Search Results (16,712)

Based on global bilateral six-digit HS code trade data of wood forest products from 2000 to 2022, we constructed a three-dimensional panel dataset combining year, importing country, and exporting country, and used a staggered difference-in-differences (DID) model to systematically examine the impact of regional trade agreements (RTAs) on the export of wood forest products and the mechanisms through which they operate. The results show that the signing of RTAs exerts a significant promoting effect on the export market share, extensive margin, and quantity margin of wood forest products, while exerting a significant negative effect on the price margin. Heterogeneity analysis indicates that the impact of signing RTAs on the ternary margin of wood forest product exports varies significantly with product characteristics, bilateral distance, and the type of trade agreement. Mechanism analysis shows that RTAs produce these effects by reducing bilateral trade costs and strengthening bilateral social ties. Accordingly, this paper proposes policy recommendations to strengthen the RTA network, optimize the bilateral trade environment, and enhance the depth of environmental provisions in RTAs to promote the green and high-quality development of wood forest products. Full article

Firm-level financial statement data form multivariate annual time series with strong cross-variable dependencies and temporal dynamics, yet publicly available panels are often short and incomplete, limiting the generalization of predictive models. We present Latent Financial Time-Series Diffusion (LFTD), a structure-aware augmentation framework that synthesizes realistic firm-level financial time series in a compact latent space. LFTD first learns information-preserving representations with a dual encoder: an FT-Transformer that captures within-year interactions across financial variables and a Time Series Transformer (TST) that models long-horizon evolution across years. On this latent sequence, we train a Transformer-based denoising diffusion model whose reverse process is FiLM-conditioned on the diffusion step as well as year, firm identity, and firm age, enabling controllable generation aligned with firm- and time-specific context. A TST-based Cross-Decoder then reconstructs continuous and binary financial variables for each year. Empirical evaluation on Korean listed-firm data from 2011 to 2023 shows that augmenting training sets with LFTD-generated samples consistently improves firm-value prediction for market-to-book and Tobin’s Q under both static (same-year) and dynamic (τ → τ + 1) forecasting settings and outperforms conventional generative augmentation baselines and ablated variants. These results suggest that domain-conditioned latent diffusion is a practical route to reliable augmentation for firm-level financial time series. Full article

Chile’s livestock industry faces growing demands for emissions reduction, animal welfare, and value creation, while continuing to play a key role in rural food security and pasture-based production systems. In light of Chile’s varied agroclimatic conditions, a diminishing national herd, and shifting market signals, such as alternative proteins and distinctive meat products, this narrative review explores four complementary transition pathways: sustainable intensification, organic and agroecological systems, heritage livestock, and regenerative practices. We map the structural challenges, including grazing dairy and beef herds, fragmented producer organization, and the absence of unified, farm-scale greenhouse-gas measurements. We assess the management strategies that have the strongest support; viz., efficiency gains at the animal/herd level, adaptive grazing and silvopastoral designs, nutrient cycling via manure management and local by-products, and welfare frameworks that are aligned with national law and World Organisation for Animal Health guidance. Heritage systems (e.g., Chilota sheep breed in the Chiloé archipelago) provide resilience, cultural identity, and low-input baselines for stepwise transitions. Regenerative procedures can improve soil function and drought buffering but require context-specific designs and credible outcome-based verification to avoid greenwashing. Key enabling policies include coordinated certification and labeling covering animal welfare and origin. Additional elements are cooperative and territorial governance, targeted R&D and extension services for smallholders, and a transparent, standardized greenhouse-gas measurement framework linking farm-level actions to national inventories. Chile’s pathway is not a single model but a practical combination shaped by regional conditions that can deliver long-term economic sustainability, ecosystem services, and nutrition. Full article

The present study analyzes the possibilities for diversification in the iGaming sector through the integration of concepts derived from financial derivatives theory. The main idea is the development of a model introducing a mechanism for buying and selling bets between two clients as a means of early position closure—an analog to option trading in capital markets. The model is structured in three phases and four conditions, forming eight scenarios with varying probabilities and expected returns. The analysis demonstrates that, under appropriate parameters, the innovation can be potentially profitable for clients and acceptable for the bookmaker, who may offset potential losses through an increased number of registrations and an enhanced corporate image. The proposed conceptual framework provides a theoretical foundation for the creation of a secondary market in iGaming, which could lead to greater market efficiency, increased liquidity, and the rationalization of player behavior. The results emphasize the significance of an interdisciplinary approach combining game theory, behavioral economics, and financial engineering as a basis for sustainable development and competitive advantage in the dynamically evolving iGaming industry. Full article

