Search Results (74)

Agricultural development is vital for East Africa’s economic growth, yet the region faces significant disparities and systemic barriers. A critical problem exists due to the lack of an integrated quantitative framework to systematically comparing agricultural capacities and facilitate optimal resource allocation, as existing studies often overlook combined internal and external factors. This study proposes a comprehensive multi-criteria decision-making (MCDM) model to assess, categorize, and strategically profile the agricultural development capacity of 18 East African countries. The method employed is an integrated Entropy-MAIRCA model, which objectively weighs six criteria (the food production index, arable land, production fluctuation, food export/import ratios, and the political stability index) and ranks countries by their distance from an ideal development state. The experiment applied this framework to 18 East African nations using official data. The results revealed significant differences, forming four distinct strategic groups: frontier, emerging, trade-dependent, and high risk. The food export index (C4) and production volatility (C3) were identified as the most influential criteria. This model’s contribution is providing a science-based, transparent decision support tool for designing sustainable agricultural policies, aiding investment planning, and promoting regional cooperation, while emphasizing the crucial role of institutional factors. Full article

Economically, all countries seek sustained growth driven by domestic demand, investment, and exports; however, COVID-19 revealed the vulnerability of interconnected economic systems and a sharp contraction in global trade. The objective of this research is to analyze through an econometric model the effect of traditional exports on Peru’s economic growth during the 2012–2023 period. The study employed a quantitative approach with a non-experimental, longitudinal design, using quarterly data from the Central Reserve Bank of Peru and the National Bureau of Statistics of China, which were transformed into natural logarithms. Unit root tests, the ordinary least squares (OLS) method and a two-stage least squares (2SLS) model were applied to correct for endogeneity. The results show that mining accounts for 81.7% of total traditional exports from Peru. The model indicated that a 1% increase in traditional exports leads to a 0.29% increase in GDP, confirming a positive impact. However, the high dependence of the mining sector exposes the economy to external risks. Therefore, a productive diversification strategy, alongside the modernization of the mining sector, is recommended to strengthen Peru’s economic resilience in the face of global crises and external fluctuations. Full article

As a core strategy for carbon emission reduction, carbon trading plays a critical role in policy guidance and market stability. Accurate forecasting of carbon prices is essential, yet remains challenging due to the nonlinear, non-stationary, noisy, and uncertain nature of carbon price time series. To address this, this paper proposes a novel hybrid deep learning framework that integrates dual-mode decomposition and a TKMixer-BiGRU-SA model for carbon price prediction. First, external variables with high correlation to carbon prices are identified through correlation analysis and incorporated as inputs. Then, the carbon price series is decomposed using Variational Mode Decomposition (VMD) and Empirical Wavelet Transform (EWT) to extract multi-scale features embedded in the original data. The core prediction model, TKMixer-BiGRU-SA Net, comprises three integrated branches: the first processes the raw carbon price and highly relevant external time series, and the second and third process multi-scale components obtained from VMD and EWT, respectively. The proposed model embeds Kolmogorov–Arnold Networks (KANs) into the Time-Series Mixer (TSMixer) module, replacing the conventional time-mapping layer to form the TKMixer module. Each branch alternately applies the TKMixer along the temporal and feature-channel dimensions to capture dependencies across time steps and variables. Hierarchical nonlinear transformations enhance higher-order feature interactions and improve nonlinear modeling capability. Additionally, the BiGRU component captures bidirectional long-term dependencies, while the Self-Attention (SA) mechanism adaptively weights critical features for integrated prediction. This architecture is designed to uncover global fluctuation patterns in carbon prices, multi-scale component behaviors, and external factor correlations, thereby enabling autonomous learning and the prediction of complex non-stationary and nonlinear price dynamics. Empirical evaluations using data from the EU Emission Allowance (EUA) and Hubei Emission Allowance (HBEA) demonstrate the model’s high accuracy in both single-step and multi-step forecasting tasks. For example, the e_MAPE of EUA predictions for 1–4 step forecasts are 0.2081%, 0.5660%, 0.8293%, and 1.1063%, respectively—outperforming benchmark models and confirming the proposed method’s effectiveness and robustness. This study provides a novel approach to carbon price forecasting with practical implications for market regulation and decision-making. Full article

