Search Results (1,324)

Limited access to quality seed constrains vegetable productivity and food security in Uganda, where informal seed systems remain central to farmers’ access to seed but are inadequately documented and largely unregulated. This study documented vegetable seed production and management practices, identified opportunities and constraints within informal seed systems, and examined the role of community seed banks in strengthening seed security. Using a mixed-method approach, quantitative surveys and key informant interviews were conducted with seed producers, seed traders, and community seed bank leaders across Uganda. Study respondents were predominantly men, with most respondents aged ≥35 years, despite women’s and youth’s primary role in vegetable production. Respondents reported key seed management constraints of seed storage pests, low-quality seeds, inconsistent market supply and price fluctuations, limited capital, and weak regulatory oversight. Seed treatment practices primarily consisted of sun-drying and plant-based pesticides, while seed quality assessment relied largely on experience and visual inspection, using cues such as seed color, size, and cleanliness, with limited use of standardized testing. Given that most vegetable growers obtain seeds through community-based seed channels, strengthening actors’ capacity to produce and supply high-quality seed is essential for sustaining vegetable production, improving farmer incomes, and enhancing food and nutrition security. Full article

The surging integration of volatile renewable energy severely exacerbates power grid frequency fluctuations, yet conventional frequency regulation (FR) market clearing mechanisms fail to efficiently coordinate heterogeneous energy storage systems (ESSs) due to the complete decoupling of multi-dimensional physical performance from economic dispatch. To resolve this critical industry bottleneck, this paper proposes a novel Stackelberg game-based clearing mechanism tailored for diverse ESS participation. A bi-level optimization framework is constructed to internalize physical FR characteristics into market economics; the upper level minimizes the system operator’s total procurement costs by transforming multi-dimensional physical metrics—including dynamic response rates, time delays, and control accuracy—into endogenous performance penalty factors. Concurrently, the lower level maximizes the individual revenues of heterogeneous ESS aggregators under a Gini coefficient-based fairness constraint to mitigate profit monopolization and promote a more sustainable market ecology. To address the computational challenges of high-dimensional non-convexity, an enhanced hybrid Genetic Algorithm and Quadratic Programming (GA-QP) solver is developed to secure robust convergence to the Stackelberg equilibrium. Comprehensive simulation results confirm that the proposed Stackelberg game-based clearing mechanism enables a highly rational, quality-driven allocation of frequency regulation capacity. By dynamically linking physical performance metrics with economic benefit factors, it successfully achieves an optimal balance of interests between heterogeneous energy storage aggregators and the overarching market. Crucially, compared to conventional purely economic models, this mechanism structurally prevents absolute technology monopoly—drastically reducing the market Gini coefficient from a hazardous 0.85 to a healthy 0.32—while sustaining multi-party equity at a negligible system cost increase of only 1.64%. Ultimately, this framework offers a highly feasible and resilient solution for the efficient clearing of multi-type energy storage in modern power systems. Full article

Identifying the spatio-temporal evolutionary patterns and driving mechanisms of rural tourism network attention is essential for predicting the development trends of the rural tourism industry and delivering refined industrial governance. Taking 356 prefecture-level cities in China from 2015 to 2024 as basic research units, this paper constructs a comprehensive evaluation system for rural tourism network attention based on multi-source data. Furthermore, its spatio-temporal evolution characteristics and internal influencing factors are systematically investigated by means of spatial autocorrelation analysis and geographically weighted regression. The results indicate that the overall level of rural tourism network attention in China shows an obvious fluctuating growth trend, which can be divided into three successive stages, namely steady growth (from 0.8530 in 2015 to 1.2028 in 2019), explosive growth (from 1.9563 in 2020 to 3.7471 in 2021) and high-level fluctuation (maintained in the high range of 2.4–3.4). In addition, with the continuous iteration of internet communication media, the guiding influence of traditional search platforms has gradually weakened, while emerging social media and short-video platforms have become the core carriers of online tourism traffic. Correspondingly, media innovation persistently reshapes the spatial distribution pattern of rural tourism network attention. In terms of spatial characteristics, rural tourism network attention has undergone a significant transformation from geographical gradient polarization to overall regional equilibrium. Specifically, from 2015 to 2024, the overall Moran’s I index remained positive, with values ranging from 0.0116 to 0.1358, indicating an overall trend of gradual decline. High-attention areas are predominantly concentrated in economically developed urban agglomerations, whereas remote and economically underdeveloped regions exhibit contiguous low-value aggregation characteristics, which reveals a remarkable trend of balanced development nationwide. In view of driving mechanisms, highway network density, tourism income, rural tourism resource and enrollment of university students are identified as the core driving factors dominating the spatio-temporal evolution of rural tourism network attention. Moreover, the intensity of the influence of each factor presents distinct spatial heterogeneity. This study further reveals that the spatial heterogeneity of rural tourism network attention calculated using multi-source fused data shows a remarkable convergent characteristic, which can reflect the actual distribution of the rural tourism market more objectively and accurately. Meanwhile, rural tourism network attention is typically characterized by scale-dependent with the spatial distribution at the macro-scale being more balanced than that at the meso- and micro-scales. Full article

