Trade Paradigm Research Articles

Blockchain with secure data transactions and energy trading model over the internet of electric vehicles

The rise of Electric Vehicles (EVs) has introduced significant advancement and evolution in the electricity market. In smart transportation, the EVs have earned more popularity because of its numerous benefits including lower carbon footprints, higher performance, and sophisticated energy trading mechanisms. These potential benefits have resulted in widespread EV adoption across the world. Despite its benefits, energy management remains the biggest challenge in EVs and it is mainly because of the lack of Charging Stations (CSs) near EVs. This creates a demand for an effective, secure and reliable energy management framework for EVs. This study presents a secure data and energy trade paradigm based on Blockchain (BC) in the Internet of EVs (IoEV). BC technology prepares for the high volume of EV integration that serves as the foundation for the next generation, and to assist in developing unique privacy-protected BC-based D-Trading and storage Models. Entities evaluated for the proposed model include Trusted Authority (TA), Vehicles, Smart Meters, Roadside Units (RSU), BC, and Inter-Planetary File System (IPFS). In addition, E-trading involves several phases, including the acquiring E-trading demand requests, E-trading response requests, request matching and token assignment. Moreover, account mapping is performed using a Mayfly Pelican Optimization Algorithm (MPOA), which is created by merging the Mayfly Algorithm (MA) and Pelican Optimization Algorithm (POA). Various security features are used to protect data and energy trade in IoEV, including encryption, hashing, polynomials, and others. The testing results revealed that the MPOA outperformed the state-of-the-art results regarding memory consumption, trading rate, transaction cost, and trading energy volume with values of 4.605 MB, 91%, 0.654, and 90 kW, respectively.

Distributed optimal storage strategy in the ADMM-based peer-to-peer energy trading considering degradation cost

The increasing installed capacity of distributed energy resources (DERs) allows prosumers to have a more flexible and proactive role in power system operation. At the same time, it may affect the stability of the power grid. To reduce the DERs penetration in the main grid and maximize the profit of the end-users, a new energy trading paradigm called peer-to-peer (P2P) energy trading emerges in the area of the local community microgrid. However, few of the previous works combine types of energy generation and storage equipment to the microgrid framework and propose a throughout distributed optimization solution to this scalable framework considering the impact of battery degradation cost. In this paper, a novel parallel market clearing mechanism is proposed for P2P energy trading with end-users in a highly scalable residential community microgrid. The proposed mechanism takes into account a variety of realistic factors such as household privacy, user preferences, peak shaving services, community power grid-related usage fees, point-to-point bilateral transaction weights, and battery degradation costs. To make the microgrid more scalable, each household in the community is designed with different household energy generation and storage appliances including photovoltaic panels, wind turbines, plug-in electrical vehicles, and various household batteries with different capacities. A distributed approach based on the alternating direction method of multiplier (ADMM) algorithm is presented to solve the energy pricing problem while guaranteeing the privacy of all participants. The designed comparative case studies take into consideration the types of users, grid transaction methods, the influence of peak shaving fee, the impact of energy storage equipment, and the effect of battery degradation costs. The results show a significant reduction of both the cost of each user and the total cost in the P2P energy market while considering the degradation cost of home energy management systems and plug-in electric vehicles. The case studies also present that prosumers have greater advantages than consumers in P2P energy transactions. Peak shaving service and home energy storage devices can distribute and utilize energy more efficiently.

The Sino-ROK chilli pepper trade prior to the establishment of diplomatic relations (1974–1978)

ABSTRACT As the international environment evolved and de-escalated in the 1970s, South Korea (the Republic of Korea, or ROK) adjusted its foreign policy to allow trade with communist governments. In 1974, South Korea attempted to import chilli peppers from the People’s Republic of China, aiming to open a path to dialogue and improve relations. However, the first effort to initiate trade failed due to the Chinese government’s indifferent attitude and trade demands from opponents on both sides. South Korea tried to import Chinese chilli peppers for a second time in 1978. This time, the transaction was successful via indirect trading, thanks to changes in China’s foreign policy and its softening attitude towards South Korea. The success of the Chinese chilli pepper trade in 1978 established a more feasible trade paradigm for China and South Korea in advance of the diplomatic recognition between the two countries. This trade in chilli peppers was directly influenced by the China–South Korea relationship and reflects the complex and subtle changes between the two countries ahead of the establishment of diplomatic relations.