This study investigates the environmental effects of agricultural trade, renewable energy use, and economic growth in a panel of 14 selected countries for the period 2000–2021. Per capita CO₂ emissions are modeled as the dependent variable using a second-generation panel data method, the Augmented Mean Group (AMG) estimator, which accounts for cross-sectional dependence and slope heterogeneity. The analysis reveals that the share of renewable energy in total energy consumption significantly reduces carbon emissions, emphasizing the role of green energy policies in environmental improvement. In contrast, economic growth is found to increase emissions, indicating the validity of only the initial phase of the Environmental Kuznets Curve (EKC) hypothesis. Additionally, agricultural imports—and in certain cases, exports—exert upward pressure on emissions, likely due to logistics and production-related externalities embedded in the trade process. Group-specific results highlight distinct dynamics across countries: while renewable energy adoption plays a stronger role in emission mitigation in developing economies, trade composition and production technology drive environmental outcomes in developed ones. The findings underscore the need to redesign trade and energy strategies with explicit consideration of environmental externalities to align with long-term sustainability objectives. Full article

South American coffee producers face growing challenges due to external trade dependencies and climate-induced disruptions. This study investigates the role of export market diversification as a sustainability strategy for four major regional exporters of roasted non-decaffeinated coffee: Brazil, Colombia, Peru, and Ecuador. A quantitative and comparative methodology was applied over a ten-year period using the Herfindahl–Hirschman Index (HHI) to evaluate export market concentration and the Revealed Comparative Advantage (RCA) Index—including its normalized variant—to assess international competitiveness by destination. The results reveal substantial disparities: Brazil and Colombia exhibit moderate to high diversification and relative competitiveness in select markets, while Peru and Ecuador remain dependent on a few strategic buyers, with limited or declining comparative advantages. The findings emphasize that sustained export performance in the coffee sector requires not only a broader destination portfolio but also improved positioning through trade agreements, infrastructure development, and climate-resilient innovation. This study concludes with a strategic proposal based on three pillars—commercial, logistical, and technological—to support structural transformation and enhance the long-term sustainability of the coffee trade in South America. Full article

Textile dye production requires significant amounts of water and chemicals, generating substantial wastewater, which places significant burdens on local environments and water resources. Bangladesh is a global textile dye hub, exporting primarily to the EU and the USA. This research explores Water Unequal Exchange (WUE), which arises when high-income countries (HIC) externalize water use and pollution from consumption and production to low-income countries (LIC), driving environmental degradation beyond their borders. To determine WUE, this paper measures wastewater, groundwater, and chemicals embedded in Bangladesh’s textile trade to the EU and USA between 2000 and 2023. This is based on the net weight of the top 18 textile imports from Bangladesh, provided by the UN Comtrade Database. This paper finds that 3,942,091 million liters of groundwater, 10,792,675 million grams of chemicals, and 2,860,420 million liters of wastewater are embedded in these textile imports. The prices per kg of textiles differ depending on product type, and the highest volume of textile product categories have the lowest price per kg. In conclusion, the textile trade from Bangladesh to the EU and the USA represents a case of WUE, where Bangladesh disproportionately internalizes resource over-extraction and environmental impacts from dye production for low economic gain. Full article

This paper argues that investing in artificial intelligence (AI) in developing economies involves significant trade-offs requiring ethical, financial, and geopolitical scrutiny. While AI is increasingly seen as a vehicle for technological leapfrogging, such ambitions often mask structural constraints, including weak infrastructure, limited institutional capacity, and external dependency. Using the economic theory of opportunity cost—extended through the political economy and digital governance perspectives—this study critically examines AI policy strategies in Ghana, Kenya, and Rwanda. A qualitative design grounded in secondary data and a thematic analysis reveal how AI investment may reallocate scarce resources away from essential services, exacerbate inequality, and entrench strategic technological dependency. This paper proposes a public policy framework built on four principles—sequential readiness, strategic alignment, ethical governance, and capacity building—to guide equitable AI deployment. It argues for establishing a digital social compact between states, citizens, and technology actors to safeguard public interest in AI-driven development. Finally, this paper outlines a future research agenda emphasizing the mixed-method evaluation of AI’s long-term social impacts, including employment, inclusion, and public service delivery. Full article

