Search Results (54)

This paper examines the changing role of emissions trading systems (ETSs) within the macro-financial framework of energy markets, emphasizing price dynamics and systemic spillovers. Utilizing monthly data from seven ETS jurisdictions spanning January 2021 to December 2024 (N = 287 observations after log transformation and first differencing), which includes four auction-based markets (United States, Canada, United Kingdom, South Korea), two secondary markets (China, New Zealand), and a government-set fixed-price scheme (Germany), this research estimates a panel vector autoregression (PVAR) employing a Common Correlated Effects (CCE) model and augments it with machine learning analysis utilizing XGBoost and explainable AI methodologies. The PVAR-CEE reveals numerous unexpected findings related to carbon markets: ETS returns exhibit persistence with an autoregressive coefficient of −0.137 after a four-month lag, while increasing inflation results in rising ETS after the same period. Furthermore, ETSs generate spillover effects in the real economy, as elevated ETSs today forecast a 0.125-point reduction in unemployment one month later and a 0.0173 increase in inflation after two months. Impulse response analysis indicates that exogenous shocks, including Brent oil prices, policy uncertainty, and financial volatility, are swiftly assimilated by ETS pricing, with effects dissipating completely within three to eight months. XGBoost models ascertain that policy uncertainty and Brent oil prices are the most significant predictors of one-month-ahead ETSs, whereas ESG factors are relevant only beyond certain thresholds and in conditions of low policy uncertainty. These findings establish ETS markets as dynamic transmitters of macroeconomic signals, influencing energy management, labor changes, and sustainable finance under carbon pricing frameworks. Full article

Distribution transformers serve as critical nodes in smart grids, and management of their recycling plays a vital role in the full life-cycle management for electrical equipment. However, the traditional manual dismantling methods often exhibit a low metal recovery efficiency and high levels of hazardous substance residue. To facilitate green, cost-effective, and fine-grained recycling of distribution transformers, this study proposes a fine-grained dismantling decision-making system based on a knowledge graph subgraph comparison and multimodal fusion perception. First, a standardized dismantling process is designed to achieve refined transformer decomposition. Second, a comprehensive set of multi-dimensional evaluation metrics is established to assess the effectiveness of various recycling strategies for different transformers. Finally, through the integration of multimodal perception with knowledge graph technology, the system achieves automated sequencing of the dismantling operations. The experimental results demonstrate that the proposed method attains 99% accuracy in identifying recyclable transformers and 97% accuracy in auction-based pricing. The residual oil rate in dismantled transformers is reduced to below 1%, while the metal recovery efficiency increases by 40%. Furthermore, the environmental sustainability and economic value are improved by 23% and 40%, respectively. This approach significantly enhances the recycling value and environmental safety of distribution transformers, providing effective technical support for smart grid development and environmental protection. Full article

This paper highlights the transition from traditional procurement systems to the newly introduced eProcurement system in Saudi Arabia, emphasizing the differences and improvements and their implications for sustainable development. The new system aims to enhance transparency, clarify purchasing methodologies, and build trust with the government through effective governance of government purchases and tender management. Guided by Royal Decree, this system aligns with the eProcurement Program to transition into digital processes for proficient bids and government purchases, contributing to more efficient and sustainable procurement practices. While some public agencies have attempted to adopt the new model contract for executing construction projects, it has faced challenges due to its lack of alignment with the best practices and sustainability considerations. The authors argue that many large projects remain exempt from this system, which poses obstacles to achieving the goals of sustainable economic development. The objective of this paper is to explore the newly revised Saudi procurement contracts in comparison with traditional public works contracts, with a focus on how they address socio-economic and environmental sustainability. The research provides an overview of various aspects related to public works contracts (PWCs) in Saudi Arabia, including framework agreements, online reverse auctions, industry localization, knowledge transfer, traditional lump sum contracts, two-phase tenders, and construction project competitions, analyzing their alignment with sustainable development goals. There is limited literature on recent models introduced by the Saudi government, but there are extensive resources on general contract law principles and international public policy. This foundation helps with understanding the legal aspects of public works contracts in Saudi Arabia, their alignment with international standards, and their implications for fostering sustainable development. By examining the literature, researchers can gain insights into the legal and policy framework governing public works contracts in Saudi Arabia and their role in promoting sustainability. The importance of this research lies in its comparative analysis, offering valuable insights into the evolution of procurement practices in Saudi Arabia and their contribution to sustainable socio-economic growth. Full article