Many-to-Many Task Offloading in Vehicular Fog Computing: A Multi-Agent Deep Reinforcement Learning Approach

Vehicular fog computing (VFC) has emerged as a promising solution to mitigate vehicular network computation load. In the hierarchical VFC, vehicles are employed as mobile fog nodes at the edge to provide reliable and low-latency services. Particularly, since privately-owned vehicles are rational nodes, their intentions for both computation provision and service demand should be considered instead of overestimating their willingness. To remunerate the participation intentions of vehicles as well as improve vehicular fog resource utilization in the large-scale VFC, the trading-based mechanism is a potential solution. In this paper, we propose a many-to-many task offloading framework based on the vehicular trading paradigm. This framework enables computational resource trading across different VFC subsystems and decides the multi-tier task offloading results based on the trading consensus. The trading process is viewed as a partially observable Markov decision process (POMDP) and a Multi-Agent Gated actor Attention Critic (MA-GAC) approach is designed to reach an effective and stable offload-and -serve cooperation among vehicles. Theoretical analyses and experiments verify the feasibility and efficiency of the proposed framework, and simulation results demonstrate that the coordinated MA-GAC approach not only benefits vehicles with higher long-term rewards but also optimizes the system social welfare in a distributed manner.

Global value chains participation and gender inequalities in Sub-Saharan Africa: Importance of women education

In the current climate of international trade paradigm changing, this paper empirically investigates the effects of global value chains (GVCs) participation on gender inequalities in sub-Saharan Africa (SSA) from 1990 to 2015. The study also highlights the role of women's education in the relationship between GVC participation and gender inequalities. The static panel model specification, followed by the estimation technique robust to cross-sectional dependence augmented by country- and year-fixed effects, enabled to overcome cross-sectional dependence, endogeneity, heteroscedasticity, and error autocorrelation. The findings show that GVC participation is beneficial in reducing gender inequalities in SSA. However, these results indicate that SSA countries must turn towards the upstream GVCs participation to better control gender inequalities. Furthermore, the study finds that GVC participation in the agricultural and manufacturing sectors decreases gender inequalities compared to other sectors. Moreover, improving women's education increases the effects of reducing gender inequalities through GVC participation, especially in the manufacturing and service sectors. These outcomes are useful for policymakers.

A Battle of the Big Three?—Competing Conceptualizations of Personal Data Shaping Transnational Data Flows

Abstract In the unfolding digital era, personal data has increasingly been conceptualized in a hybrid way, primarily drawing on notions of economics, privacy, and national security. Such a sui generis nature makes personal data something of its own category, opening the door to different conceptualizations by different actors. This article identifies three primary conceptualizations of personal data adopted by three major powers (the US, the EU, and China) with respect to cross-border data transfers—namely, a factor of production, an embodiment of fundamental rights, and an element of national security. Highlighting different attributes of personal data, these conceptualizations differ in what they understand personal data to be, how they address the relationship between individuals, their data, and the governments, as well as what they frame as the most important normative goals for regulatory interventions. Moreover, these conceptualizations are embedded in different philosophies and ideologies, ranging from neo-liberalism to human rights and to sovereignty. These irreducible normative elements render the contestations among such conceptualizations unable to be easily resolved. Further, divergent conceptualizations of personal data mandate and justify different legal paradigms to institutionalize their basic logics and policy choices. Built on different primary conceptualizations of personal data, three major legal paradigms compete to regulate cross-border personal data flows: a trade paradigm, a privacy paradigm, and a security paradigm. Aligned with its major conceptual framing of personal data, a great power often embraces a particular paradigm as its dominant approach to regulate cross-border transfers of personal data. These paradigms strive to provide legal and regulatory solutions (including legal prescriptions, regulatory institutions, and enforcement tools) to address the salient problems identified and framed by different conceptualizations of personal data.