Carbon market prices are jointly shaped by policy interventions, energy market fluctuations, and macroeconomic dynamics, and thus exhibit pronounced nonlinearity, non-stationarity, localized abrupt changes, and time-varying uncertainty. From an information-theoretic perspective, carbon price forecasting can be viewed as the extraction and fusion of effective information from a complex market system driven by heterogeneous endogenous and exogenous signals. To address the challenges of accurately characterizing local high-frequency fluctuations in carbon price series, effectively modeling the interactions between endogenous and exogenous variables, and mitigating the structural noise introduced by conventional serial forecasting frameworks, this study proposes ConvTimeXer, a hybrid model combining bidirectional temporal convolution and TimeXer for carbon market price forecasting. Specifically, the model first employs front-end bidirectional temporal convolutions to extract local multi-scale fluctuation features from the endogenous carbon price series. It then leverages the global token and cross-attention mechanism in TimeXer to achieve dynamic decoupling and deep interaction between endogenous and exogenous variables. Finally, residual fusion of shallow and deep features is introduced to enhance the preservation of local details. Experimental results based on data from China’s carbon market over the past three years demonstrate that the proposed framework delivers high predictive accuracy and strong robustness, effectively balancing responsiveness to local abrupt changes with global trend modeling. This study not only provides an effective approach for carbon price forecasting in complex and uncertain market environments, but also offers valuable insights into non-stationary time-series forecasting driven by multi-source heterogeneous information. Full article

Artificial transaction generation remains an important source of potential market manipulation on cryptocurrency exchanges, as it may distort reported liquidity and reduce market transparency. This study proposes a diagnostic framework for detecting unusual trading patterns based on complexity and statistical structure measures derived from high-frequency trade-level data. The analysis considers log-returns, trading volume, and transaction counts, using tail distributions, autocorrelation functions, multifractal characteristics, approximate entropy, and detrended cross-correlations. The methodology is applied to BTC, ETH, and XRP traded on Binance, Bitget, KuCoin, and Kraken over the period from 1 April to 30 June 2025. The results reveal a pronounced anomaly on Bitget for BTC and ETH after mid-May 2025. The number of transactions increases sharply, but there is no proportional increase in traded volume or return fluctuations. This regime is characterised by numerous low-volume trades, weaker autocorrelations, reduced multifractal organisation, higher short-pattern irregularity, and weaker cross-correlations involving the transaction-count series. These features are consistent with a noise-like component in trading activity and may indicate artificially increased transaction counts, although they do not provide direct proof of wash trading. The findings show that complexity-based indicators can be useful for detecting exchange-specific trading anomalies that remain hidden in price-based measures. Full article

Background: Perishable agri-food supply chains require logistics networks that remain economically viable despite fluctuations in demand, transportation costs, and market prices. This study develops and evaluates a two-echelon logistics network for the banana supply chain in Magdalena, Colombia. Methods: A mixed-integer linear programming model was formulated to maximise daily profit by jointly determining collection-centre activation and product flows among 14 producers, five candidate collection centres, and two commercial buyers. The deterministic solution was complemented by sensitivity analysis and 1000 Monte Carlo optimisation scenarios incorporating variability in demand, transportation costs, and selling prices. Results: Under nominal conditions, all five collection centres were activated, the full demand of 42,000 kg/day was served, and the optimal profit was USD 3093/day. Centres C1–C4 operated at full capacity, whereas C5 reached 42.9% utilization. Under uncertainty, the mean profit decreased to USD 1955.70/day, the mean unmet demand was 954.03 kg/day, and shortages occurred in 72.3% of scenarios. C1–C4 remained the network core, while C5 acted as a flexible contingency facility. Conclusions: The proposed framework reveals an efficiency–resilience trade-off overlooked by deterministic optimisation. Demand growth and capacity reductions are the principal operational risks, supporting investment in collection capacity and proactive demand management. Full article

