Search Results (879)

The nitrogen and phosphorus pollution in water is highly related to the land use pattern in the watershed. The impacts of the land use patterns on total nitrogen (TN) and total phosphorus (TP) exports in an agricultural watershed of the Lijiang River Basin were studied using the Soil and Water Assessment Tool (SWAT). The SWAT model performed well in simulating runoff, TN, and TP exports, and the R² values were all above 0.67. The model simulation results showed that the total nitrogen (TN) and total phosphorus (TP) outputs in the wet season were 13.97 tons and 1.37 tons, respectively, approximately three times those in the dry season, highlighting that outputs of TN and TP predominantly occurred in the wet season in the basin. The correlation analysis showed that the forest land and water in the sub-basin had negative impacts on TN and TP exports, while the orchard, cultivated land, and building land had a positive correlation with TN and TP exports. Then, scenario simulations were conducted using the calibrated and validated SWAT model. A total of 55 scenarios were set up, involving five land use types with five conversion ratios (10%, 20%, 30%, 40%, and 50%), to analyze the impacts of dynamic land use changes on TN and TP exports. The results showed that the TN and TP exports significantly increased under the conversion of the other land use types into building land, cultivated land, and orchards, and the increasing rate decreased in order, while the TN and TP exports declined with the rising forest and water body area. Generally, the changing rates of TN exports under land use conversion were higher than those of TP exports, except for the orchard conversion. This study revealed that the reasonable planning of land use could alleviate nitrogen and phosphorus pollution, which was helpful for aquatic ecosystem restoration. It provided scientific references for land use planning, aquatic ecosystem restoration, and the achievement of sustainable development goals related to water environment protection in similar karst basins. Full article

Background: Emerging Asian economies face a critical policy dilemma: macroeconomic and sustainability factors affect high-performing and struggling logistics exporters in fundamentally different ways. Methods: Analysing transportation trade data from China, South Korea, India, Vietnam, Malaysia, and Indonesia (2000–2023) using Panel Quantile Autoregressive Distributed Lag (P-QARDL) methodology, this study investigates asymmetric relationships between macroeconomic indicators (real GDP, inflation, real effective exchange rate), sustainability variables (energy intensity, energy prices, CO₂ emissions), and logistics performance measured through transportation trade flows. Results: The results reveal striking performance-dependent heterogeneities that conventional approaches overlook. Economic growth provides 55% larger benefits to high performers (0.345) versus strugglers (0.222), confirming scale advantages. Energy constraints intensify for successful exporters, with energy intensity penalties 12% larger in upper quantiles. CO₂ emissions correlate positively with logistics performance, with effects doubling from lower (0.142) to upper quantiles (0.341), highlighting an intensifying sustainability trade-off. Error correction operates 39% faster during high-performance periods. Conclusions: These asymmetric relationships challenge one-size-fits-all policies, necessitating targeted energy efficiency interventions for high performers and growth-enabling support for struggling exporters. Full article

South Africa’s wine industry has traditionally concentrated on developed nations as its principal export markets. In recent years, regional markets within East Africa have emerged as promising areas for growth. However, these markets have not been examined, with existing research predominantly centred on the export of unprocessed agricultural commodities. This study investigates the factors influencing South Africa’s wine exports to Kenya, Tanzania, and Mauritius, considering wine as a final, value-added agricultural product. A trend analysis was conducted to examine export performance from 2010 to 2022, and an augmented gravity model was employed to identify key factors. The results show a steady increase in wine exports to these markets, with strong demand in Kenya and Tanzania. The gravity model demonstrates that higher production capacity in South Africa, larger populations in the importing countries, advantageous import duty structures, and a depreciated exchange rate positively influence exports, whereas high inflation rates significantly constrain export volumes. Consequently, strategies focused on increasing production, maintaining favourable trade conditions, and leveraging market opportunities could enhance export performance. Additionally, mitigating the effects of inflation through strategic pricing policies and industry collaboration could further strengthen South Africa’s wine export position within East African markets. Full article