African Continental Free Trade Agreement's Conditional Most Favoured Nation: A Necessary Compromise?

AbstractThe inception of the African Continental Free Trade Agreement (AfCFTA) constitutes a major advancement in Africa's economic integration process. Diverging from what appears to be the norm in contemporary trade treaties, the agreement adopts a conditional Most Favoured Nation (MFN) clause hinged on the principle of reciprocity. Without the promise to reciprocate preferential treatment, the beneficiary state does not assume the right to demand MFN treatment. In broader discussions, this feature has been criticized for possessing the tendency to restrict trade. However, examining it in the context of Africa's trade paradigm, this paper argues that the non-automaticity of the AfCFTA's MFN clause is a cardinal feature safeguarding its existence.

Hierarchical Hybrid Multi-Agent Deep Reinforcement Learning for Peer-to-Peer Energy Trading Among Multiple Heterogeneous Microgrids

Peer-to-peer (P2P) energy trading among multi-microgrids has emerged as a promising paradigm to facilitate more efficient supply-demand balancing within local areas. However, existing works still exhibit limitations in terms of trading architecture and pricing schemes. In addition, the existing multi-agent deep reinforcement learning (MADRL) methods suffer from computational overload caused by the exploration of joint and hybrid action space during centralized training. In this paper, we propose a P2P energy trading paradigm based on hierarchical hybrid MADRL to maximize the trading profits among multiple heterogeneous MGs. First, we design a novel hierarchical structure of the MC agent to model the coupled interaction between flexible demands scheduling and autonomous quotation. Then, a P2P market that employs an improved mid-market rate (IMMR) pricing scheme is proposed to incentivize participation in local trading. Furthermore, to handle hybrid discrete-continuous action space and reduce computational complexity, we propose a hierarchical hybrid multi-agent double deep Q-network and deep deterministic policy gradient (hh-MADDQN-DDPG) algorithm to split the optimal policy learning-workload into a sequence of two sub-tasks. The DDQN for flexible demands scheduling and DDPG for energy trading. Numerical results of simulation I demonstrate that our hh-MADDQN-DDPG with IMMR increases 25% of the trading profits averaged over the baselines. Results of simulation II show that our hh-MADDQN-DDPG provides higher profits compared with the existing methods while maintaining better computational performance and scalability.

Mean-Field Multi-Agent Reinforcement Learning for Peer-to-Peer Multi-Energy Trading

With increasing numbers of prosumers employed with multi-energy systems (MES) towards higher energy utilization efficiency, an advanced energy management scheme is becoming increasingly important. The incorporation of MES into the existential energy market holds promise for future power systems. The continuous double auction (CDA) market, in a decentralized manner, makes it ideal for enabling peer-to-peer (P2P) energy trading due to its high transparency and efficiency. However, the CDA market is difficult to model when considering the highly stochastic and dynamic behaviors of market participants. For this reason, we formulate this task as a Decentralized Partially Observed Markov Decision Process and propose a novel multi-agent reinforcement learning method that allows each prosumer agent to stabilize the training performance with mean-field approximation and also to maintain the scalability and privacy with market public information. Case studies constructed on a real-world scenario of 100 prosumers show that our method captures the economic benefits of the P2P energy trading paradigm without violating the prosumers' privacy, and outperforms the state-of-the-art methods in terms of policy performance, scalability, and computational performance.

A review of international trade and investment agreements and nutrition policy space in the Pacific.

Small Island Developing States (SIDS) in the Pacific are heavily reliant on imported foods which are often nutritionally deficient, and typically high in salt, fat, and sugar. To curb nutrition-related non-communicable diseases, nutrition policies are needed to create food environments that promote healthy diets. However, international trade and investment agreements (TIAs) may interfere with the policy space for SIDS to regulate their food environments by requiring member states to meet trade obligations that could conflict with their nutrition policy goals. In this review, we identify real examples of where TIAs have been responsible for changes in Pacific SIDS' nutrition policies alongside the potential for further constraints on healthy nutrition policies from Pacific Island participation in TIAs. In addition, we note the effects of regulatory chill from TIA obligations in Pacific SIDS, whereby healthy nutrition policies are not considered, developed, or implemented due to the threat of trade disputes or the complexity of TIA procedural requirements. Existing literature indicates that TIAs have shaped nutrition policies to fit within the global trade paradigm despite SIDS' nutrition policy imperatives. More can be done locally, regionally, and internationally to increase the importance of nutrition in the trade agenda, leverage regional institutions to champion nutrition regulation and support SIDS in navigating the trade and nutrition policy environment.