Smart cities are equipped with a vast number of IoT devices, which help to collect and analyze data to improve the quality of life for urban people by offering a sustainable and connected environment. However, the rapid growth of IoT systems has issues related to the Quality of Service (QoS) and allocation of limited resources in IoT-based smart cities. The cloud in the IoT system also faces issues related to higher consumption of energy and extended latency. This research presents an effort to overcome these challenges by introducing opposition-based learning incorporated into Golden Jackal Optimization (OL-GJO) to assign distributed edge capabilities to diminish the energy consumption and delay in IoT-based smart cities. In the context of IoT-based smart cities, a three-layered architecture is developed, comprising the IoT system, the Unmanned Aerial Vehicle (UAV)-assisted edge layer, and the cloud layer. Moreover, the controller positioned at the edge of UAV helps determine the number of tasks. The proposed approach, based on opposition-based learning, is put forth to offer effective computing resources for delay-sensitive tasks. The multi-joint symmetric optimization uses OL-GJO, where opposition-based learning confirms a symmetric search process is employed, improving the task scheduling process in UAV-assisted edge computing. The experimental findings exhibit that OL-GJO performs in an effective manner while offloading resources. For 200 tasks, the delay experienced by OL-GJO is 2.95 ms, whereas Multi Particle Swarm Optimization (M-PSO) and the auction-based approach experience delays of 7.19 ms and 3.78 ms, respectively. Full article

The transition towards renewable energy has increased the importance of virtual power plants (VPPs) in integrating distributed energy resources (DERs). However, questions remain regarding the most appropriate auction mechanisms (pay-as-bid (PAB) versus pay-as-clear (PAC)) and imbalance penalty structures, which significantly influence VPP bidding strategies and market operations. This study employs a three-stage stochastic programming model to evaluate VPP bidding behaviors under these auction mechanisms while also considering the effects of imbalance penalty structures. By simulating various market scenarios, the results reveal that PAC markets offer higher VPP revenues due to settlement at the market-clearing price; they also exhibit greater volatility and elevated imbalance penalties. For instance, power deviations in PAC markets were 52.60% higher than in PAB markets under specific penalty structures, and imbalance penalty cost ranges differed by up to 82.32%. In contrast, PAB markets foster stable, stepwise bidding strategies that minimize imbalance penalties and improve renewable energy utilization, particularly during high- and moderate-generation periods. The findings emphasize the advantages of the PAB mechanism in electricity markets with substantial renewable energy integration, providing significant insights for the design of auction mechanisms that facilitate reliable and sustainable market operations. Full article

Spectrum auctions in international mobile telecommunications (IMT) are a representative method for selling the right to transmit signals within a specific band of electromagnetic waves to communication service providers (CSPs); it is important to design a fair spectrum auction that can benefit both government and auction bidders. The government should reduce the burden of maintenance costs by setting a reasonable initial price and selling it to bidders at the highest price they can afford. However, due to the complex auction rules and decision-making process, not many studies has been conducted on how to select an appropriate initial price for the auction. This study aims at introducing a novel simulation modeling approach to develop a spectrum auction policy for international mobile telecommunications (IMT) using agent-based simulation (ABS), which involves three telecommunications service provider types (i.e., the Aggressive bidder, the Moderate bidder, and the Conservative bidder) and the auction environment of IMT in South Korea. In particular, the proposed approach adopts the exponential utility theory to model the behavior of auction bidders and identify the optimal initial bid price. The devised ABS model is calibrated to the IMT spectrum auction conducted in 2018 in South Korea, and the best initial pricing policy identified (i.e., $85.24 million per spectrum block) regarding a sustainable market environment for existing service providers (i.e., 10 blocks for the Aggressive bidder, 10 blocks for the Moderate bidder, and 8 blocks for the Conservative bidder). The proposed approach will be beneficial to both government agencies and auction bidders under fair competition in the IMT market. Full article

