Search Results (7,825)

Japan’s import market for wood pellets has expanded rapidly since the introduction of the feed-in tariff (FIT) scheme in 2012, with imports exceeding six million tonnes in 2024, positioning Japan as the world’s second-largest wood pellet importer. Despite this expansion, empirical evidence on its demand structure remains limited. This study employs a Dynamic Linear Approximate Almost Ideal Demand System (Dynamic LA-AIDS) model incorporating demand inertia stemming from long-term fuel supply contracts to analyze Japan’s wood pellet import demand from 2012Q1 to 2025Q3. The results reveal a distinct two-tiered structure: North American pellets behave as a strategic necessity, exhibiting price-inelastic demand and a tendency toward a stable long-run procurement pattern following price and expenditure shocks, suggesting procurement practices that prioritize supply security under long-term contracts. In contrast, Vietnamese pellets behave as a price-sensitive commodity, displaying price-elastic demand and relatively sustained responsiveness following such shocks. These results indicate a dual procurement strategy under the FIT scheme that balances stability and cost flexibility. Importantly, the Japanese demand structure differs from the more uniformly price-inelastic patterns observed in the EU and South Korean markets, providing new insights into how institutional frameworks shape biomass allocation and market responsiveness in renewable energy systems. Full article

This paper develops a dynamic game-theoretic model to evaluate market competitiveness in industries characterized by price competition and adjustment stickiness. We extend the dynamic oligopoly framework for estimating market competitiveness in the literature from a quantity-setting to a price-setting context with differentiated goods. By deriving the subgame perfect equilibrium in a linear-quadratic structure, we utilize an index analogous to the price conjectural variation to measure market competitiveness with differentiated goods. The model is applied to the Chinese retail oil market, and we find that the Chinese retail oil market, particularly dominated by two state firms, exhibits characteristics close to a collusive benchmark within the maintained model. The dynamic game model provides a tractable analytical tool for antitrust authorities to monitor strategic coordination in dynamic environments where price transparency or regulation may facilitate tacit coordination of pricing behavior to a high degree. Full article

Small and Medium-sized Enterprises (SMEs) are central to the industrial fabric of Sub-Saharan Africa (SSA). Yet, they confront increasing demands to implement sustainability practices originating from institutional contexts markedly different from their own. Existing research has tended to neglect the cultural and institutional negotiations inherent in this process, often framing sustainability adoption as a technical or compliance-oriented exercise rather than as a multifaceted cultural adaptation. This study proposes and empirically examines the concept of sustainability acculturation—the process by which firms align global sustainability norms with local business cultures. Drawing on Institutional Theory, the Resource-Based View, and Berry’s Acculturation Model, we present a context-specific framework, tested using a sequential explanatory mixed-methods approach: survey data from 284 manufacturing SMEs across six SSA countries, followed by 24 semi-structured interviews. Structural equation modeling reveals that international market pressure and owner–manager values are direct drivers, whereas local regulatory pressure exhibits only a weak association with deep cultural integration. Managerial commitment and organizational learning mediate these relationships, while Ubuntu values enhance social sustainability integration, and institutional voids diminish regulatory effectiveness. The model accounts for 57% of the variance in sustainability acculturation. Findings show that SSA SMEs employ distinct acculturation strategies—Integration, Assimilation, Resilient Adaptation, and Decoupling—shaped by the interplay of external pressures, internal capabilities, and contextual conditions. The study underscores the importance of culturally attuned, context-specific interventions for sustainable industrial development in SSA. Full article