Analysis of strategic interactions among distributed virtual alliances in electricity and carbon emission auction markets using risk-averse multi-agent reinforcement learning

The incorporation of carbon emission auction market (CEAM) and ancillary service market (ASM) is an emerging trading paradigm in active distribution network (ADN). Such regime not only promotes the elimination of carbon emission, but also facilitates the secure operation of power network, especially considering the participation of distributed virtual alliances (DVAs) consisting of renewable distributed generators (RDGs) with uncertain output. In this research, a bi-level bidding and market clearing dynamic programming model is developed for in-depth analysis of market participants’ bidding strategies and market equilibrium. This model allows DVAs to modify their bidding strategies in the energy market (EM), ASM and CEAM based on the market clearing results and uncertainty of RDG output. Also, a new Meta-Learning based Win-or-Learn-Fast (MLWoLF-PHC) algorithm, which not only enables the fully distributed bidding strategy modification, but also performs well considering uncertainty as a risk-averse method, is proposed to solve this model. Its computational performance, the market equilibrium analysis, and the impact of CEAM on the converged market clearing price of EM and ASM would be thoroughly investigated and examined in the case studies.

New is old? TheEU's Open, Sustainable and Assertive Trade Policy

AbstractThe European Commission presented its Open, Sustainable and Assertive (OSA) trade strategy in early 2021, heralding the document as representing a new and strategic approach to countering dependency and strengthening resiliency. We analyse the OSA through policy paradigms, making two contributions to the trade policy literature. First, we show that embedded liberalism and fair trade—often presented as two trade paradigms —are rather elements of one, namely managed globalisation (MG). Using qualitative and quantitative content analyses, we argue that MG has been more influential in shaping 21st‐century trade policy than heretofore recognised in the literature. Second, we show that the OSA represents a paradigmatic rebalancing and complementarity, between the MG and a realist trade‐as‐foreign‐policy paradigm. The OSA represents an EU seeking to increase its capabilities in order to defend its values and interests, while simultaneously promoting the return to a rules‐based liberal international trading order; the co‐dominance of MG and trade‐as‐foreign‐policy represent an evolution from managing interdependence to managing dependency.

International trade between Turkey and China

Purpose- This study examines the development of China – Turkey international trade with the comparison of macroeconomic trends, global trade developments, bilateral agreements. Also, this study analyses the expected effects of de-globalization, broken global value chains after the Covid19 pandemic and Russia’s invasion of Ukraine. Methodology- The study provides a macroconomic background for international trade developments between 2000 – 2021, focusing on China and Turkey. By comparing and examining world trade developments, the study sets a numerical background for potential future developments. Findings- The analysis reveals that global developments, changing power structures and trade flows has been re-shaping the world economic outlook and causing few debates around the continuity of “globalization”. Rising political nationalism, repercussions of Covid 19 pandemic, supply/demand mismatches, Brexit and Russia’s invasion of Ukraine has shifted international trade paradigms and Great Powers that has been the cause and pioneers of globalization are now forming new partnerships. As one of the largest and most dominant players in global world trade, China has a pivotal role in determining the changing paradigms of international trade. Turkey also plays a critical role in current re-positioning of the world trade not because of the share it has from international trade like United States, European Union, China or Russia but because of the geopolitical importance it has and because of the bilateral relations it builds with other Great Powers. Conclusion- Turkey’s bilateral trade volume has been growing with China and is set to grow further with all the economic and politic steps taken. Covid19 pandemic and the shutdowns have shown to the world the importance of free trade, uninterrupted supply chains and global stability. The pandemic restrictions are finally over but the effects on worldwide economy and politic instability caused by Russia’s invasion of Ukraine have forced new economic alliances. Turkey, with its proximity to European markets and cheap labor, is becoming a new base of production for international players. Numbers prove that bilateral economic relations have grown with China, but the growth has been mostly fueled by exports from China, causing the trade gap to become wider. As Asia is becoming to be the center of global economic growth, it is certain that Turkey’s international trade with China and it’s partners will grow. Keywords: China, Turkey, world trade, international trade, de-globalization, bilateral trade. JEL Codes: M30, M31.