The self-operation and outsourcing cooperation mode has become an important approach for logistics companies to cope with demand fluctuations and resource constraints. However, the hierarchical mechanisms and network characteristics of the factors driving the development of logistics companies in this cooperative mode remain underexplored. A three-stage analytical framework integrating Latent Dirichlet Allocation (LDA), Total Interpretive Structural Modeling (TISM), and Social Network Analysis (SNA) was developed in this study to systematically identify core influencing factors and reveal their interactive structural relationships. The results reveal seven key determinants of enterprise development: the resource management level, the logistics service level, market competition, the outsourcing service level, risk factors, operational costs, and sustainability benefits. The quantitative SNA results demonstrate that operational costs achieve the highest point centrality value of 83.333, acting as the most direct and core outcome factor in the influence network. By contrast, the resource management level, the logistics service level, market competition, and the outsourcing service level are the fundamental root factors of the entire influencing system and generate prominent spillover effects. Accordingly, logistics companies should prioritize refined cost control while enhancing resource integration, service capacity cultivation, and outsourcing management to improve operational resilience and sustainable competitive advantages. This study not only deepens theoretical understanding of the hybrid self-operation and outsourcing operation mode but also provides targeted practical guidance for the transformation, upgrading, and high-quality development of modern logistics companies. Full article

Visibility graphs map time series to networks whose combinatorial structure encodes fractality, recovering the Hurst exponent of self-affine processes. We ask what the visibility construction carries beyond this fractal content. We lift the visibility graph to a 1-varifold, a measure on position and direction space from geometric measure theory, and equip it with a multiscale positive definite kernel. The lift embeds visibility graphs of unequal size in a common Hilbert space and yields a channel-resolved measure of cross-series geometric alignment. On a 25.8-year daily panel of thirteen commodity and currency layers, we define a relative alignment contrast that compares commodity currencies and safe haven currencies in their geometric alignment with the commodity complex. During global risk-off episodes the contrast is large and positive: commodity currencies import commodity shock geometry far beyond a time-shift independence benchmark, while the Japanese yen remains near geometric independence and the franc is confounded by a managed regime. The contrast is significant under three stress definitions with autocorrelation robust inference, holds as a continuous dose response, survives the removal of any single crisis, withstands moment, fractal, and topological controls, is direction-consistent across sixteen specifications, and collapses under a time-shift placebo. Detrended fluctuation analysis explains only two percent of it, so the reconfiguration is geometric information beyond fractality at this horizon, and a scaling exponent of the kernel mass separates a fractal-free component from a fractal-driven one. For investors, financial institutions, and policymakers, the contrast is a real-time structural diagnostic of flight to safety: it marks when commodity currencies stop diversifying the commodity complex while genuine safe havens still do, signaling through a channel that second-moment risk measures are built to miss. Full article

Renewable energy communities (RECs) are increasingly recognized as a strategic instrument for enhancing the sustainability and resilience of energy systems, promoting local renewable integration, and reducing consumer exposure to electricity market volatility. This study analyzes the Italian electricity market and assesses the economic performance of RECs relative to individual consumers using high-frequency hourly data from 2021 to 2023, covering both the 2022 European energy crisis and the subsequent Italian regulatory reform of incentive mechanisms. The optimization problem is formulated in physical terms, aiming to maximize locally utilized energy, defined as the sum of self-consumed and shared photovoltaic generation. This choice reflects the structure of the Italian regulatory framework, where incentives are directly linked to the amount of energy shared within the community. In this context, energy-based optimization is preferred to avoid embedding assumptions on discount rates, investment horizons, and financing conditions, which may vary significantly across users and introduce additional uncertainty. From a sustainability perspective, maximizing local energy utilization contributes to improving energy efficiency, reducing reliance on external energy sources, and enhancing the capacity of decentralized systems to absorb market shocks. For this reason, economic indicators such as Net Present Value (NPV) or payback period are not explicitly included in the optimization objective. This is justified by the focus of the analysis on short-term operational performance and exposure to electricity price volatility, rather than long-term investment evaluation. Moreover, given that the economic value of the REC is largely determined by shared energy volumes under the current Italian incentive scheme, maximizing local energy utilization provides a consistent proxy for economic performance. Nevertheless, the integration of financial metrics such as NPV or payback period represents a relevant extension for future research, particularly in the context of investment decision-making. Through panel econometric analysis, we estimate the sensitivity of economic value to electricity price fluctuations. Results show that RECs reduce price sensitivity by approximately 8–15% compared to individual users, as estimated by panel regression coefficients. Furthermore, the volatility of economic value decreases by around 1.95% under the community configuration, particularly during the 2022 price shock demonstrating that RECs exhibit significantly lower price dependence than standalone consumers. To assess the robustness of these findings, a machine learning framework is employed to relax linearity assumptions and capture potential non-linear effects. Results consistently show that while market prices remain an important determinant, RECs substantially attenuate their impact, particularly during periods of extreme price stress. A policy counterfactual comparison between pre- and post-reform incentive structures further indicates that the coefficient of variation decreases by approximately 4.4% under the post-reform incentive scheme, highlighting the role of policy design in supporting economically and operationally sustainable energy communities. Overall, this study develops a data-driven analysis based on a high-frequency synthetic dataset designed to reproduce realistic consumption and generation dynamics, providing robust evidence that RECs contribute not only to renewable energy deployment but also to the economic and systemic sustainability of electricity markets under conditions of high volatility. Full article