The proposed model derives closed-form solutions for investment efforts in both product development and its associated production processes while balancing economic profit, product quality, and marginal production costs. The system dynamics, including state and control variables, as well as the relevant constraints, are explicitly formulated. To demonstrate the practical utility of the framework, a numerical example is investigated. Simulation results illustrate adaptive strategies that anticipate future market conditions, manage product–process innovation trade-offs, and respond effectively to changing innovation returns. In the baseline numerical scenario, the model achieves a cumulative profit of 7850 with an average period profit of 196. Finally, a sensitivity analysis is conducted to examine the impact of key model parameters on system performance. Full article

In the context of strategies for the promotion of a sustainable wine industry, the utilization of production regulations under the European Geographical Indications system is seldom contemplated. Furthermore, when such texts are considered, the focus is typically on rules for viticulture or winemaking, rather than on articles governing the boundaries of a PDO or PGI. The present study examines the manner in which regulatory innovation, when viewed from a strictly geographical perspective, can promote the sustainable growth of the sparkling wine districts of Franciacorta and Oltrepò Pavese, which are located in the Italian Lombardy region. Through a comparative analysis of Franciacorta and Oltrepò Pavese, we explore how regulatory frameworks, land-use constraints, and production capacities interact to shape environmental, social, and economic sustainability. Franciacorta’s premium positioning and global reputation are constrained by its limited geographic area, making expansion environmentally and socially challenging. In contrast, Oltrepò Pavese has substantial production potential, particularly for Pinot Noir-based classic-method sparkling wines but suffers from a fragmented identity and weak market recognition. Benchmarking the Prosecco PDO evolution, we propose a sustainability-oriented growth model integrating multiple territories under harmonized rules, termed “Grande Franciacorta”. This framework would enable controlled growth, reduce land pressure in high-density areas, enhance regional competitiveness, and support long-term ecological stewardship. This study outlines managerial implications for producers, emphasizing multi-tier product architectures, dynamic capabilities, and coordinated governance mechanisms. Policy recommendations highlight the need for regulatory frameworks that embed sustainability criteria, optimize land use, and consolidate regional reputation to ensure the long-term viability of high-quality sparkling wine production. Full article

This conceptual article examines the shift of circular business models from policy-driven sustainability initiatives to commercially viable strategies in fast-moving product categories, with particular attention to repair, refurbishment, remanufacturing, and end-of-life recovery. Drawing on a structured narrative review and theoretical synthesis, it argues that circular models seldom scale within a single firm because slowing and closing resource loops require ecosystems that integrate product design, reverse logistics, and secondary markets. The paper develops an analytical framework that combines ecosystem strategy, complex adaptive systems, and common agency theory to explain how distributed complementarities, feedback dynamics, and multi-principal incentives jointly shape ecosystem trajectories. Reinforcing and balancing loops can accelerate, stabilise, or lock ecosystems into low-value routines, while incomplete contracts and divergent metrics may fragment effort and produce measurement traps. To address these coordination externalities, the framework introduces the super-principal as a meta-governance role that aligns principals through shared performance indicators, pooled funding rules, and investments in enabling infrastructures such as traceability. The framework offers implications for circular economy policy and ecosystem strategy aimed at sustaining higher-value circular loops. Full article