ТОРГОВЛЯ ПОД ВЛИЯНИЕМ ГЛОБАЛИЗАЦИИ И РЕГИОНАЛИЗАЦИИ: ОТ СЕМЕЙНОГО ПРЕДПРИНИМАТЕЛЬСТВА К СИСТЕМНО-СЕТЕВОЙ ПАРАДИГМЕ РАЗВИТИЯ

The article is devoted to understanding the transformation of the forms and principles of organization of the trade sphere under the influence of globalization processes and, at the same time, regionalization of the economy and social relations, intensified at the macro level by centrifugal processes. The author substantiates the objective nature of the ongoing changes in the trading paradigm, formulates the concept of the current “systemic” paradigm, which is based on corporate retail chains and trade and purchasing unions. Marketplaces are for the first time considered as a tool for forming a systemic alternative to retail chains and a condition for the further merging of offline and online forms of trading systems based on omnichannel. The author substantiates the prospects for the transition from an entrepreneurial business model to a “system- network” model, built on the synergy of interests of trade organizations, suppliers and associations of loyal consumers, with the growing importance of the latter, which will allow us to talk about the gradual formation of ecosystems in the consumer market.

글로벌 경제질서 변화에 따른 수출금융 지원제도 개선에 관한 연구

Purpose : This study presents a plan to enhance the competitiveness of SME exporters by identifying problems in the domestic export finance system in accordance with changes in the global economic order.
 Research design, data, and methodology : Reviews of previous studies in the literature are related to domestic export finance support system, and analysis of work procedures in export finance support institutions.
 Results : This study analyzes how the current export finance support system is somewhat insufficient in terms of policies that enhance the export competitiveness of SMEs in a rapidly changing trade environment.
 Conclusions : In order to improve Korea's export finance support system, it is necessary to establish a one-stop financial service system to support a response to the trade paradigm shift, to encourage induction of services in the manufacturing industry, to diversify support programs suitable for the characteristics of SMEs, and to integrate support systems by institution. All are urgently needed.

Peer-to-peer energy trading optimization in energy communities using multi-agent deep reinforcement learning

In the past decade, the global distribution of energy resources has expanded significantly. The increasing number of prosumers creates the prospect for a more decentralized and accessible energy market, where the peer-to-peer energy trading paradigm emerges. This paper proposes a methodology to optimize the participation in peer-to-peer markets based on the double-auction trading mechanism. This novel methodology is based on two reinforcement learning algorithms, used separately, to optimize the amount of energy to be transacted and the price to pay/charge for the purchase/sale of energy. The proposed methodology uses a competitive approach, and that is why all agents seek the best result for themselves, which in this case means reducing as much as possible the costs related to the purchase of energy, or if we are talking about sellers, maximizing profits. The proposed methodology was integrated into an agent-based ecosystem where there is a direct connection with agents, thus allowing application to real contexts in a more efficient way. To test the methodology, a case study was carried out in an energy community of 50 players, where each of the proposed models were used in 20 different players, and 10 were left without training. The players with training managed, over the course of a week, to save 44.65 EUR when compared to a week of peer-to-peer without training, a positive result, while the players who were left without training increased costs by 17.07 EUR.