Predicting garlic prices is difficult because the crop behaves as both an agricultural commodity and a speculative asset. Unlike staple grains, which follow more predictable seasonal supply cycles, garlic can be stored for over a year, its production is geographically concentrated, and its demand remains inelastic. This industry structure makes it susceptible to speculative hoarding, where even minor harvest deficits may trigger sharp price spikes. A typical example is the “Suan Ni Hen” (crazy garlic) phenomenon in the Chinese market: during the 2009–2010 and 2016 periods, speculative capital repeatedly exploited expectations of harvest reduction to engage in large-scale hoarding. According to data released by China’s National Development and Reform Commission (NDRC) at the end of October 2016, national wholesale garlic prices surged by 90% year-on-year, with purchase prices in some major producing areas doubling or multiplying within a short period. Such short-term price bubbles, together with severe volatility and abrupt regime shifts, can make standard forecasting models unreliable in this uncertain environment. Existing methods, ranging from traditional seasonal algorithms to deep learning networks, often overlook the need to decouple the local trend-weekly-seasonal baseline from the dynamic effects of multi-source external signals. This paper proposes GPF-EVMoLE, a compositional multi-step forecasting framework built on an explicit division of labor. The framework first extracts an interpretable local trend and weekly-seasonal baseline through an ETS decomposition module. Two specialized components then process the residual signal: a temporal fusion Transformer-style variable selection network (VSN) uses multi-source external features to identify informative macroeconomic and environmental signals at each forecasting step, while a Mixture of Linear Experts (MoLE) models phase-wise regime shifts within the residual series. Together, these modules adaptively integrate heterogeneous information. This study evaluates the framework on a custom daily evaluation dataset containing 17,685 records across six major producing regions in three provinces. At 7-day and 14-day forecasting horizons, GPF-EVMoLE consistently outperforms eight representative statistical, machine learning, and deep learning baselines across MAE, RMSE, and MAPE metrics. Ablation studies verify the necessity of each component, showing that structural separation of the forecasting tasks helps overcome the limitations of monolithic models and provides an accurate and interpretable solution for complex agricultural markets. Full article

This study investigates the effect of green innovation on environmental performance and the moderating role of ownership structure. A generalized method of moments regression approach was applied to a sample of 68 firms operating in the United Arab Emirates (UAE), observed from 2012 to 2024. The results indicate a significant and positive relationship between green innovation and environmental performance. In addition, institutional and state ownership strengthen this relationship. Splitting the sample according to key UAE characteristics (firms listed on the Abu Dhabi Securities Exchange versus the Dubai Financial Market, and the pre-UAE Vision versus post-UAE Vision period) as well as economic conditions (COVID-19) provides further interesting results. Our findings remain robust across alternative estimation methods. The results show significant differences in how ownership structures moderate green innovation effectiveness across the two markets. We also find that green innovation’s effectiveness on environmental performance significantly intensifies after the UAE Vision’s announcement. Our findings also indicate that the positive impact of green innovation on environmental performance becomes more pronounced in the post-COVID period. This paper provides an in-depth assessment of the role of sustainable tools (particularly green innovation) in enhancing environmental performance in the United Arab Emirates. It offers valuable insights for board members, CEOs, regulators, and policymakers who remain undecided or hesitant about implementing sustainability-oriented practices. Full article

Farmland management rights (FMR) mortgage lending has been advanced as a central instrument of rural credit reform in China, yet the program has consistently failed to sustain itself in the absence of direct government facilitation. Drawing on five national and provincial pilot counties in Henan Province, this study investigates the structural factors underlying this sustainability failure. We employ a sequential mixed-methods design: grounded theory analysis of in-depth interviews, policy documents, and media reports from five focal sites to inductively construct a constraint framework, followed by structural equation modeling (SEM) validation using 1055 survey responses. Our grounded theory analysis identifies three internal constraint categories—property rights insecurity, a thin secondary land market, and subject-level agricultural risk—and one external environmental constraint, which together produce a state of mutual non-recognition: neither financial institutions nor farming households regard FMR as legitimate collateral. Notably, the effect of collateral acceptance on farmer mortgage willingness is statistically insignificant, revealing that demand-side barriers are more deeply entrenched than supply-side institutional improvements alone can resolve. These findings challenge the premise that legal formalization of land rights is sufficient to generate market-driven credit activity, and call attention to the equally important role of institutional ecosystem development—encompassing land markets, appraisal capacity, supervisory infrastructure, and rural credit culture. The insights carry direct relevance for developing economies exploring land-backed agricultural credit as a rural finance strategy. Full article

