Search Results (1,129)

Promoting resilience, increasing productivity and sustainability, and profit maximization remain key challenges facing farmers globally. These are exacerbated by factors such as climate change, low to no access to technological advancement, financial constraints, poor technical and management skills, inadequate government support, and limited access to resources. However, there are diverse strategies that abound, including on-farm diversification, that farmers could leverage on to address these numerous and complex challenges. This study investigated the determinants of on-farm diversification strategies among smallholders in Mpumalanga Province. The study employed a quantitative approach using closed-ended survey questionnaires to elicit information from a total of 465 farmers who were randomly sampled from a total population of 14,411. The data gathered were analysed using descriptive statistics to determine the on-farm diversification strategies employed by farmers and the factors influencing the use of these strategies. A binary logistic regression model was employed to establish the relationship between on-farm diversification strategies and the determining factors. More than half of the farmers were female (51.8%), with only 48.2% male. The majority (59.1%) of the farmers were between the ages of 36 and 60, with only 20.2% youth participation in farming. Slightly more than half (50.8%) of the farmers practise mixed farming as their on-farm diversification strategy, while only 4.3% of the farmers practise mono-cropping. The study identified significant variables such as level of education (p = 0.001), secondary source of income (p = 0.057), farmland size (p = 0.022), number of farm assistants (p = 0.016), and on-farm diversification awareness as key determinants of on-farm diversification among smallholder farmers in Mpumalanga Province. Therefore, it is recommended that policies within the agricultural sector be revised to encourage on-farm diversification in order to motivate farmers to transition to agripreneurship for poverty alleviation, food security and rural economic development (RED). Full article

This study examines whether size-contingent employment regulations are associated with distortions in firm size distribution and whether such patterns are more consistent with productivity-based selection or with broader constraints on firm scaling. Using 2024 census data covering 86,758 formal firms in Ecuador, we combine bunching analysis, regression discontinuity design (RDD), and logistic regression to analyze firm responses at the 10- and 50-employee thresholds. We document significant bunching below the 50-employee threshold, consistent with an economically meaningful implicit regulatory tax on firm scaling. However, RDD estimates reveal no productivity discontinuity at the cutoff, indicating that the observed threshold effects are more consistent with broad-based scaling constraints than with selective filtering of low-productivity firms. Sectoral analyses show a consistent pattern of bunching across technologically diverse industries, supporting an institutional rather than technological interpretation. Conditional on threshold proximity, firm crossing is more strongly associated with sales growth than with productivity advantages. By distinguishing between compositional avoidance and local crossing, the study sheds new light on the puzzle of absent productivity selection. These findings provide the first rigorous evidence of regulatory threshold effects in Ecuador and show how size-based regulations may distort firm scaling and contribute to allocative inefficiencies. Full article

Although digital economic development is often viewed as a catalyst for green transformation, the causal implications of policy-driven AI deployment for low-carbon logistics development remain unclear. To address this gap, this study leverages China’s National New Generation Artificial Intelligence Innovation Development Pilot Zones (AIIDPZs) as a quasi-natural experiment. Using panel data from 30 provincial regions from 2012 to 2022, this research employs a double machine learning framework to rigorously quantify the AIIDPZ policy’s causal effects on the logistics industry’s green total factor productivity (GTFP). We further examine underlying transmission mechanisms and spatial spillover effects. Results show that the AIIDPZ policy significantly enhances logistics GTFP, a finding robust to parallel trend tests, sample adjustments, and algorithm substitutions. Mechanism analysis reveals that the AIIDPZ policy promotes logistics GTFP by alleviating manufacturing agglomeration and collaborative agglomeration. This occurs mainly through the mitigation of environmental externalities and the easing of inter-sectoral resource competition. Heterogeneity analysis highlights substantial regional variation: the policy impact is strongest in East China, Central China, and Southwest China; positive but weaker in Northeast and Northwest China; and statistically insignificant in North and South China. Spatial econometric results confirm significant positive spillovers to neighboring regions. Temporally, the logistics industry’s GTFP shows a sustained upward trajectory, while spatially it follows a spatial pattern of “Eastern leadership, Central rise, and Western catch-up.” Robust empirical evidence is presented to evaluate the environmental outcomes of AI policy implementation, alongside policy-relevant insights for advancing coordinated and spatially differentiated regional development. Full article

The electric vehicle revolution has created an urgent need for lithium-ion battery (LIB) recycling, with projections exceeding 11 million tons of end-of-life batteries annually by 2030. However, progress toward a circular economy remains fragmented. This perspective article introduces the concept of a ‘Recycling Trilemma,’ arguing that technological advancements in material separation are systematically undermined by economic volatility and regulatory fragmentation. While current literature focuses on isolated domains—chemistry, business models, or policy—this work provides a systems-level synthesis. By analyzing the friction points between material science (e.g., binder removal, impurity sensitivity), economic realities (e.g., logistics costs, LFP profitability), and regulatory frameworks (e.g., EU vs. US divergence), we propose that true circularity requires synchronized design-for-recycling, market stabilization mechanisms, and harmonized digital product passports. The paper concludes that overcoming the trilemma demands a shift from isolated fixes to integrated, cross-sectoral coordination. Full article

