Search Results (1,270)

For a long time, the explanation of the various determinants of oil price fluctuations and their impact on economic activity has been based on the supply and demand mechanism. However, with various volatile changes in the international situation in recent years, such as threats to public health and an increase in regional conflicts, special attention has been paid to the geopolitical context as an additional driver of oil price fluctuations. This study examines the relationship between oil price changes and GDP growth and other macroeconomic variables from the perspective of the vulnerability of oil-importing and oil-exporting countries to unexpected oil price shocks, driven by tense geopolitical events, in three European countries (Norway, Germany, and Poland). We apply the Structural Vector Autoregressive (SVAR) model and orthogonalized impulse response functions, based on quarterly data, in regard to two samples: the first spans 1995Q1–2019Q4 (pre-2020 sample), with relatively gradual changes in oil prices, and the second spans 1995Q1–2024Q2 (whole sample), with sudden fluctuations in oil prices due to geopolitical developments. A key finding of this research is that vulnerability to unpredictable oil price shocks related to geopolitical tensions is higher than in regard to expected gradual changes in oil prices, both in oil-importing and oil-exporting countries. Different causality patterns and stronger responses in regard to GDP growth during the period, including in regard to tense geopolitical events in comparison to the pre-2020 sample, lead to the belief that economies are not more resilient to oil price shocks as has been suggested by some studies, which referred to periods that were not driven by geopolitical events. Our research also suggests that countries implementing policies to reduce oil dependency and promote investment in alternative energy sources are better equipped to mitigate the adverse effects of oil price shocks. Full article

Background: Rapid urbanization has intensified the challenges of freight transport, particularly in last-mile (LM) delivery, leading to rising costs and environmental externalities. Autonomous vehicles (AVs) have emerged as a promising innovation to address these issues. While much of the existing literature emphasizes business and operational perspectives, this study focuses on the acceptance of AVs from the standpoint of e-consumers—individuals who make purchases via digital platforms—in an emerging market context. Methods: Grounded in an extended Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), which is specifically suited to consumer-focused technology adoption research, this study incorporates five constructs tailored to AV adoption. Structural Equation Modeling (SEM) was applied to survey data collected from 304 e-consumers in Northeast Brazil. Results: The findings reveal that performance expectancy, hedonic motivation, and environmental awareness exert significant positive effects on acceptance and intention to use AVs for LM delivery. Social influence shows a weaker, yet still positive, impact. Importantly, price sensitivity exhibits a minimal effect, suggesting that while consumers are generally cost-conscious, perceived value may outweigh price concerns in early adoption stages. Conclusions: These results offer valuable insights for policymakers and logistics providers aiming to implement consumer-oriented, cost-effective AV solutions in LM delivery, particularly in emerging economies. The findings emphasize the need for strategies that highlight the practical, emotional, and environmental benefits of AVs to foster market acceptance. Full article

Population aging poses significant challenges to the sustainability of pension systems. This study presents an integrated methodological approach that uniquely combines actuarial life-cycle modeling with agent-based simulation to assess the potential of Reverse Mortgage Loans (RMLs) as a dual lever for enhancing retiree welfare and supporting pension system resilience under demographic and financial uncertainty. We explore Reverse Mortgage Loans (RMLs) as a potential financial instrument to support retirees while alleviating pressure on public pensions. Unlike prior research that treats individual decisions or policy outcomes in isolation, our hybrid model explicitly captures feedback loops between household-level behavior and system-wide financial stability. To test our hypothesis that RMLs can improve individual consumption outcomes and bolster systemic solvency, we develop a hybrid model combining actuarial techniques and agent-based simulations, incorporating stochastic housing prices, longevity risk, regulatory capital requirements, and demographic shifts. This dual-framework enables a structured investigation of how micro-level financial decisions propagate through market dynamics, influencing solvency, pricing, and adoption trends. Our central hypothesis is that reverse mortgages, when actuarially calibrated and macroprudentially regulated, enhance individual financial well-being while preserving long-run solvency at the system level. Simulation results indicate that RMLs can improve consumption smoothing, raise expected utility for retirees, and contribute to long-term fiscal sustainability. Moreover, we introduce a dynamic regulatory mechanism that adjusts capital buffers based on evolving market and demographic conditions, enhancing system resilience. Our simulation design supports multi-scenario testing of financial robustness and policy outcomes, providing a transparent tool for stress-testing RML adoption at scale. These findings suggest that, when well-regulated, RMLs can serve as a viable supplement to traditional retirement financing. Rather than offering prescriptive guidance, this framework provides insights to policymakers, financial institutions, and regulators seeking to integrate RMLs into broader pension strategies. Full article