Urban flooding is a growing concern, particularly in coastal lowland cities where climate change exacerbates hazards through rising sea levels and intense rainfall. Traditional flood defenses like fluvial polders often exacerbate urban fragmentation and maintenance costs if poorly integrated into planning. This study proposes a multifunctional assessment design framework to evaluate polder design effectiveness considering both the hydraulic and social–environmental dimensions, emphasizing blue–green infrastructure (BGI) for flood control, leisure, and landscape integration. Three design scenarios for Rio de Janeiro’s Jardim Maravilha neighborhood were modeled hydrodynamically: S1 (dike near urban areas, pump-dependent) and S2/S3 (dikes along the riverbank, gravity-driven). Results show S2/S3 outperformed S1 in storage capacity (2.7× larger volume), freeboard resilience (0.42–0.43 m vs. 0.25 m), and urban integration (floodable parks accessible to communities), though S1 had faster reservoir emptying. Under climate change, all scenarios sustained functionality, but S1’s freeboard reduced by 86%, nearing its limit. The framework’s standardized scoring system balanced quantitative and qualitative criteria, revealing trade-offs between hydraulic efficiency and urban adaptability. The optimized S3 design, incorporating external storage and dredging, achieved the best compromise. This approach aids decision-making by systematically evaluating resilience, operational feasibility, and long-term climate adaptation, supporting sustainable flood infrastructure in coastal cities. Full article

Maritime carbon responsibility allocation can guide sea level rise and storm surge mitigation in BRICS coastal zones by addressing emissions-driven climate risks. This study analyzes the characteristics of and differences in embodied carbon emissions in the Maritime Transport Industry of the BRICS countries from the perspectives of producer responsibility, consumer responsibility, and shared responsibility, based on a global value chain framework. Using non-competitive input–output data from the OECD and introducing a processing trade adjustment mechanism, the study calculates the carbon emissions of the five countries from 1995 to 2018. The empirical results show that under producer responsibility, carbon emissions in China and South Africa’s maritime transport sectors are mainly driven by exports, with production-side emissions significantly higher than consumption-side emissions. Under consumer responsibility, emissions in India and Brazil are driven by the demand for imported goods, reflecting their high reliance on external markets. In shared responsibility accounting, China’s cumulative carbon emissions account for 66.81% of the total emissions from the five countries, highlighting its central role in global supply chains. The study also finds that the differences in carbon emissions among the countries are mainly due to differences in economic structures, trade dependencies, and consumption patterns. Different responsibility accounting methods have a significant impact on carbon emissions, with export-oriented countries tending to weaken producer responsibility, while import-oriented countries seek to avoid consumer responsibility. The shared responsibility mechanism, through the dynamic allocation coefficient $\alpha$, provides a practical approach to balancing efficiency and equity in global carbon governance. Full article

A meticulous thermo-hygrometric control is essential for various industrial production processes, particularly those involving the painting phases of body-in-white, in which the air temperature and relative humidity in production boots must be limited in strict intervals to ensure the high quality of the final product. However, traditional proportional integrative derivative (PID) controllers may result in non-optimal control strategies, leading to energy wastage due to response delays and unnecessary superheatings. In this regard, predictive models designed for control can significantly aid in achieving all the targets set by the European Union. This paper focuses on the development of a predictive model for the energy consumption of an air handling unit (AHU) used in the paint-shop area of an automotive production process. The model, developed in MATLAB 2024b, is based on mass and energy balances within each component, and phenomenological equations for heat exchangers. It enables the evaluation of thermal powers and water mass flow rates required to process an inlet air flow rate to achieve a target condition for the temperature and relative humidity. The model was calibrated and validated using experimental data of a real case study of an automotive production process, obtaining mean errors of 16% and 31% for the hot and cold heat exchangers, respectively, in predicting the water mass flow rate. Additionally, a control logic based on six regulation thermo-hygrometric zones was developed, which depended on the external conditions of temperature and relative humidity. Finally, as the main outcome, several examples are provided to demonstrate both the applicability of the developed model and its potential in optimizing energy consumption, achieving energy savings of up to 46% compared to the actual baseline control strategy, and external boundary conditions, identifying an optimal trade-off between energy saving and operation feasibility. Full article