Environmental sustainability and fairness in auction systems are becoming increasingly important as systems evolve with the integration of digital technologies. This paper introduces a novel demand-supply matchmaking (DSM) framework designed to improve fairness and sustainability in auction environments, aligning with the principles of the circular economy. The framework addresses key challenges in supply chain management, such as equitable resource distribution and the reduction of environmental footprints. The framework integrates key aspects of environmental impact assessments, fairness assessments, and behavioral analytics. This enables the simulation of bidder behavior and assessment of auction scenarios. Our simulation results demonstrate that the platform can promote sustainable, fair, and informed auction practices. By comparing our approach with existing tools, we highlight the advantages of using the DSM framework to improve sustainability and fairness in digital marketplaces. This work supports the development of platforms that integrate economic efficiency with environmental responsibility and social equity. Full article

The transportation system has a big impact on daily lifestyle and it is essential to energy transition and decarbonization initiatives. Stabilizing the grid and incorporating sustainable energy sources require technologies like the Internet of Energy (IoE) and Internet of Vehicles (IoV). Electric vehicles (EVs) are essential for cutting emissions and reliance on fossil fuels. According to research on flexible charging methods, allowing EVs to trade electricity can maximize travel distances and efficiently reduce traffic. In order to improve grid efficiency and vehicle coordination, this study suggests an ideal method for energy trading in the Internet of Vehicles (IoV) in which EVs bid for electricity and Road Side Units (RSUs) act as buyers. The Taylor Social Optimization Algorithm (TSOA) is employed for this auction process, focusing on energy and pricing to select the best Charging Station (CS). The TSOA integrates the Taylor series and Social Optimization Algorithm (SOA) to facilitate flow redirection post-trading, evaluating each RSU’s redirection factor to identify overloaded or underloaded CSs. The Flow-DNET model determines redirection policies for overloaded CSs. The TSOA + Flow-DNET approach achieved a pricing improvement of 0.816% and a redirection success rate of 0.918, demonstrating its effectiveness in optimizing electricity trading and flow management within the IoV framework. Full article

With The integration of renewable energy sources into smart grids and electric vehicle (EV) charger-sharing networks is essential for achieving the goal of environmental sustainability. However, the uneven distribution of distributed energy trading among EVs, fixed charging stations (FCSs), and mobile charging stations (MCSs) introduces challenges such as inadequate supply at FCSs and prolonged latencies at MCSs. In this paper, we propose a multi-agent deep reinforcement learning (MADRL)-based auction algorithm for energy trading that effectively balances charger supply with energy demand in distributed EV charging markets, while also reducing total charging latency. Specifically, this involves a MADRL-based hierarchical auction that dynamically adapts to real-time conditions, optimizing the balance of supply and demand. During energy trading, each EV, acting as a learning agent, can refine its bidding strategy to participate in various local energy trading markets, thus enhancing both individual utility and global social welfare. Furthermore, we design a cross-chain scheme to securely record and verify transaction results of energy trading in decentralized EV charger-sharing networks to ensure integrity and transparency. Finally, experimental results show that the proposed algorithm significantly outperforms both the second-price and double auctions in increasing global social welfare and reducing total charging latency. Full article