Fuel price increases have long been a contentious issue in Indonesia, sparking intense public and political debates. This study examines how digital media, particularly Kompas.com and Tempo.co, shape public discourse on fuel price hikes through mediatization. Using discourse network analysis, this study compares the political narratives surrounding fuel price increases during the administrations of Susilo Bambang Yudhoyono (2013) and Joko Widodo (2022). The findings reveal a shift in dominant discourse—opposition to price hikes was prominent in both periods, with government authority and economic justification emphasized in 2013, whereas concerns over rising living costs and social unrest dominated in 2022. This study highlights how mediatization has transformed policymaking from deliberative discussions into fragmented media battles, where digital platforms amplify competing narratives rather than facilitating consensus. Kompas.com predominantly featured counter-discourses, while Tempo.co exhibited stronger pro-government narratives in 2013. This study suggests that while digital media plays a crucial role in shaping policy perceptions, it does not necessarily translate into policy influence. It contributes to the broader understanding of the media’s role in policy debates. It underscores the need for more strategic government communication to manage public expectations and mitigate political unrest surrounding fuel price adjustments. Full article

In today’s volatile supply chain environment, organizations require flexible and collaborative procurement strategies. Swap contracts, originally developed as financial instruments, have recently been adopted to address inventory imbalances—such as the 2021 COVID-19 vaccine swap between South Korea and Israel. Despite its increasing adoption in the real world, theoretical studies on swap-based procurement remain limited. This study proposes an integrated model that combines buyer-to-buyer swap agreements with long-term wholesale contracts under demand uncertainty. The model quantifies the expected swap quantity between parties and embeds it into the profit function to derive optimal order quantities. Numerical experiments are conducted to compare the performance of the proposed strategy with that of a baseline wholesale contract. Sensitivity analyses are performed on key parameters, including demand asymmetry and swap prices. The numerical analysis indicates that the swap-integrated procurement strategy consistently outperforms procurement based on long-term wholesale contracts. Moreover, the results reveal that under the swap-integrated strategy, the optimal order quantity must be adjusted—either increased or decreased—depending on the demand scale of the counterpart and the specified swap price, deviating from the optimal quantity under traditional long-term contracts. These findings highlight the potential of swap-integrated procurement strategies as practical coordination mechanisms across both private and public sectors, offering strategic value in contexts such as vaccine distribution, fresh produce, and other critical products. Full article

This study focusses on the transmission of carbon pricing mechanisms in shaping trade dynamics between the Eurozone and key partners: the USA and China. Using Bayesian variable selection methods and a Time-Varying Structural Vector Autoregressions (TV-SVAR) model, the research identifies the key variables impacting EU carbon emissions over time. The results reveal that manufactured products from the US have a diminishing positive impact on EU carbon emissions, suggesting potential exemption from future regulations. In contrast, manufactured goods from the US and petroleum products from China are expected to increase emissions, indicating a need for stricter trade policies. These findings provide strategic insights for policymakers aiming to balance trade and environmental objectives. Full article

This study investigates the critical determinants of customer retention in casual dining restaurants within the context of the post-pandemic “new normal.” Anchored in service quality and consumer behavior theories, the research examines the influences of food quality, health measures, perceived price, brand image, ambiance, and location on customer decision making. Using Partial Least Squares Structural Equation Modeling (PLS-SEM), data from 336 respondents in the National Capital Region, Philippines were analyzed to assess the relationships among these variables and their effects on restaurant selection and customer retention. The results reveal that food quality (β = 0.698, p < 0.05) exerts the strongest influence on restaurant selection, followed by health measures (β = 0.477, p = 0.001), perceived price (β = 0.378, p < 0.02), and brand image (β = 0.341, p < 0.035). Furthermore, health measures (β = 0.436, p = 0.002) and restaurant selection (β = 0.475, p < 0.05) significantly enhance customer retention, while ambiance and location were not found to be significant predictors. These findings offer theoretical contributions to the service quality and consumer trust literature and provide practical and policy-relevant insights for food establishments adapting to health-driven consumer expectations. The study highlights the need for the strategic integration of safety protocols, pricing value, and brand positioning to foster long-term loyalty and resilience in the evolving food service market. Full article