Accurate forecasting of agricultural commodity prices is essential for managing market volatility, improving supply chain coordination, and supporting food security-related decision-making. Recent advances in deep learning have demonstrated strong potential for capturing nonlinear and temporal dependencies in commodity price dynamics. In this study, we propose a hybrid long short-term memory–multi-head attention (LSTM–MHA) framework for agricultural commodity price forecasting using global futures market data. The model is trained and evaluated on multivariate global commodity futures prices, reflecting internationally traded benchmark markets rather than region-specific domestic prices. While the empirical analysis is based on global data, the study is motivated by the relevance of international price movements for import-dependent regions, particularly West Africa, where global price transmission plays a critical role in domestic market dynamics. The experimental results demonstrate that the proposed model effectively captures short-term temporal dependencies and provides interpretable attention-based insights into lag relevance. An ablation study further highlights the trade-offs between forecasting accuracy and interpretability across different model configurations. The hybrid architecture combines the time-based pattern identification and weighting capabilities of multi-head attention with the sequential learning capabilities of LSTM. Mean absolute error (MAE), root mean squared error (RMSE), and mean squared error (MSE) were used to evaluate the model’s performance. With an MSE of 0.0124, an RMSE of 0.1114, and an MAE of 0.1097, the model outperformed conventional models like ARIMA and standalone LSTM by three to four times in error reduction. The findings suggest that attention-enhanced deep learning models can serve as valuable analytical tools for understanding global price dynamics and informing policy analysis and risk management in West African agricultural markets. Full article

Wet markets remain a cornerstone of fresh food retail in Chinese cities, continuously evolving alongside urbanization. However, the drivers and implications of their transformation at the city level remain underexplored. Drawing on government documents and survey data from Nanjing and Suzhou, this study reveals that China’s wet market evolution is characterized by incremental semi-formalization and upgrading, preserving their essential role in the food supply chain without displacing other retail formats. This transformation reflects shifting government attitudes, strategic urban planning for food security, and the effective integration of public and private interests. The hybrid governance model, which combines public oversight with private operation, has enhanced wet markets’ resilience, ensuring affordability, freshness, and social interaction. Their adaptability underscores a broader lesson: inclusive urban food systems require soft–hard infrastructure synergy, where physical upgrades coexist with social functions. In this paper, we argue that wet markets exemplify social infrastructure: they are not merely food hubs but spaces fostering civic life, cultural continuity, and equitable access. Their co-evolution with supermarkets and e-commerce challenges the “supermarketization” thesis, highlighting the importance of policy flexibility and localized governance. Our findings offer insights for Global South cities grappling with food system transitions, emphasizing the need to balance modernization with the preservation of informal economies’ social fabric. Full article

The cement industry will be officially regulated by China’s national carbon market. Authenticity and accuracy of emission data are prerequisites and the foundation for ensuring the healthy and stable operation of the market. At present, China’s carbon market mainly adopts the Calculation-Based Method (CBM) for data accounting. However, in the cement sector, this method faces challenges due to the inherent complexity of both raw materials and fuels, making it difficult to obtain accurate emission data through CBM alone. Therefore, regulatory authorities are promoting the installation and application of the Continuous Emission Monitoring System (CEMS) by enterprises. Pilot studies, however, have revealed considerable discrepancies between the data from the two methods. In this study, a combined data monitoring and accounting method was proposed, in which CBM and CEMS were combined to improve emission data quality. The actual operational and emission data from a case enterprise was taken as an example, and this study conducted systematic analysis and research on data collection and preprocessing, operating condition classification, correlation model construction, and abnormal data diagnosis. The results revealed that this combined method can effectively improve the degree of correlation between CBM and CEMS carbon emissions. Moreover, higher accuracy of abnormal data identification can be achieved through statistical testing. This combined monitoring method not only strengthens data tamper-resistance at the enterprise level but also has the potential to reduce regulatory oversight costs, thereby providing reliable technical support for emission data quality control. Full article

To address the limitation of traditional microgrid operator-led optimization models that compromise user-side benefits, this paper proposes a novel method for the collaborative optimal operation strategy of community-based integrated energy microgrids and diversified flexible resources. The method deeply integrates user-side flexibility resources into the decision-making process. Unlike existing research that only considers electro-heat coupling, our model integrates shared energy storage services into an integrated energy system, reflecting a more realistic future application. A Stackelberg game framework is then established with the microgrid operator (MGO) as the leader and the user aggregator as the follower. The user aggregator optimizes its energy strategy by coordinating user demand response, thereby increasing the profits of both itself and the shared energy storage operator. Meanwhile, this model guides the MGO’s pricing decisions for electricity and heat, balancing interests of all stakeholders. To solve the model, a hierarchical approach that merges the Harris Hawks Optimization algorithm with the CPLEX solver is employed. Finally, simulation results demonstrate that the proposed model and strategy significantly enhance user-side revenue and flexibility, achieve a win-win outcome for the user aggregator and MGO, and lay the foundation for future shared energy storage service providers to participate in market pricing as key game entities. Full article