Computing Resource Provisioning at the Edge: An Overbooking-Enabled Trading Paradigm

The arrival of the Internet of Things (IoT) era calls for cost-effective resource provisioning techniques to secure necessary computing resources in supporting dynamic and computation-intensive applications on diverse IoT devices, where resource trading in edge networks brings a viable prospect. While conventional onsite spot trading may facilitate this paradigm, the inherent extra latency of onsite decision making and unreliable network significantly limit resource trading efficiency and the eventual resource utilization rate. More importantly, the “no show” of confirmed resource buyers incurred by unreliable network conditions, can result in negative impacts on trading performance and resource usage. To this end, resource overbooking is adopted that provides an effective solution in handling “no shows” while substantially improving resource utilization rate. In this article, an interesting overbooking-enabled resource trading paradigm in mobile edge networks is proposed, allowing pre-negotiated forward contracts between the edge server (seller) and a number of overbooked smart devices (buyers), which will be fulfilled during future trading. Specifically, a framework associated with the proposed trading paradigm is introduced first to provide a comprehensive view of the relevant key issues. Then a case study is presented that elaborates how the proposed trading can be implemented in practice. Extensive simulation results and analysis demonstrate that the overbooking-enabled trading paradigm achieves both time and energy efficiency, as well as substantial resource utilization and profit improvements.

공연과 무역의 융복합, 공연무역의 유형과 확대 전략

The purpose of this paper is to convey the possibility and expansion strategy of the Performing Arts Services Trade(hereafter as PAST), a convergence of performing arts and trade. Entering the 4th industrial revolution and information-based society, we have experienced many of big changes in the global economy and trade paradigm and now are expecting some bigger ones to come. To seek for a three-dimensional strategy rather than a flat, one-dimensional solution, this paper focused on performing arts contents as a creative convergence field of three keywords, Digital Transformation, Culture, and Services, which create infinite economic value and are called the pinnacle of cultural services. Beginning with the concept of the PAST, this paper established its four types based on the 4 supply modes of WTO GATS and presented expansion strategy for each type. These approaches are for delivering the purpose of this paper to show the sustainable soft power engine in the convergence of performing arts and trade. Furthermore the internalization strategy called 2-4-7, which is a structure of intrinsic values to achieve the mission of expanding the PAST, and also the B.T.S. DNA were described

Peer‐to‐peer decentralized energy trading in industrial town considering central shared energy storage using alternating direction method of multipliers algorithm

Distributed energy resources are being progressively deployed by industry. The penetration of distributed generation in low voltage (LV) networks can position traditional consumers as market participants. With the improvements in communication networks and the introduction of new energy markets, these prosumers are incentivized to sell their excess production to other industries by participating in peer-to-peer (P2P) energy markets. This market paves the way for developing new technologies such as a shared battery energy storage system (SBESS). In this paper, a central storage unit rents its capacity for the prosumers to reduce the overall peak-load of the microgrid. Each user requests the required SBESS capacity and calculates the best charging and discharging times to reduce their cost. To this end, this paper organized a P2P energy trading paradigm with the presence of SBESS. The optimization problem was simulated and solved using the alternating direction method of multipliers (ADMM) algorithm. Results demonstrate how combining features of P2P energy trading and SBESS can save up to 29% for the industrial town.

The impact of agricultural trade approaches on global economic modeling

Researchers explore future economic and climate scenarios using global economic and integrated assessment models to understand long-term interactions between human development and global environmental changes. However, differences in trade modeling approaches are an important source of uncertainty in these types of assessments, particularly for regional projections. In this study, we modified the Global Change Analysis Model (GCAM) to include a novel logit-based Armington trade structure, to examine two approaches to modeling trade: (1) an approach that represents segmented regional markets (SRM), and (2) an approach that represents integrated world markets (IWM). Our results demonstrate that assuming IWM, i.e., homogeneous product modeling and neglecting economic geography, could lead to lower cropland use (i.e., by 115 million hectares globally) and terrestrial carbon fluxes (i.e., by 25%) by the end of the century under the default GCAM scenario, compared with the logit-based Armington SRM structure. The results are highly heterogeneous across regions, with more pronounced regional trade responses driven by global market integration. Our study highlights the critical role that assumptions about future trade paradigms play in global economic and integrated assessment modeling. The results imply that closer harmonization of trade modeling approaches and trade parameter values could increase the convergence of regional results among models in model intercomparison studies.