This paper presents a theoretically grounded integration of deterministic Q-learning with relational game theory (QLRG) for efficiently identifying minimal winning coalitions in Online Social Networks (OSNs). We address the fundamental challenge that coalition formation is NP-hard under traditional approaches by leveraging structural properties of relational dependencies and Armstrong’s axioms to transform the problem into one solvable in polynomial time. Our framework reduces the state space from exponential O(2ⁿ) to O(n²) through a sufficient statistic representation based on coalition size, follower reach, and terminal status, while achieving O(n⁴) time complexity under deterministic, static, and sufficiently symmetric influence structures. The QLRG framework introduces three critical innovations: (1) a principled agent selection mechanism derived directly from the Q-function that eliminates heuristic weight tuning; (2) a formal Boost action defined through temporal closure operators that captures influence spread dynamics; and (3) a constrained MDP formulation that enforces relational consistency through action elimination rather than penalty terms. We prove that the Bellman optimality operator forms a contraction mapping, guaranteeing deterministic convergence to optimal policies with established rates of O(1/√k) for decreasing learning rates or linear convergence up to bias for constant rates. To bridge the gap between this idealized model and the asymmetry inherent in real OSNs, we further develop a cluster-based sufficient statistics approach. By partitioning the network into communities with bounded internal variation, we relax the global symmetry requirement while preserving polynomial state space complexity, and obtaining a single within-community swap changes the optimal Q-value by at most ε_i/(1−γ), which is a local Lipschitz continuity result. The implications of this are both theoretical and practical, and they form the bedrock for relaxing the global symmetry assumption in the QLRG framework. Empirical validation on synthetic networks satisfying the symmetry assumption demonstrates that QLRG consistently identifies minimal winning coalitions matching the optimal solutions found by exhaustive search, while operating with polynomial-time complexity. Unlike conventional approaches, our framework simultaneously satisfies four critical properties: deterministic convergence, policy optimality, minimal coalition identification, and computational tractability. The work bridges computational social science and operations research, providing a mathematically rigorous foundation for strategic decision-making in influencer marketing and coalition formation. While the framework requires symmetry assumptions that may only hold approximately in real-world OSNs, it establishes an idealized baseline for future extensions addressing stochasticity, dynamics, and partial observability. This research represents a paradigm shift from empirical improvements to theoretically grounded convergence guarantees for coalition formation problems, demonstrating how structural mathematical insights can transform intractable problems into efficiently solvable ones without sacrificing solution quality. Full article

This study develops a lifecycle economic comparison framework for shared energy storage, in which multiple users share a common storage asset through capacity leasing. A multi-service revenue structure, including capacity leasing, spot-market arbitrage, auxiliary frequency regulation, peak shaving, and capacity compensation, is established for comparative evaluation. Case studies are conducted for lithium iron phosphate (LFP) and vanadium redox flow (VRF) batteries across six representative Chinese electricity markets and six standardized revenue-combination scenarios. The results show that, among the scenarios that more closely reflect current operating practices, P3 (capacity compensation + spot market + auxiliary frequency regulation) delivers the highest net present value (NPV). P6 combines all five revenue streams without explicitly modeling service-coupling dispatch constraints, and is therefore treated as a theoretical benchmark rather than an immediately deployable operating mode. Under this benchmark assumption, its calculated NPV is 21.1% and 41.7% higher than that of P3 for the two battery types, respectively. The study also shows that power-related services are more sensitive to rated power, while spot-market and peak-shaving revenues are more dependent on rated capacity. Full article

This article develops a simple model of the smartphone circular economy where the buy-back price offered to consumers is the main control variable. The model explicitly integrates refurbishment, recycling, and premature phone replacement encouraged by buy-back incentives. We distinguish three types of smartphones: premium, basic, and refurbished premium phones. Furthermore, consumers are segmented by buy-back sensitivity: sensitive users tend to return their phones and shorten their device lifespan, while non-sensitive users keep their phones longer. Additionally, refurbished consumers can buy new phones in case of insufficient supply. Our analysis reveals that with realistic parameters, there exists an optimal buy-back level that minimizes CO₂eq. emissions and waste, and this optimum coincides with the equilibrium between supply and demand on the refurbished market. However, depending on the system parameters, negative effects can be observed. For example, if most components are replaced during refurbishment, emissions increase with the buy-back. More generally, our results highlight the importance of eco-design. While the model is applied to smartphones, its structure is generic and can be adapted to other products with refurbishment and buy-back incentives. Full article