Tourist satisfaction of nighttime urban lakes as scenic areas, such as the Daming Lake, is influenced by multiple factors, which are crucial for tourists’ experiences and the sustainable development of these areas. This paper explores the factors impacting nighttime visitor satisfaction at the Daming Lake Scenic Area. Basing our studies on analysis of the literature and questionnaire surveys, the study constructs a visitor satisfaction evaluation index system based on the Expectancy-Disconfirmation Theory. Utilizing the revised importance-performance analysis method, the study identifies several significant influencing factors including the distinctive features of nighttime shopping products, the rich variety of nighttime tourscape and entertainment products, the aesthetically pleasing design of nighttime lighting products, the affordable price of nighttime dining products, and the diverse methods, reasonable pricing, and multimodal transit options of nighttime transportation. Furthermore, it finds the main factors that reduce tourists’ satisfaction in nighttime urban lakes include: premium pricing of nighttime shopping and dining products, transport infrastructure deficiencies, the cultural connotation of tourism products, and the safety of nighttime tourscape and entertainment products. This research provides insights to enhance satisfaction in urban lake scenic areas and expands the application of the tourist satisfaction theory. Full article

Since the advent of composite quantile regression (CQR), its inherent robustness has established it as a pivotal methodology for high-dimensional data analysis. High-dimensional outlier contamination refers to data scenarios where the number of observed dimensions (p) is much greater than the sample size (n) and there are extreme outliers in the response variables or covariates (e.g., p/n > 0.1). Traditional penalized regression techniques, however, exhibit notable vulnerability to data outliers during high-dimensional variable selection, often leading to biased parameter estimates and compromised resilience. To address this critical limitation, we propose a novel empirical likelihood (EL)-based variable selection framework that integrates a Bayesian LASSO penalty within the composite quantile regression framework. By constructing a hybrid sampling mechanism that incorporates the Expectation–Maximization (EM) algorithm and Metropolis–Hastings (M-H) algorithm within the Gibbs sampling scheme, this approach effectively tackles variable selection in high-dimensional settings with outlier contamination. This innovative design enables simultaneous optimization of regression coefficients and penalty parameters, circumventing the need for ad hoc selection of optimal penalty parameters—a long-standing challenge in conventional LASSO estimation. Moreover, the proposed method imposes no restrictive assumptions on the distribution of random errors in the model. Through Monte Carlo simulations under outlier interference and empirical analysis of two U.S. house price datasets, we demonstrate that the new approach significantly enhances variable selection accuracy, reduces estimation bias for key regression coefficients, and exhibits robust resistance to data outlier contamination. Full article

Electric vehicles (EVs) have been the most dependable and feasible choice for decarbonizing road transport over the last decade. To ensure the advancement of EVs and establish them as a sustainable alternative to internal combustion engine (ICE) vehicles, the EV sector and technological growth have largely relied on government subsidies. A significant challenge for EVs is their faster depreciation compared to ICE vehicles, primarily owing to swift technological advancements that propel the market while simultaneously rendering older EV models outdated too soon. Another factor that leads to the quicker depreciation of EVs is subsidies. The anticipated cessation of subsidies is expected to provide the required leverage to mitigate the rapid value decline in EVs, given the larger price disparity between new and used EVs. Batteries, which enable EVs to be a viable option, significantly contribute to the depreciation of EVs. In addition to the potential decline in EV battery performance, advancements in technology and reduced prices provide newer models with improved range at a more affordable cost. The used EV market accurately represents the rapid devaluation of EVs; consequently, the two topics are tightly related. Though it might not be immediately apparent, it seems evident that the pace of depreciation of EVs significantly contributes to the small size of the second-hand EV market. Depreciation is a key factor influencing the used EV market. This manuscript outlines the key aspects of depreciation and sustainability in the EV transition, especially those linked to rapid technological advancements, such as batteries, in addition to subsidies and the used EV market. The objective of this manuscript is to expose and analyze the relation between the drivers of the second-hand EV market, such as the cost of ownership, technology, and subsidies, and, on the other hand, present the interplay perspectives and challenges. Full article

The international trade in agricultural products is complex and diverse. Global buyers must diversify their import sources, while sellers must explore new market opportunities. In the past, there has been no analysis on how second-tier exporters, with a smaller market share compared to dominant exporters, interact in the same target market and within an existing trade market and what factors affect trade prices and market forces. Based on Vollrath’s revealed competitive advantage index framework, this study analyzes the global tangerine trade (HS08052100) and means of production from 2008 to 2021, performs clustering, and estimates the residual demand elasticities of two main second-tier exporting countries—South Africa and Morocco—in four major importing countries for empirical analysis. The results show that South African tangerines have a lower market share than Moroccan tangerines in the Netherlands, the United States, and the United Kingdom. However, all data indicate that the residual demand elasticity for the country’s products in the target markets is negative, indicating that South African exporters have market influence in all three markets and significantly affect the prices of Moroccan products in these markets. Unlike other studies that have focused on the ranking analysis of export indices, the novelty of this study is that it provides an oligopolistic framework based on agricultural value chain analysis, which can be used for many countries with limited export scales. The method proposed in this study is expected to help citrus traders to effectively find export markets by evaluating the remaining market niches using key market data and the prices of similar competitors in the same category. Full article