The land price reflects the supply and demand relationship in the land market and plays an important role in regulating land use. Improving land auction rules is of great significance for avoiding abnormal fluctuations in the land market and promoting the sustainable use of land resources. To regulate the abnormal fluctuations in the state-owned land use rights’ auction prices, Chinese local governments have implemented a “sealed one-time bidding, average price wins” rule. However, limited theoretical and empirical research that assesses its policy impact exists. This study examines the policy motivations behind this rule, constructing three game models; namely, static complete information, static incomplete information, and multiperiod repeated games. By deducing bidding strategies and equilibrium results, hypotheses are formulated. A baseline difference-in-differences (DID) and a dynamic policy effect model are designed, and the Python crawler is used to obtain 1182 microland auction samples in Suzhou. This study evaluates the impact of the one-time bidding rule on the starting prices, transaction prices, and premium rates. The empirical results underwent multiple robustness tests, eliminating potential endogeneity issues and biases. The results show that while the policy is effective in restraining the premium rate, indicating the bidding intensity in single-land auctions, it proves challenging to curb the long-term rise in land prices through continuous bidding auctions. Moreover, the policy may stimulate local governments to increase auction starting prices. Full article

The organic food market’s rapid expansion necessitates an understanding of factors influencing consumer behavior. This paper investigates the impact of packaging colors on perceptions and willingness to pay (WTP) for organic foods, utilizing an experimental auction among university students. Drawing on previous research, we explore how colors influence perceived healthiness, premiumness, trust, and sustainability. The results indicate nuanced responses to different colors, emphasizing the need for businesses to adopt tailored packaging strategies. White and green dominate organic food packaging, aligning with associations of freshness and health. However, the study uncovers varied consumer responses, suggesting a more intricate relationship between color, trust, premiumness, and healthiness perceptions. Demographic factors such as age, gender, income, and residence areas influence WTP for organic foods with different colors, emphasizing the importance of diverse consumer segments in marketing strategies. Trust and perceived premiumness significantly influence WTP, highlighting their pivotal role in consumer valuation. The results highlight that green packaging builds trust among non-organic buyers, while organic buyers are influenced by a broader range of colors that emphasize premiumness and healthiness. The study concludes that businesses in the organic food market should carefully consider color choices in branding and packaging to effectively communicate product qualities and align with consumer values. Full article

Although Cyber–Physical Systems (CPSs) provide a flexible architecture for enterprises to deal with changing demand, an effective method to organize and allocate resources while considering sustainability factors is required to meet customers’ order requirements and mitigate negative impacts on the environment. The planning of processes to achieve sustainable CPSs becomes an important issue to meet demand timely in a dynamic environment. The problem with planning processes in sustainable CPSs is the determination of the configuration of workflows/resources to compose processes with desirable properties, taking into account time and energy consumption factors. The planning problem in sustainable CPSs can be formulated as an integer programming problem with constraints, and this poses a challenge due to computational complexity. Furthermore, the ever-shrinking life cycle of technologies leads to frequent changes in processes and makes the planning of processes a challenging task. To plan processes in a changing environment, an effective planning method must be developed to automate the planning task. To tackle computational complexity, evolutionary computation approaches such as bio-inspired computing and metaheuristics have been adopted extensively in solving complex optimization problems. This paper aims to propose a solution methodology and an effective evolutionary algorithm with a local search mechanism to support the planning of processes in sustainable CPSs based on an auction mechanism. To achieve this goal, we focus on developing a self-adaptive neighborhood search-based Differential Evolution method. An effective planning method should be robust in terms of performance with respect to algorithmic parameters. We assess the performance and robustness of this approach by performing experiments for several cases. By comparing the results of these experiments, it shows that the proposed method outperforms several other algorithms in the literature. To illustrate the robustness of the proposed self-adaptive algorithm, experiments with different settings of algorithmic parameters were conducted. The results show that the proposed self-adaptive algorithm is robust with respect to algorithmic parameters. Full article

