upstream assigned to mixed traffic lanes. The results validate the effectiveness of the analytical method proposed for lane management strategies. It also indicates that the aforementioned system factors have significant impacts on the dynamic optimal lane management strategy.

Research on Factors Affecting the Career Choice Decision of High School Students in Thai Nguyen Province

This study evaluates factors influencing career choice decisions among high school students in Thai Nguyen province through statistical analysis. Results indicate that crucial factors affecting these decisions include labor market needs, career counseling, family influence, university factors, and personal preferences. Exploratory factor analysis identifies 5 significant factors, explaining 73.68% of data variance. Strong positive correlations are observed between career decisions and factors such as labor market needs, career counseling, family, university factors, and personal preferences. Additionally, a linear regression model predicts that labor market demand, career counseling, and university factors positively and significantly influence career choices, while personal and family influences show weaker correlations. Practical implications of this research include assisting educational institutions, families, and students in comprehending career decisions and formulating effective support strategies.

Mechanically induced dynamic QBIC-BIC switching and sensing performance enhancement using elastomer-based double resonant gratings

A quasi-bound state in the continuum (QBIC) is a hot topic in optics, as it brings extremely high Q factors by slightly breaking the structural symmetry or tuning geometric parameters. However, QBIC devices are usually not dynamically tunable; thus, achieving this ultra-high Q factor demands ultra-high fabrication accuracy. Here, by first proposing an elastomer-based double resonant gratings (DRG) in the THz band, a strategy to realize dynamically tunable QBIC devices is presented. By simple stretching, structural parameters can be tuned, leading to dynamic QBIC-BIC switching, dynamic Q factors tuning to the order of 106, and enhancement of sensing performance. Moreover, by varying the incident angle, this dynamic tuning is achievable at a constant frequency. The strategy is expected to neutralize the demanding requirements for fabrication accuracy in the implementation of QBIC devices. Hence, it is a promising solution to realize tunable QBIC devices for applications such as THz biosensing.

National system of price indicators: From theory to practice

Under the conditions of sanctions against the Russian Federation, foreign price indicators, which were previously widely used by both business and regulators, have become difficult to access and generally less relevant due to changes in the direction of commodity flows. There is a need for a national system of price indicators, the design of which is currently being decided, and the concept, despite more than two years of discussion and a high degree of priority, has not yet been adopted. The purpose of the article is to outline the subject field of the problem of constructing a national system of price indicators both from the point of view of the theory of the issue and in terms of its application to the problems of antimonopoly policy. The approaches of the new institutional economic theory and Austrian economic theory serve as the methodological basis of the study, and its methods are comparison of structural alternatives. The information base of the study was made up of data from the Federal Antimonopoly Service on law enforcement practice. The results of the study emphasize that in order to fulfill their functions, national price indicators must be formed in the conditions of the market (not quasi-regulation) and competition. The volume and frequency of data collected to calculate the indicators should be determined taking into account the power of the regulator as a guarantor, the characteristics of companies and transactions as factors in demand for long-term formula-based contracts, the risks of concerted actions and tacit collusion (as a broader category). The findings enable participants in the debate both within and outside the academic community to understand the scope of what is possible in constructing a national price indicator system, both in terms of the alternatives available and the price that would have to be paid for the preferred alternative.

Demand and supply side factors that drive delayed referrals from traditional birth attendants to public primary healthcare facilities: Insights from three states in Nigeria.

Despite global efforts encouraging institutional deliveries with skilled attendants, many pregnant women in developing countries such as Nigeria continue to rely on traditional birth attendants (TBAs) for child delivery. Attempts at weeding off TBAs have been firmly resisted by their clients who have developed confidence and trust in their services and herald them as first and trusted responders to child delivery cases. Unfortunately, recent evidence has shown that TBAs in servicing public trust and for other reasons, often do not timely refer their clients to the closest source of formal healthcare-primary healthcare centres (PHCs). Looking into the motivations for these reasons is the crux of the current study. Data were collected through qualitative interviews with 85 respondents across three states in Nigeria. The qualitative data comprised of 73 in-depth interviews and 12 focus group discussions. Respondents were TBAs and other informal health providers, formal health workers, policymakers, community leaders and service users. Data were analysed thematically to explore the demand and supply determinants of referrals from TBAs to the formal healthcare providers. Demand side factors that constrain referrals from TBAs to PHCs were community preference for TBAs, and cultural and religious inclinations of the consumers to TBAs' practices of infusing religious teachings and rules into the process of child delivery. Supply-side factors were infrastructural deficits, staff shortages and lack of sanctions for poor practices in PHCs. Demand and supply-side factors constrain the referral of pregnant women from TBAs to the PHC facility for child delivery. These factors should be addressed, and innovative interventions used to link the TBAs to the formal health system for increased institutional deliveries with skilled birth attendants. The interventions could include incentives to TBAs to encourage early referrals.