In the context of the China Certified Emission Reduction (CCER) market restart, accounting for forestry carbon sinks faces two challenges: ambiguous theoretical classification and the absence of sound measurement standards. To address the lack of reliable value benchmarks for initial measurement and the distortion of income statements caused by subsequent value fluctuations, this paper establishes an accounting measurement system incorporating the “Environmental Value Equivalence Method” and the “Dynamic Fair Value Adjustment Method” and introduces a dedicated “Environmental Equity Reserve” account. The “Environmental Value Equivalence Method” constructs a weighted average benchmark price using observable market inputs such as CEA and CCER prices, which is then adjusted by an ecological adjustment coefficient to establish an initial valuation anchor. For subsequent measurement, the “Dynamic Fair Value Adjustment Method” is applied, under which changes in fair value are recorded directly in the “Environmental Equity Reserve” account rather than in the income statement, thereby isolating unrealized gains and losses during the holding period from current period profit or loss. Using the afforestation carbon sink project in Honghe Prefecture as a case study and drawing on actual transaction data from the CCER market, the study demonstrates the complete operational process from initial recognition and subsequent adjustments to partial disposal. The findings offer a theoretically grounded and practically feasible solution for the accounting treatment of forestry carbon sinks under China’s CCER mechanism. Full article

Against the backdrop of global warming, China has set forth its ‘dual carbon’ goals, striving to achieve carbon neutrality by 2060. As a vital engine of economic development, the Yangtze River Delta region has formulated implementation plans, prioritizing carbon emission reduction. The transport industry, a major source of carbon emissions, plays a crucial role through its transition to clean energy, making it pivotal for advancing regional carbon neutrality. This study categorizes carbon emission drivers based on an assessment of current emissions and dynamic evolution analysis, integrating policy evolution and technological innovation trajectories. These drivers are classified into: transport structure, transport intensity, energy intensity, year-end resident population, per capita GDP, and industrial structure. Using the extended STIRPAT-Ridge model, quantitative analysis of carbon emission drivers is conducted. Employing the Tapio decoupling model, the decoupling state between carbon emissions and economic growth is deconstructed. Empirical findings reveal that carbon emissions from the transport industry in the Yangtze River Delta are influenced by multiple factors, with year-end resident population and industrial structure emerging as primary drivers. The decoupling between carbon emissions and economic growth exhibits fluctuating characteristics, but has been progressively strengthened in recent years by government policy initiatives and market mechanisms. Full article

The construction industry in Jordan faces significant economic and environmental challenges due to high material costs, fluctuating market prices, and the continued reliance on a linear economy model that prioritizes material extraction, consumption, and disposal over reuse and recycling. These challenges contribute to substantial construction waste generation and hinder the transition toward a Circular Economy (CE). Therefore, this study aimed to develop a framework for managing construction waste during the construction phase of residential building projects in Jordan and to facilitate the adoption of circular practices within the construction sector. A questionnaire survey was administered to 31 experts, and the collected data were analyzed using the Relative Importance Index (RII) and the Analytic Hierarchy Process (AHP). Subsequently, the proposed framework was evaluated through a three-round Delphi study involving an independent panel of experts. The results identified the principal barriers to implementation as the low demand for reused or recycled materials, limited stakeholder awareness, and difficulties in material disassembly. The findings further revealed that applying visual management and 5S techniques to improve site efficiency, implementing Building Information Modeling (BIM) for material and component mapping throughout the building life cycle, and providing tax incentives and grants for recycled materials were among the highest-ranked sub-strategies for supporting circular practices and minimizing construction waste. The Delphi evaluation demonstrated strong expert consensus regarding the framework’s applicability and practicality, indicating that it provides a structured, expert-informed approach that may assist policymakers and construction practitioners in promoting circular practices and reducing construction waste in Jordan. In addition, the framework has the potential to support Jordan’s Vision 2025 and contribute to the achievement of the Sustainable Development Goals (SDGs). Full article