This study analyzes the closure, decommissioning, and abandonment (CDA) of a fuel storage and distribution facility in southern Quito, Ecuador, conceptualizing the process as a socio-technical urban transition embedded within territorial governance dynamics. While infrastructure decommissioning is commonly addressed from a predominantly technical perspective, limited research integrates reverse logistics design, stakeholder influence structures, and territorial development into a unified analytical framework, particularly in Latin American metropolitan contexts. Using a mixed-methods case study approach, the research combines documentary analysis, operational data, and 34 semi-structured interviews with public authorities, engineers, fuel marketers, business owners, and community representatives. A thematic analysis was applied to reconstruct the decommissioning logistics chain and to develop a stakeholder mapping and influence matrix assessing actor positions, economic interdependencies, and legitimacy claims. The findings show that decommissioning operates as a structured reverse logistics system embedded within asymmetric governance configurations, where economic dependency, risk perception, and urban redevelopment expectations generate competing territorial imaginaries. Technical feasibility alone proves insufficient to guide decision-making; instead, legitimacy emerges through the alignment of engineering planning, institutional coordination, and community-level expectations. The study advances an integrated socio-technical framework that articulates Engineering Management, Social Management, and Territorial Development, positioning decommissioning as a governance-driven transition rather than a purely technical operation. The results contribute to sustainability and infrastructure transition scholarship while offering practical guidance for managing urban hydrocarbon infrastructure closure in socially vulnerable territories. Full article

Growing consumer awareness of environmental and health issues has increased demand for sustainable products, yet a persistent gap remains between positive attitudes and actual purchasing behavior. This study addresses inconsistent findings in prior literature regarding the effects of psychological drivers on willingness to pay a premium for green products. Drawing on the Theory of Planned Behavior and value-based perspectives, this study examines how environmental concern, health consciousness, and consumer innovativeness influence purchase intention and willingness to pay a premium (WTP) for green cosmetics. Data were collected from 872 respondents in Indonesia and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with multi-group analysis (MGA) to capture demographic heterogeneity. The results show that all three drivers significantly influence purchase intention, which in turn affects WTP and acts as a partial mediator. Demographic differences further moderate several relationships, highlighting heterogeneity in green consumer behavior. This study contributes by integrating psychological drivers, behavioral mechanisms, and demographic heterogeneity into a unified framework to explain willingness to pay for green cosmetics. The findings offer practical insights for developing targeted strategies to promote sustainable consumption in emerging markets. Full article

Urban housing rents are central to socioeconomic dynamics and urban sustainability, shaping affordability and quality of life. Existing research largely relies on linear models and focuses on economic, demographic, and locational factors, often neglecting complex nonlinear interactions and the impact of human perceptions. This study introduces a comprehensive, multi-perspective framework that integrates housing attributes, living convenience, competition, location, accessibility, and quantified perceptual metrics using multimodal machine learning. Advanced techniques, including XGBoost, SHAP, Partial Dependence Plots (PDPs), Interpretative Structural Modeling (ISM), and Bayesian Network (BN), capture nonlinearities, interactions, and hierarchical dependencies among rent determinants. Housing attributes and living convenience indicators exert the strongest cumulative influence on rents, while perceptual variables rank third, providing significant, threshold-dependent contributions and explaining up to 21.66% of rent variation. Notable interactions are identified between accessibility, facility density, and perceptual quality. The ISM–BN analysis uncovers multi-level pathways, demonstrating how both environmental features and human perceptions jointly influence rents. This framework offers actionable insights for equitable housing and urban planning policies, supporting data-driven decisions in complex urban rental markets. Full article

This study investigates the forecasting of the XBANK banking index traded on Borsa Istanbul by integrating financial and textual data within a deep learning framework. Unlike the majority of existing studies that focus on stable market environments, this paper explicitly examines a period of heightened political uncertainty, namely the cancellation and re-run of the 2019 Istanbul local elections. This setting provides a unique opportunity to analyze how political events and news-driven information flows influence financial market dynamics. The empirical analysis is based on a comprehensive dataset that includes daily price indicators (opening, closing, high, and low values), technical indicators, selected macroeconomic variables, and Turkish-language news headlines. Textual data are processed using topic modeling techniques to extract latent information embedded in financial news, allowing for the incorporation of qualitative signals into the forecasting framework. From a methodological perspective, this study employs a feedforward deep neural network model designed to capture nonlinear relationships across heterogeneous and contemporaneous features. Feature selection is conducted using the Boruta algorithm, while hyperparameters are optimized via grid search. The model structure reflects a deliberate design choice aimed at capturing short-term, news-driven shocks and cross-feature interactions, which are particularly relevant during periods of political uncertainty. The results indicate that incorporating textual information significantly improves forecasting performance and that news topics related to political decisions, central bank policies, and geopolitical developments have a measurable impact on the XBANK index. Furthermore, the findings suggest that the political uncertainty surrounding the 2019 local elections led to increased market sensitivity and volatility, highlighting the role of information shocks in emerging financial markets. Overall, this study contributes to the literature by combining financial and textual data in an emerging market context, utilizing Turkish-language news sources, and providing empirical evidence on the impact of political uncertainty on the BIST bank index. Full article