Demand Risks and Term Structure of Volatility Index Futures

In this paper, we develop an equilibrium framework to explain the characteristics of volatility index (VIX) futures prices and returns across maturities. In the framework, the investors prefer VIX futures with specific maturities, and the arbitrageurs optimize portfolios based on mean-variance preferences. The model-implied futures prices are affected by the variance and demand risk factors. Theoretical and empirical analyses show that incorporating jump risks helps to explain the higher-order moments of futures and that including the demand factor improves futures pricing performance.

Survey Analysis Of Real Demand Of The Non Customer Community In Perumda Tirtauli Pematangsiantar City

This research aims to find out the real demand survey of the non-customer community of Perumda Tirta Uli, Pematang Siantar City, as well as to find out the reasons why they are not willing to register as customers of the company. The research design is a descriptive method with an in-depth survey. Data collection techniques through questionnaires, interviews and documentaries. Data analysis techniques with stages of data tabulation, data compilation, data analysis of demand factors, supply, socio-economics, customer willingness to pay for water and customer ability to pay water bills. The research results concluded: (1) the reason people were not willing to register as customers was because from the start they had used drilled wells (35%), because installation costs were high (19%), and distribution pipes were far away (6%). while the percentage of reasons for the water not being clear is low (6%), water distribution is often jammed (16%) and the reason for not being able to pay is only (14%). non-customers who have never applied for installing a water connection pipe (92.06%), only 7.94% have applied but it was canceled. It is recommended that the use of drilled wells must be controlled by the regional government through public awareness outreach and/or the issuance of Regional Regulations, so that underground water extraction (ABT) is not excessive

A Case Study on the Application of Evolutionary Algorithms for Solving Non-linear Programming Problems in Water Resource Management

Evolutionary algorithms have been proposed and used in this case study for the solution to non-linear programming problems related to water resources. Heuristic, a type of optimization algorithm characterized by population-based search as well as using the genetic algorithms, are used to model and determine the optimal distribution and utilization of aquatic resources based on environmental and demand factors. These materials include examples based on water distribution networks, managing a reservoir, and forecasting demands of customers, demonstrating the effectiveness of the aforementioned algorithms in solving non-linear constraint problems and in dealing with multiple objectives. The comparative analysis is based on a number of simulation experiments to examine the efficiency of such microevolutionary algorithms as Genetic Algorithm, Particle Swarm Optimization, and Differential Evolution in contrast to the traditional optimization algorithms. These results show that there is significant value in using evolutionary algorithms as a basis for managing and modelling water resources as it offers a pragmatic approach and has the potential to meet the demands of the water industry in terms of sustainability, cost and resource requirements. There seems to be considerable scope for extension of these algorithms to a wide range of natural resources management and administration.

Exploring the dynamics of the transport system energy consumption in Australia − A preliminary systems modelling effort

In the face of an ever-increasing trend in the Australian transport demand with minimal improvement in fuel efficiency, petroleum products continue to dominate the primary energy source for the transport sector. The persistence of this reliance hinges on the urgent formulation and robust implementation of effective policies. This paper endeavours to elucidate how the application of system dynamics modelling can facilitate the development and assessment of efficacious policies to steer the trajectory of transport energy consumption in Australia. To illustrate this approach, we present two compelling case studies − the urban passenger transport system in New South Wales (NSW) and the freight transport system in Australia. The first case study explores the intricate dynamics of urban passenger transport mode choice, which shapes different pathways for energy consumption. These pathways are influenced by relative vehicle utility, corresponding to changes in GDP per capita, congestion levels, toll and fuel costs, and historical travel patterns. The second case study delves into Australia’s complex dynamics of heavy vehicle fleet transition. This transition is contingent on many variables, such as changes in GDP, freight tasks, infrastructure capacity requirements, operational and capital costs of various heavy vehicle classes, and fluctuating fuel costs, among others. Simulation results present the dynamic interplay between supply and demand factors. This showcases how an integrated system dynamics model of the transport system can be developed as a holistic policy assessment tool to achieve a balanced energy consumption in the transport system and thereby the sustainability goals. This study calls for more in-depth research about this problem to support policymakers with a decision-support tool to assist scenario analysis and intervention design over time toward achieving sustainable development goals within Australia’s transport system.