Background/Objectives: Accurate full-arch implant impressions are essential for predictable digital prosthodontics, yet the performance of different intraoral scanners (IOSs) remains variable. This in vitro study compared the trueness and precision of three widely used IOSs-Sirona Primescan, 3Shape TRIOS Core, and Medit i700-in a standardized full-arch implant model. Methods: A maxillary model with six multi-unit implants was digitized using a high-accuracy laboratory scanner to obtain the reference dataset. Each IOS was used to perform ten scans, exported as unmodified STL files. Accuracy was evaluated in Geomagic Control X through a two-step alignment and a peri-implant region-of-interest deviation analysis. Trueness (mean absolute surface deviation, µm) and precision (SD) were compared using one-way ANOVA with Tukey’s test (α = 0.05). Results: Primescan and TRIOS Core showed comparable trueness (202.76 ± 13.89 µm and 204.21 ± 2.61 µm, respectively), while Medit i700 demonstrated significantly higher deviations (221.05 ± 6.28 µm) (p < 0.05). TRIOS Core exhibited the highest reproducibility across repeated scans. Conclusions: The three scanners demonstrated measurable accuracy differences under standardized conditions. Primescan and TRIOS Core performed similarly in trueness, with TRIOS Core achieving superior precision. Medit i700 showed higher deviation values but remained consistent in its performance. These findings highlight measurable differences in accuracy and reproducibility among intraoral scanners under standardized laboratory conditions and may assist clinicians in selecting appropriate devices for full-arch digital implant workflows; however, clinical validation is required to confirm their performance in vivo. Full article

In recent years, Peru’s agricultural sector has expanded steadily despite recurrent external shocks and persistent volatility in global commodity markets. This sustained performance reflects the sector’s exposure to international price dynamics, a connection with direct implications for structural sustainability in a small, open and commodity-dependent economy. In this context, the study examines whether the terms of trade (TOT) sustain a stable long-run relationship with Peru’s agricultural GDP and assesses how this linkage shapes structural sustainability. The analysis applies Johansen’s cointegration method combined with a bivariate Vector Error Correction Model (VECM), enabling the identification of common long-run trends and the estimation of adjustment speeds following external shocks. The results reveal a single cointegrating vector and a negative, highly significant error-correction term in the agricultural equation, indicating that the sector gradually corrects deviations from its long-run equilibrium. In contrast, the TOT display no meaningful adjustment mechanism, behaving as a weakly exogenous driver. Short-run effects of external shocks are small and statistically fragile, suggesting that quarterly disturbances are overshadowed by the longer-run correction process. Beyond quantifying these dynamics, the study offers a structural reading of how volatile imported inputs—fertilisers, fuels and agricultural machinery—influence agricultural performance, even when export prices are favourable. Overall, the findings underscore that long-term sustainability depends not only on global price trajectories but also on domestic productive capacities and gradual technological improvement, highlighting the need for adaptive strategies in an environment of persistent global volatility. Full article

In recent decades, Peru’s manufacturing sector has steadily declined in its share of gross domestic product, despite sustained economic growth and repeated improvements in the terms of trade. This study investigates whether this divergence between external bonanza and industrial stagnation reflects a manifestation of Dutch disease, with long-term implications for the structural sustainability of the country’s manufacturing base. A quantitative approach is applied through a multiple linear regression model estimated by Ordinary Least Squares, using quarterly data from 2012 to 2024. The analysis includes control variables such as real gross domestic product, private gross fixed investment, the real exchange rate, and a dummy for COVID-19. The results reveal a negative and statistically significant relationship between terms of trade and manufacturing performance, suggesting that favorable external shocks may undermine productive capacities by exacerbating structural vulnerabilities. Beyond quantifying this effect, the study offers a structural interpretation of how external shocks can erode industrial resilience in economies dependent on commodity exports. These findings underscore that structural sustainability depends not only on external conditions, but also on internal factors such as investment dynamics, institutional governance, and technological innovation capacity. In addressing a gap in the literature on Dutch disease and sectoral sustainability in the Peruvian context, the study concludes by calling for a strategic reorientation of industrial policy toward a more diversified, inclusive, and innovation-driven growth model, capable of absorbing external rents productively and ensuring the long-term resilience of the manufacturing sector amid persistent global volatility. Full article

This study investigates how skilled labor influences the development of the secondary sector in emerging economies, using China as a case study. We focus on the transitional process in which manufacturing growth shifts from labor-intensive expansion toward productivity-driven industrial upgrading. Using provincial-level data from 2000 to 2023, we evaluate the role of skilled labor across different stages of development by applying fixed-effects panel regressions, a difference-in-differences framework, and multiple robustness checks. Our findings reveal that skilled labor does not significantly contribute to secondary sector performance in the early phase, when growth relies primarily on low labor costs and rapid urbanization. However, once regions accumulate sufficient economic capacity and technological readiness, skilled labor becomes an important driver of value added and export performance. Stricter environmental policies further widen regional differences: developed regions benefit from green upgrading supported by skilled workers, while less developed regions face firm exits and weakening industrial output. These results highlight the importance of aligning human capital investments with industrial and environmental policies to promote more balanced and sustainable economic development in emerging markets. Full article