The paper examines the multifractal and asymmetric behavior of oil, freight and exchange rate markets in the global trade system with the help of the Asymmetric Multifractal Detrended Fluctuation Analysis (AMF-DFA) technique. Based on daily data of West Texas Intermediate (WTI) crude oil, the Baltic Dry Index (BDI), and the exchange rate between the RMB/USD over the period of post-COVID-19 (2021–2024), the analysis focuses on whether efficiency in the markets varies across time scales and directional regimes. The findings show that there is strong evidence of multifractality in all markets, which implies that the scaling behavior is heterogeneous, and that it is long-range-dependent. Notable directional persistence is found between up and down movements with oil and exchange rate markets showing stronger directional persistence, especially at longer horizons with the freight markets displaying relatively weaker directional persistence. Additional results imply that temporal dependence and nonlinearity are the main drivers of multifractality in oil and exchange rate markets, and short-term fluctuations are prevalent factors in the dynamics of the freight market. These findings not only refute the classical Efficient Market Hypothesis but also provide empirical evidence in support of the Adaptive Market Hypothesis, and how efficiency is dynamic, dependent on scale, and directionally asymmetric. The study contributes by examining asymmetric multifractal efficiency across three trading markets during the post-COVID period, while recognizing that formal cross-market spillover analysis remains a direction for future research. Full article

Each financial market carries a unique “fractal signature” with its own distinct risk and return pattern. This study comparatively deciphers these fractal signatures of the leading stock market indices of the Emerging Seven (E7) countries (Turkey, India, Brazil, Mexico, Russia, China, Indonesia), using Multifractal Detrended Fluctuation Analysis (MFDFA) with data covering the 2021–2025 period. The findings reveal that all examined markets deviate from the classical random walk model and exhibit distinct multifractal characteristics. However, significant differences were observed among these signatures: in contrast to Russia’s chaotic structure, which showed extreme fragility to geopolitical shocks, the Chinese and Mexican markets presented a more stable and homogeneous risk profile. In all indices, it was found that small-scale fluctuations carry a strong long-memory effect (stable trends), while large-scale fluctuations assume a more random character (sudden shocks). This asymmetric behavior confirms the heterogeneous nature of investor expectations. For example, the generalized Hurst exponents H(q) ranged from 0.22 (RTS, Russia) to 0.73 (BIST100, Turkey), and the spectrum width Δα varied between 0.10 (Mexico) and 0.45 (Russia), confirming significant heterogeneity in market complexity. Turkey’s BIST100 index, with its structure encompassing both predictable and sudden-shock-prone dynamics, occupies a balanced position within this spectrum. Consequently, the study confirms that understanding these unique fractal signatures of emerging markets is a fundamental prerequisite for formulating effective risk management strategies and achieving global portfolio diversification. Full article

Agricultural research helps us to reduce the risks associated with climate variability, pests and diseases, market fluctuations, and biennial bearing. However, genotype characterization is often conducted independently of economic performance and yield stability, limiting the identification of genotypes that combine high yield, stability, and economic return potential. This study characterized the genotype × harvest season interaction and gross revenue of Coffea canephora clones evaluated in Rondônia, Brazil, over five harvest seasons (2021–2025). Twenty-eight genotypes were evaluated in a randomized complete block design with four replications and five plants per plot, and yield stability was assessed using the centroid method. Cumulative yield over five harvest seasons totaled 306.22 bags ha⁻¹, with a mean of 61.24 bags ha⁻¹ per harvest season. The genotype × harvest season interaction revealed distinct temporal yield patterns. Genotypes closest to the ideotype of maximum yield and stability included BAG19, BRS1216, GJ8, GJ25, AS2, and BAG24. Gross revenue simulations based on contrasting historical coffee price scenarios indicated the economic superiority of these clones. Differences in yield potential contributed more to gross revenue variation than biennial bearing. Relative to the overall mean of the 28 genotypes, these clones achieved an 85% increase in mean yield and a 61.2% increase in mean gross revenue. Full article