Small-scale prosumer installations are playing an increasingly important role in the Polish electricity sector. These primarily include photovoltaic systems and heat pumps installed for internal use. Noticeable losses for individual investors, generated by the power flow mechanism during peak production hours (connection to the grid) and peak demand (drawback from the grid), as well as the issue of fluctuating grid capacity and the observed redispatch procedures for photovoltaic installations, are driving increased interest in equipping home energy installations with energy storage systems, strengthening the aspect of sustainable energy development in this dimension. The impact of energy storage on investment motivation and the actual effects of incorporating it into home energy installations have not yet been sufficiently researched, particularly in Poland. Therefore, the aim of the study was to assess the use of energy storage in home installations as a socio-technical direction of power development at the micro level, in light of the constantly increasing energy demand observed worldwide in line with the challenges of sustainable development. The results of a survey of 206 individual users of power installations equipped with energy storage systems in Poland were used for this study. The research was qualitative and quantitative in nature, with descriptive statistics and a logistic regression model used in the in-depth section, and the findings were supported by PQStat software. The research revealed that the selection of energy storage systems in home power grids is related to the potential for prosumer optimization. On the other hand, they are seen as a path towards increasing energy security at the household level. Supporting this direction of installation development at the micro level is a justified concept for the development of green energy in Poland, socially and environmentally beneficial as well as economically justified, i.e., in line with the trend of sustainable development. The information campaign, combined with financial support for this type of investment, should be continued and strengthened in Poland. Full article

This study examines associations between socio-demographic characteristics and online shopping risk determinants of consumers in Bulgaria. It focuses on nine risk determinants grouped into four domains—technological, logistical, legal and geographical, and other risks. The analysis is based on aggregated official data from Eurostat and the National Statistical Institute of Bulgaria. The methodological framework employs a correlational approach using non-parametric correlation coefficients. The empirical results reveal statistically significant associations of varying strength. Employment status demonstrates the strongest associations among the socio-demographic variables, while gender, educational level, and age exhibit relatively weaker associations. These findings provide actionable insights for evidence-based strategies to mitigate online shopping risk determinants and support policies and initiatives to enhance consumer protection and engagement in Bulgaria’s e-commerce sector. Full article

At present, the global logistics sector faces critical challenges, including rising energy costs and pressure to reduce CO₂ emissions. Traditional linear supply chains are becoming inefficient, necessitating a transition toward shared logistics based on the principles of the sharing economy. This paper presents a progressive three-layer architecture that transforms conventional reactive data collection into an autonomous, proactive management system for the distribution of consumable materials. While previous research established foundations in IoT connectivity for smart vending machines, this study advances the process by integrating an intelligent layer of artificial intelligence (AI) algorithms. The framework utilizes Long Short-Term Memory (LSTM) neural networks for demand forecasting, dynamic route optimization (VRP/ACO) for replenishment, and Isolation Forest/DBSCAN algorithms for real-time anomaly detection. To evaluate the framework, a numerical simulation was conducted using representative pilot scenarios. The results indicate that within the simulated environment, the system achieves over 95% accuracy in inventory depletion prediction (MAPE = 4.02%). In these analyzed instances, this leads to a 25–30% reduction in stock-out risks and a 25% reduction in replenishment distance. These findings demonstrate the significant potential for reducing operational costs and carbon footprints in green logistics. The study confirms that the synergy between IoT infrastructure and AI-driven analysis provides a robust foundation for transitioning from static methodologies to resilient, collaborative logistics ecosystems. Full article

Background: This study examines the competitive dynamics of the artisanal wooden furniture industry in Misantla, Veracruz, Mexico, a predominantly informal productive system characterized by family-managed production units and strong territorial embeddedness. Methods: A mixed-methods research design was employed. Quantitative data were collected from 187 family-managed production units (86 woodworking units and 101 workshops) using a structured questionnaire based on five-level Likert scales assessing external efficiency, collective efficiency, and innovation. Statistical analyses included descriptive measures and chi-square tests to examine associations between competitiveness and collective strategies, while qualitative validation and thematic interpretation based on expert assessments were used to contextualize sectoral practices and structural constraints. Results: The findings indicate a low overall competitiveness score (1.92/5), associated with informal practices, limited technical training, and weak supply chain integration. Despite these constraints, the sector maintains a strong cultural identity and contributes to its local economy. Conclusions: Artisanal supply chains can achieve functional levels of logistics performance through internal coordination dynamics. Strengthening collaboration mechanisms is a viable strategy for improving logistics performance in artisanal manufacturing systems in emerging economies. These findings provide empirical evidence to support the design of collaborative strategies that integrate traditional craftsmanship with modern supply chain practices in artisanal micro-industries. Full article