Farming is a major sector required for any nation to become self-sustainable. Quality seeds heavily influence the effectiveness of farming. Seeds cultivated by breeders pass through several entities in order to reach farmers. The existing seed supply chain is opaque and intractable, which not only hinders the growth of crops but also makes the life of a farmer miserable. Blockchain has been widely employed to enable fair and secure transactions between farmers and buyers, but concerns related to transparency and traceability in the seed supply chain, counterfeit seeds, middlemen involvement, and inefficient processes in the agricultural ecosystem have not received enough attention. To address these concerns, a blockchain-based solution is proposed that brings breeders, farmers, warehouse owners, transporters, and food corporations to a single platform to enhance transparency, traceability, and trust among trust-less parties. A smart contract updates the status of seeds from a breeder from $submitted$ to $approved$. Then, a non-fungible token (NFT) corresponding to approved seeds is minted for the breeder, which records the date of cultivation and its owner (breeder). The NFT enables farmers to keep track of seeds right from the date of their cultivation and their owner, which helps them to make better decisions about picking seeds from the correct owner. Farmers directly interact with warehouses to purchase seeds, which removes the need for middlemen and improves the trust among trust-less entities. Furthermore, a tender for the transportation of seeds is auctioned on the basis of the priority location $lo{c}_p$, Score, and bid_amount of every transporter, which provides a fair chance to every transporter to restrict the monopoly of a single transporter. The proposed system achieves immutability, decentralization, and efficiency inherently from the blockchain. We implemented the proposed scheme and deployed it on the Ethereum network. Smart contracts deployed over the Ethereum network interact with React-based web pages. The analysis and results of the proposed model indicate that it is viable and secure, as well as superior to the current seed supply chain system. Full article

Regional food marketing initiatives in the United States include state-sponsored marketing programs, promotional efforts made by non-profit organizations, and retail-level supermarket campaigns. Some employ food miles, while others emphasize state boundaries or regions. Given that U.S. consumers are faced with these options, the objectives of this study are to (1) determine whether consumers have a clear preference ranking between three regional marketing logos currently seen in the marketplace, (2) estimate whether consumers are willing to pay a price premium for food mileage information, and if so, what mileage cutoffs are preferred, and (3) uncover whether displaying food mileage, regional marketing logos, or dual-displaying both sets of information is most preferred by consumers. To address these objectives, an artefactual field experiment featuring a series of non-hypothetical, random nth-priced auctions is conducted with 98 community participants in Wisconsin. The experimental auctions feature cheese displaying a regional marketing logo, a food mileage cutoff, or both simultaneously. A random-effects two-limit tobit model is used to fit the elicited bid data. Our results suggest regional logos referencing smaller geographic areas are preferred over state logos by U.S. consumers who are willing to pay a price premium. Consumers are not willing to pay a price premium for food mileage information unless it is within 50 miles. Our results also suggest larger distances do not meet consumers’ definition of local. Therefore, to appeal to consumers, federal and state agencies, retailers, and producers should consider marketing efforts targeting smaller regional areas. Such efforts could help shorten the food supply chain while providing consumers with an opportunity to make more sustainable food choices. Full article

The state-owned power Electricity Generating Authority of Thailand (EGAT), a monopoly market in charge of producing, distributing, and wholesaling power, is the focal point of Thailand’s electricity market. Although the government has encouraged people to install on-grid solar panels to sell electricity as producers and retail consumers, the price mechanism, i.e., purchasing price and selling prices, is still unilaterally determined by the government. Therefore, we are interested in studying the case where blockchain can be used as a free trading platform. Without involving buying or selling from the government, this research presents a model of fully traded price mechanisms. Based on the study results of the double auction system, data on buying and selling prices of electrical energy in Thailand were used as the initial data for the electricity peer-to-peer free-trading model. Then, information was obtained to analyze the trading price trends by using the law of demand and supply in addition to the principle of the bipartite graph. The price trend results agree well with those of price equilibrium equations. Therefore, we firmly believe that the model we offer can be traded in a closed system of free-trade platforms. In addition, the players in the system can help to determine the price trend that will occur according to various parameters and will cause true fairness in the sustainable electricity supply chain industry in Thailand. Full article