The growing incorporation of environmental policy into international trade has transformed the competitiveness of export industries and raised risks for carbon-intensive products. Focusing on China’s aquatic product exports to the European Union, this study examines how a CBAM-related carbon exposure index is associated with export performance and what this implies for trade competitiveness and adaptation. We construct an HS6-level panel for three aquatic product groups and three EU importers over 2015–2024, combining observed trade, tariff and macroeconomic data with constructed indicators of embedded carbon exposure derived from MRIO-based emission intensities and CBAM rules. Using a structural-gravity framework estimated with PPML (HDFE-PPML), we find a negative association between carbon exposure and export values in a simple specification, but this effect weakens and becomes statistically insignificant once richer importer–year and product fixed effects are introduced. Overall, the results suggest that the early CBAM transition has not yet produced a robust impact on China–EU aquatic trade, but they highlight emerging vulnerabilities for carbon-intensive products and the need for exporters and policymakers to pursue adaptation strategies that safeguard long-run trade competitiveness. Full article

This study examines the macroeconomic impacts of major earthquakes in Türkiye using annual data from 1990 to 2023. Despite growing global interest in disaster economics, evidence on how large seismic events shape national economic performance over extended periods remains limited, particularly in emerging economies. Using data from the World Bank, the Central Bank of the Republic of Türkiye, and the Disaster and Emergency Management Authority, the analysis incorporates real gross domestic product, gross fixed capital formation, consumer prices, and export capacity. A dummy variable identifies years with high-fatality earthquakes. After confirming stationarity, Johansen cointegration and a Vector Error Correction Model were applied. Results indicate that earthquakes exert a statistically significant negative influence on long-term economic growth. Based on the log-level specification, the long-run equilibrium level of real gross domestic product in earthquake years is approximately 45 percent lower than in non-earthquake years. Investment, price stability, and trade capacity support long-term growth. Model diagnostics confirm stability, normality, and no autocorrelation. These findings highlight the structural economic vulnerabilities created by major earthquakes and underscore that disaster risk reduction and resilient infrastructure policies must be integral components of sustainable growth strategies. The study contributes updated national time-series evidence from a structurally fragile context. Full article

Oil palm is an important economic crop that is widely cultivated, especially in Southeast Asia. Thailand is one of the world’s largest producers and exporters of palm oil. Efficient management of oil palm plantations is crucial for increasing yields and minimizing agricultural losses. This study aimed to develop a smart oil palm plantation and production management system. This system utilizes Internet of Things (IoT) technology and an integrated supervised machine learning model utilizing regression analysis to monitor and control agricultural equipment within the plantation. MySQL database was used for management of sensor data. Python (version 3.9.6) programming and Google Map API were used for data analysis, spatial analysis and data visualization suite in the system. The results showed that the data from the sensors are displayed in real-time, allowing plantation managers to monitor conditions remotely and make informed adjustments as needed. The system also includes data analysis and data visualization tools for decision-making regarding production management. The model attained an accuracy of over 95%, which reflects its reliability in performing the specified prediction task. The system serves as a support tool for automating soil quality monitoring, fertilization, and field maintenance in oil palm plantations. This enhances productivity, reduces operational costs, and improves yield planning. Full article

Geomagnetic disturbances are an emerging sustainability challenge for modern, low-carbon and highly interconnected power systems, affecting both grid stability and market performance. We develop a deep causal neural network that fuses geomagnetic observatory measurements with national operational indicators and, via counterfactual inference, traces shock and no-shock trajectories to estimate instantaneous and cumulative impacts. Using Switzerland as a case, shocks significantly change national load, canton-level consumption, cross-border flows, and balancing prices. East–west disturbances have stronger effects than north–south, highlighting the role of grid topology. At the regional scale, the canton of Aargau shows pronounced cumulative consumption responses, revealing spatial heterogeneity. In cross-border exchanges, imports rise after shocks while exports contract and transit flows decline; balancing prices increase markedly, suggesting that market mechanisms can amplify physical stress into economic impacts. The approach goes beyond correlation and exposure metrics by providing system-level, decision-relevant effect sizes. The main contributions are as follows: (i) a deep causal framework that identifies and quantifies the causal effects of geomagnetic disturbances on grid operations and prices; (ii) topology-linked empirical evidence of directional and spatial asymmetries across national, canton-level, and cross-border indicators; and (iii) actionable levers for system operation and market design. These findings inform risk-aware reserve procurement, topology-aware dispatch, and cross-border coordination in highly interconnected, low-carbon grids, helping to enhance reliability, maintain affordability, and facilitate clean-energy integration. Full article