This paper extends the micro-founded DSGE open economy model by incorporating unionized labor markets. Unlike the standard framework with atomistic unions, large labor unions consider broader economic conditions and internalize the impact of their wage settlements on the aggregate economy. By emphasizing the interplay between internal and external sources of economic distortions and monetary policy regimes, we demonstrate that the economy’s openness, the degree of wage-setting centralization, and different monetary policy regimes influence unions’ wage-setting behavior and macroeconomic outcomes. The analysis identifies three key effects—the monetary policy effect, the intertemporal substitution effect, and the open economy effect—that large unions internalize when adjusting their wage demands in response to policy actions and external conditions. This novel wage-based mechanism alters the New Keynesian Phillips curve, with implications for the conduct of monetary policy, particularly in shaping the economy’s response to shocks and equilibrium determinacy. The real effects of monetary policy shocks under different policy settings depend on large unions’ internalization effect. In a unionized labor market, the impact of monetary shocks on the real economy is amplified compared to the standard case with atomistic unions. Additionally, interactions among large unions, openness, and monetary policy regimes affect determinacy properties of equilibrium (i.e., uniqueness of the solution path) under various forms and timing of monetary policy rules. This paper offers new insights into how union coordination interacts with monetary policy regimes and trade openness to shape macroeconomic stability (uniqueness of rational expectations equilibrium) and the dynamic response of the economy to shocks. These findings enhance our understanding of monetary policy design in economies with strong large labor institutions and external trade exposure—an area that remains underexplored in the existing DSGE literature. Full article

Three-phase induction motors are widely applied in industrial systems due to their durability and efficiency. However, electrical faults such as inter-turn short circuits can compromise performance, leading to unplanned downtime and maintenance costs. Traditional fault detection methods rely on stator current or vibration analysis, each with limitations regarding sensitivity to specific failure modes and dependence on motor power ratings. Despite advancements in non-invasive sensing, challenges remain in balancing fault detection accuracy, computational efficiency, and adaptability to real-world conditions. This study proposes a stray flux-based method for detecting inter-turn short circuits using an externally mounted search coil sensor, eliminating the need for intrusive modifications and enabling fault detection independent of motor power. To account for variations in fault manifestation, the method was evaluated with three different relative positions between the search coil and the faulty winding. Feature extraction and selection are performed using a hybrid strategy combining random forest-based ranking and collinearity filtering, optimizing classification accuracy while reducing computational complexity. Two classification tasks were conducted: binary classification to differentiate between healthy and faulty motors, and multiclass classification to assess fault severity. The method achieved 100% accuracy in binary classification and 99.3% in multiclass classification using the full feature set. Feature reduction to eight attributes resulted in 92.4% and 85.4% accuracy, respectively, demonstrating a trade-off between performance and computational efficiency. The results support the feasibility of deploying stray flux-based fault detection in industrial applications, ensuring a balance between classification reliability, real-time processing, and potential implementation in embedded systems with limited computational resources. Full article