The HoReCa (Hotels, Restaurants, and Cafes) sector plays a pivotal role in the economy due to its strong connections with various other industries, including agriculture, food and beverage, construction, packaging, waste management, water, and textiles. Given its broad impact, understanding the perceptions of students—emerging consumers and future professionals—could provide valuable insights for businesses seeking to enhance sustainable practices in ways that resonate with younger generations and improve their competitiveness. However, there is still limited understanding of how students perceive and engage with sustainability in this sector. This study explores student perceptions of sustainability practices within the HoReCa sector, examining their awareness levels, expectations, and behavior. The objective is to assess how effectively current business approaches align with student values regarding sustainability initiatives and identify key factors influencing their engagement. A structured questionnaire was distributed among university students, and the collected data was analyzed using statistical techniques to identify meaningful trends and correlations. Findings revealed a notable disconnect between students’ professed sustainability values and their actual behavior. Primary obstacles included price sensitivity, skepticism toward environmental marketing claims, and insufficient access to clear sustainability information from businesses. Despite supporting sustainable initiatives in principle, students often struggle to translate their values into purchasing decisions. The research suggests that greater business transparency, enhanced sustainability education, and incentive programs could foster increased student engagement. Full article

This study examines the causes and implications of the unprecedented electricity consumption observed in Croatia during an intense heatwave in July 2024. On the evening of 17 July 2024, power demand reached an all-time high of 3381 MW, significantly surpassing the average demand of around 2000 MW. More concerningly, during these peak hours, 35% of the electricity had to be imported due to insufficient domestic generation capacity. As a result, average monthly electricity prices for July and August 2024 exceeded 250 EUR/MWh in the evening hours. Looking ahead, Croatia and Southern Europe are expected to face increasingly hotter summers, pushing power systems to accommodate even higher peak loads. As the energy transition progresses toward a greater reliance on intermittent renewable energy, enhancing power grid flexibility will become essential. Flexible power generation will play a critical role in bridging gaps in renewable energy output. Solutions such as pumped hydro storage and battery systems can store excess renewable energy and release it during peak demand periods. Additionally, demand response strategies—encouraging the shift of electricity usage to times of higher wind and solar availability—offer another effective way to adapt to the intermittent nature of renewable energy sources. Full article

The residential sector has the potential to significantly impact overall energy consumption and emissions due to the long lifespan of residential buildings and the difficulty of outsourcing residential emissions to other nations. This research primarily aimed to investigate the impact of government expenditure on environmental protection in reducing household energy consumption, thus contributing to Canada’s environmental quality. We utilized panel data from ten Canadian provinces covering the period from 1995 to 2020. To estimate household energy demand, both conceptual and empirical models were developed. Advanced second-generation econometric techniques were applied, including appropriate unit root and co-integration tests. Long-term relationships were analyzed using the fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) methodologies. The long-term estimates indicate that all explanatory variables align with theoretical expectations and are highly significant. The findings reveal that government funding for environmental protection, along with province-specific environmental policies, contributes to reducing per capita residential energy consumption, which is essential for enhancing energy efficiency. Additionally, factors such as weather, income, demographic shifts, and energy prices are found to influence household energy consumption trends. These outcomes highlight the importance of increased government funding for environmental protection and underscore the need for provinces to implement context-specific environmental policies. This approach is essential in the effort to reduce household energy consumption across Canada. Full article

This paper analyzes the control strategy for urban battery-swapping stations by optimizing the charging policy based on real-time battery demand and the time required for a full charge. The energy stored in available batteries serves as an electricity buffer, allowing energy to be drawn from the grid when costs or equivalent ${\mathrm{CO}}_2$ emissions are low. An optimized charging policy is derived using dynamic programming (DP), assuming average battery demand and accounting for both the costs and emissions associated with electricity consumption. The proposed algorithm uses a prediction of the expected traffic in the area as well as the expected cost of electricity on the net. Battery tests were conducted to assess charging time variability, and traffic density measurements were collected in the city of Valencia across multiple days to provide a realistic scenario, while real-time data of the electricity cost is integrated into the control proposal. The results show that incorporating traffic and electricity price forecasts into the control algorithm can reduce electricity costs by up to 11% and decrease associated ${\mathrm{CO}}_2$ emissions by more than 26%. Full article