This paper examines whether a residual structural state extracted from cross-asset downside-risk dependence contains incremental information for forecasting next-day market downside risk beyond a strong heterogeneous autoregressive (HAR) benchmark. The empirical analysis uses Binance intraday data from September 2019 to December 2025 and a fixed sample of 24 liquid cryptocurrencies obtained through explicit data-quality screening and sample diagnostics. The forecasting target is the log of an equal-weight cross-sectional downside-risk index constructed from strictly valid asset-level realized downside semivariance measures. The empirical design is deliberately conservative: the market sample is fixed ex ante, the target is evaluated against Bitcoin (BTC) and Ethereum (ETH) dominance diagnostics, and asset-level HAR-type models are estimated recursively to generate ex-ante one-step-ahead residuals, from which rolling residual-dependence matrices and structural signatures are constructed. The selected residual state contains four components: average residual correlation, Frobenius-type deformation, influence concentration, and influential-set turnover. The evidence supports three qualified conclusions. First, the full residual state attains the lowest average QLIKE loss relative to the HAR benchmark, although the corresponding Diebold–Mariano test under the primary QLIKE loss does not reject equal predictive accuracy at conventional levels. Complementary Clark–West evidence on the nested log-scale comparison supports incremental predictive content for the level-state and full-state augmentations. Second, the strongest forecasting evidence comes from the full state rather than from deformation-only specifications. Third, event-window diagnostics show that structural reorganization is most pronounced around stress-entry and extreme-risk episodes, supporting an onset-sensitive rather than a long-lead early-warning interpretation. Overall, the evidence supports a cautious and statistically qualified predictive conclusion: residual market structure may contain incremental information for short-horizon downside-risk forecasting in cryptocurrency markets, especially around stress onset, but the result should not be interpreted as a decisive primary-loss improvement or as evidence that deformation alone dominates a strong benchmark. Full article

The shift to 15 min market time units (MTUs) in single-day-ahead coupling (SDAC) increases temporal granularity, but complicates the interpretation of intra-hour electricity price spikes and rapid ramps. This paper examines whether architectural decomposition improves the reliability of large language model (LLM)-based diagnostics in price-only settings, rather than causal market analytics, under severe information constraints. We compare a proposed agentic workflow featuring structured context extraction, spike/ramp detection, hypothesis generation, consistency checks, and explicit uncertainty calibration against non-agentic baselines. The paper contributes: (i) a reproducible benchmark for 15 min diagnostic question answering in day-ahead markets, (ii) an agentic architecture tailored to structured time-series reasoning with explicit uncertainty handling, and (iii) empirical evidence that decomposition and verification improve evidence grounding and trustworthiness in market analytics. The evaluation includes 360 price-only cases sampled across autumn 2025, winter 2025–2026, and early spring 2026, balanced by bidding zone, temporal period, event type, and impact tier, comprising 180 spike and 180 ramp cases from six Central and Eastern European bidding zones (Bulgaria, Czechia, Hungary, Poland, Romania, and Slovakia). Using identical inputs, we assess automatic reliability metrics and human ratings. The agentic workflow improves reliability (∆ = +0.067, 95% CI [+0.049, +0.085]) and significantly increases calibrated price-only disclaimers (∆ = +0.500) relative to the monolithic LLM baseline. Human evaluation confirms higher overall quality (+0.74), helpfulness (+1.06), and correctness (+0.94), with a 65.5% pairwise win rate. Overall, the results support a narrower conclusion: structured decomposition and verification improve calibration and perceived explanation quality relative to a simple monolithic LLM baseline, but their advantages are not uniform across stronger non-agentic baselines and remain limited by the absence of exogenous market data. Full article