The transportation sector is a significant contributor to global carbon emissions, thus necessitating a transition toward renewable energy sources (RESs) and electric vehicles (EVs). Among EV technologies, fuel-cell EVs (FCEVs) offer distinct advantages in terms of refueling time and operational efficiency, thus rendering them a promising solution for sustainable transportation. Nevertheless, the integration of FCEVs in rural areas poses challenges due to the limited availability of refueling infrastructure and constraints in energy access. In order to address these challenges, this study proposes a multi-objective energy management model for a hydrogen refueling station (HRS) integrated with RESs, a battery storage system, an electrolyzer (EL), a fuel cell (FC), and a hydrogen tank, serving diverse FCEVs in rural areas. The model, formulated using mixed-integer linear programming (MILP), optimizes station operations to maximize both cost and load factor performance. Additionally, bi-directional trading with the power grid and hydrogen network enhances energy flexibility and grid stability, enabling a more resilient and self-sufficient energy system. To the best of the authors’ knowledge, this study is the first in the literature to present a multi-objective optimal management approach for grid-interactive, renewable-supported HRSs serving hydrogen-powered vehicles in rural areas. The simulation results demonstrate that RES integration improves economic feasibility by reducing costs and increasing financial gains, while maximizing the load factor enhances efficiency, cost-driven strategies that may impact stability. The impact of the EL on cost is more significant, while RES capacity has a relatively smaller effect on cost. However, its influence on the load factor is substantial. The optimization of RES-supported hydrogen production has been demonstrated to reduce external dependency, thereby enabling surplus trading and increasing financial gains to the tune of USD 587.83. Furthermore, the system enhances sustainability by eliminating gasoline consumption and significantly reducing carbon emissions, thus supporting the transition to a cleaner and more efficient transportation ecosystem. Full article

In the context of increasing external competition uncertainty and the growing maturity of digital information technology applications, digital transformation has become the crucial pathway for manufacturing enterprises to respond to market changes, enhance comprehensive competitiveness, and achieve sustainable development. In order to promote the effective implementation of the digital transformation strategy of manufacturing enterprises and enhance their export technological complexity, this paper, based on data from Chinese manufacturing listed companies and customs trade data, uses a multiple fixed effects model to explore the impact of digital transformation on the technological complexity of manufacturing exports. The results show that digital transformation significantly improves the export technological complexity of manufacturing enterprises, with innovation capability and production efficiency as the mediators. Further analysis of the research results reveals that supply chain integration and dynamic capabilities amplify these effects, exhibiting significant heterogeneity in terms of firm ownership, technological intensity, industry competition, geographic region, and stages of digital transformation. The research conclusions of this paper are of great significance for manufacturing enterprises to enhance their competitiveness in international markets and achieve sustainable development through digital transformation. However, its dependence on single-country data and fixed-period analysis limits its universality and applicability. These insights highlight the necessity of future research on the global applicability and long-term sustainability of digital transformation strategies in the manufacturing industry. Full article

Soybeans play a crucial role in global food security and international agricultural trade. As the world’s largest consumer and trader of soybeans, China faces significant external dependence on supply and concentration of import sources. In light of increasing uncertainties in the international political and economic landscape, risks within China’s soybean supply chain have become increasingly prominent, highlighting the need to explore the global soybean supply potential and optimize import strategies. In response to national food security strategic requirements and anticipated changes in global production capacity, the Global Agro-Ecological Zone (GAEZ) model and the multi-objective nonlinear programming model are used in this paper to estimate the potential of soybean yield increase globally. From dual perspectives of risk minimization and cost minimization, diversified soybean import schemes for China are proposed across three scopes: neighboring countries, countries along the Belt and Road Initiative (BRI), and globally. The results indicate that in the long term, the center of gravity for global soybean production capacity remains in the Americas; these areas, along with Europe and Africa, are key regions for China to expand its soybean import sources in the future. If all countries’ soybean production potentials are fully explored, China can achieve sufficient soybean supply by relying on neighboring countries as well as those countries participating in the BRI. Specifically, it is estimated that during the 2020s (2011–2040), the potential soybean production in the United States, Brazil, and Argentina could reach more than 290 million tons, 140 million tons, and 130 million tons, respectively, under scenarios of both yield increase and cultivated land expansion. Neighboring countries such as India and Russia also show significant potential, with India’s production potentially increasing by 42.8 million tons and Russia’s by 10.4 million tons. The results suggest that China can achieve a more balanced and secure import strategy by leveraging the production capabilities of countries in the Americas, Europe, and Africa while also fostering closer economic and agricultural cooperation with neighboring nations and BRI countries. Based on these findings, policy recommendations aimed at stabilizing and ensuring China’s soybean supply are discussed. Full article