This study examines the pricing efficiency of the Mint Gold Certificate (ALTINS1) traded on Borsa Istanbul and its relationship with the underlying asset (gram gold), focusing on the structural break identified in the data. Analyses conducted using Mann–Kendall trend analysis, the Pettitt structural break test, Rolling Window regression, and the Threshold Error Correction Model (Threshold ECM) reveal that certificate prices have systematically decoupled from the underlying asset, creating a persistent premium exceeding 16%. The findings indicate that the risk structure of the certificate has diverged from the underlying asset, the market has become desensitized to high premium levels (asymmetric threshold effect), and prices move independently of fundamental value through a speculative feedback loop (Granger causality). The study argues that the root cause of this anomaly lies in the “Limits to Arbitrage” problem stemming from supply constraints and short-sale bans, offering new evidence on the pricing efficiency of financial innovations in emerging markets. Full article

Logistics alliance and integrated passenger-freight transit are two widely adopted collaborative logistics modes in rural areas. With the rapid development of agricultural e-commerce, rural “first-mile” logistics has become critical for agricultural products' upward circulation, but remains constrained by high costs and insufficient service provision. Existing studies mainly focus on a single transportation mode and pay limited attention to logistics service providers’ strategic choice among alternative modes under government intervention. Using a Stackelberg game framework, this study models the interaction among the government, a logistics service provider, and a rural bus company, and analyzes transportation mode choice and subsidy effectiveness. The results show that government subsidies improve rural “first-mile” logistics service levels and stimulate demand for cargo collection services. Transportation mode choice is jointly influenced by market share, service cost coefficient, and subsidy intensity. Large-scale logistics service providers tend to adopt the integrated passenger-freight transit mode when subsidies are insufficient, and prefer the logistics alliance mode when subsidy support becomes adequate. These findings suggest that subsidy policies should consider fiscal capacity and regional operating costs: the integrated passenger-freight transit is more suitable under limited budgets, while the logistics alliance becomes preferable for promoting regional logistics development when sufficient subsidies can be sustained. Full article

Consumer attitude uncertainty can hinder disruptive innovation (DI) diffusion in the new energy vehicle (NEV) market and weaken enterprises’ incentives to adopt new technologies. This study develops a dual-layer coupled network model linking consumer attitude dissemination and enterprise R&D strategy evolution under bounded observability. Our simulations show three main findings. First, stronger discouragement of counter-attitudinal dissemination markedly suppresses diffusion and lowers steady-state adoption. Second, diffusion strengthens when consumers weight public information more and firm messaging less, particularly under stronger policy support. Third, network structure shapes diffusion: stronger inter-enterprise connectivity increases adoption, and consumer topology and interaction breadth exert different effects under different network types. These results clarify how information environments, policy support, and cross-layer behavioral modulation jointly shape diffusion regimes. Full article

This study investigates volatility spillovers from the stock markets of the United States, Germany, China, and Japan to the UK stock market using daily data from major benchmark indices (FTSE 100, S&P 500, DAX, Shanghai Composite, and Nikkei 225) and Brent crude oil prices. Using a novel two-stage bootstrap framework, we first model time-varying conditional volatilities with GARCH-family models and compare them with long-memory FIGARCH specifications to account for persistent volatility dynamics. These volatilities are then incorporated into a VAR-X model, treating Brent crude oil price volatility as an endogenous or exogenous variable in robustness checks. To overcome limitations of traditional VARs, bootstrap-corrected GIRFs are employed to trace dynamic, order-invariant impacts across key sub-periods: the global financial crisis, Brexit, COVID-19, and the Ukraine war. We also benchmark our results against the Diebold–Yilmaz connectedness index and conduct rigorous out-of-sample forecasting and Value-at-Risk backtesting. Results reveal heterogeneous spillovers: US and German shocks trigger strong, immediate, and persistent UK market volatility, reflecting deep integration; Chinese shocks are delayed and gradual, while Japanese shocks are muted or short-lived. Spillover intensity is time-varying, peaking during global crises. Our model outperforms standard benchmarks in out-of-sample volatility forecasting and risk management applications. The study offers critical insights for investors seeking international diversification and for policymakers aiming to manage systemic risk in an interconnected global financial system. Full article