Phalaenopsis orchids are one of Taiwan’s key floral export products, and maintaining consistent quality is crucial for international competitiveness. To improve production efficiency, many orchid farms outsource the early flask seedling stage to contract growers, who raise the plants to the 2.5-inch potted seedling stage before returning them for further greenhouse cultivation. Traditionally, the quality of these outsourced seedlings is evaluated manually by inspectors who visually detect defects and assign quality grades based on experience, a process that is time-consuming and subjective. This study introduces a smart image-based deep learning system for automatic quality grading of Phalaenopsis potted seedlings, combining computer vision, deep learning, and machine learning techniques to replace manual inspection. The system uses YOLOv8 and YOLOv10 models for defect and root detection, along with SVM and Random Forest classifiers for defect counting and grading. It employs a dual-view imaging approach, utilizing top-view RGB-D images to capture spatial leaf structures and multi-angle side-view RGB images to assess leaf and root conditions. Two grading strategies are developed: a three-stage hierarchical method that offers interpretable diagnostic results and a direct grading method for fast, end-to-end quality prediction. Performance comparisons and ablation studies show that using RGB-D top-view images and optimal viewing-angle combinations significantly improve grading accuracy. The system achieves F1-scores of 84.44% (three-stage) and 90.44% (direct), demonstrating high reliability and strong potential for automated quality assessment and export inspection in the orchid industry. Full article

This study analyzes the determinants of organizational performance and efficiency in Ecuadorian banana-exporting firms, considering human capital management as a strategic axis of competitiveness. Based on a cross-sectional quantitative design, a structured questionnaire was administered to 513 employees from companies registered in the El Oro Chamber of Commerce. The survey evaluated indicators of human capital, organizational climate, leadership, and competencies. To reduce dimensionality and uncover latent patterns, a Principal Component Analysis (PCA) was performed, followed by unsupervised clustering algorithms (K-means and Ward’s method). The results identified three principal components: (i) specific human capital and job support, (ii) general human capital and inter-area coordination, and (iii) applied competencies and current performance, jointly explaining more than 54% of the total variance. The segmentation revealed two major efficiency profiles: one of high specific deployment, characterized by greater training, tenure, and managerial support; and another of low deployment, dependent on individual effort. The evidence confirms that organizational efficiency is grounded in the articulation between idiosyncratic learning, managerial accompaniment, and structured processes. The study extends the application of the Resource-Based View (VRIO framework) to the agro-export context and proposes a replicable multivariate analytics model for diagnosing and strengthening human capital management in labor-intensive sectors. Full article

The postharvest preservation of banana (Musa paradisiaca) is essential to maintain fruit quality and minimize losses during storage and export. Packaging films play a critical role in protecting fruit from mechanical damage and environmental stress. This study compared the physicochemical and mechanical properties of two commercial polyethylene films—high-density polyethylene (HDPE) and low-density polyethylene (LDPE)—under controlled postharvest conditions (13 °C, 95% RH). Films were characterized using Differential Scanning Calorimetry (DSC), Fourier Transform Infrared Spectroscopy (FTIR), Thermogravimetric Analysis (TGA), and Flame Atomic Absorption Spectroscopy (AAS), while tensile testing evaluated mechanical performance. HDPE exhibited greater melting stability (+8%), relative crystallinity (+12%), and tensile strength (+15%) compared with LDPE, which presented higher flexibility. HDPE contained trace zinc (0.82–0.94 mg/100 g), whereas LDPE was zinc-free. Both polymers retained their polyethylene fingerprint without oxidative degradation, confirming structural integrity under cold storage. The TGA data verified the absence of thermally unstable additives rather than operational degradation, supporting material homogeneity. Overall, HDPE demonstrated superior stability and durability for banana packaging applications, highlighting the relevance of integrated polymer diagnostics for safe and sustainable postharvest systems. Full article

Soil erosion threatens agricultural sustainability and water quality in Mediterranean watersheds, necessitating effective Nature-Based Solutions (NBSs) for mitigation. This study applied the InVEST Sediment Delivery Ratio (SDR) model to assess erosion patterns and evaluate NBS effectiveness in the Carapelle watershed (506 km²). The SDR model was calibrated and validated using measured sediment yield data from 2007 and 2008. Model validation achieved a 4.3% deviation from observed data after parameter optimization. Four NBS scenarios were evaluated: contour farming (CF), no-tillage (NT), cover crops (CCs), and combined practices (Comb). Baseline soil loss varied from 2.43 t ha⁻¹ yr⁻¹ (2007) to 3.88 t ha⁻¹ yr⁻¹ (2008), with sediment export ranging from 0.86 to 1.30 t ha⁻¹ yr⁻¹. NT demonstrated the highest individual effectiveness, reducing sediment export by 72.2% on average. The Comb approach (NT + CCs) achieved a superior performance with a 75.9% sediment export reduction and a 70.5% soil loss reduction. Spatial analysis revealed that high-retention zones were concentrated in forest and shrubland, while agricultural zones showed the greatest potential for NBS implementation. NBSs significantly enhance sediment retention services in Mediterranean agricultural watersheds. The InVEST SDR model proves to be effective for watershed-scale assessment. The results provide actionable guidance for sustainable land management and soil conservation policy in erosion-prone Mediterranean environments. Full article