Background: The COVID-19 pandemic has underscored the critical importance of supply chain resilience. However, little is known about firms’ willingness to implement logistics and supply chain resilience strategies (SCRESTs), and how this willingness varies across contexts. This study investigates the willingness of Japanese manufacturing firms to implement SCRESTs and examines how the pandemic has influenced this willingness. Methods: Using survey data from 549 Japanese manufacturing firms collected from March to April 2022, we employed binary choice models and the average treatment effect on the treated (ATET) analysis to examine the factors influencing the willingness to implement SCRESTs before and during/after the pandemic. Results: Firms demonstrated significantly higher willingness to implement SCRESTs during/after the pandemic compared with before. Company size, industry sector, logistics strategy, implementation obstacles, and past SCREST implementation significantly influenced willingness across both periods. The ATET analysis confirmed that past SCREST implementation positively affects future willingness. Conclusions: The pandemic served as a catalyst for enhanced supply chain resilience awareness among Japanese manufacturers. Sector-specific interventions addressing both informational and structural barriers are essential to sustain and strengthen the willingness to implement SCRESTs, particularly in strategically important sectors where financial incentives alone may prove insufficient. Full article

The offshore wind energy sector faces significant logistics costs due to complex maritime environments. This study addresses the multi-period Crew Transfer Vessel routing within offshore wind farms and scheduling problems through a novel mixed-integer linear programming framework. The model integrates personnel inventory management with dynamic service times. It determines optimal routing and scheduling plans to minimise total operational costs. Numerical experiments demonstrate the effectiveness of the approach. The results indicate that increasing vessel capacity from 8 to 20 reduces total expenses by approximately 80%. Moreover, shifting from single-trip to multi-trip operations decreases fixed charter costs by 30%. The computational performance is efficient, and the solver achieves optimal solutions within an average of 5.67 s. This framework provides operators with precise decision support for complex offshore wind farm maintenance scenarios. Full article

The growing importance of sustainability in the financial sector increases the need to analyse the green image of banks. However, research to date mainly focuses on large commercial banks, while cooperative banks, despite their local nature of operation and strong social ties, remain relatively poorly recognised empirically, especially regionally and in terms of young consumers’ perceptions. The aim of this article is to assess the level of the green image of cooperative banks and to identify selected socio-demographic and economic, psychological, and behavioural determinants that determine its perception by young customers in a selected region of Poland. Empirical research was conducted using a survey method (questionnaire interview, n = 256) in 2024. The green image was operationalised as a synthetic indicator, including an assessment of cooperative banks’ environmental responsibility activities, environmental initiatives, and sustainability communication, measured on a scale of 1–7. Student’s t-test, analysis of variance and a logistic regression model were used in the data analysis. The results indicate that the green image of cooperative banks in the study group is neutral (M = 4.34). Statistically significant differences were found depending on selected characteristics of the respondents. The results suggest the need to segment communication activities in the area of sustainability and to adapt the image-building strategy to the profile of young customers. Full article

Urban depopulation has become an increasingly visible phenomenon worldwide, affecting cities of different sizes and economic structures. This article develops a typology of U.S. depopulating cities beyond the Rust Belt’s iconic industrial cities, which dominate academic literature, to include a wider range of shrinking settlements in the shadows. The analysis is based on a dataset of U.S. census places constructed from decennial census population data (1990–2020) combined with employment structure indicators and spatial classification variables identifying metropolitan position and industrial specialization. Using 1990–2020 population change and three explanatory dimensions—city size, industrial heritage, and peripheral location—the analysis identified 1082 places that lost at least 10% of their population. Logistic regression showed manufacturing and mining reliance, small size, and remoteness as significant predictors of depopulation. Based on these factors, settlements are divided into seven types, from large urban centers to small peripheral towns with fewer than 5000 people. The overwhelming predominance of small towns (97%) in the sample highlights their distinct development challenges and questions the narrative of decline focused solely on larger industrial cities. By situating American trajectories within the broader shrinking cities discourse, the findings demonstrate the value of typology as a methodological tool for identifying intra-group heterogeneity, capturing regional differences, and establishing a more reliable basis for comparative urban studies. Ultimately, the study shows that urban decline in the United States is not exclusively a Rust Belt phenomenon, but a multidimensional process encompassing different scales, sectors, and geographies. Full article

Digital public infrastructure is increasingly viewed as a catalyst for rural and agricultural transformation, yet its structural impact on agricultural modernization remains insufficiently examined. Using the Broadband China policy as a quasi-natural experiment, this study applies a difference-in-differences framework to city-level panel data to identify the causal effect of digital public infrastructure on agricultural modernization. The estimates indicate that digital infrastructure significantly advances agricultural modernization. Further analysis shows that the effect operates through enhanced technological innovation and strengthened economic agglomeration, suggesting that digital connectivity reshapes both productivity and spatial organization within the agricultural sector. The impact is more pronounced in regions with developed logistics systems and lower information frictions, underscoring the importance of complementary infrastructure and institutional conditions. By linking digital public infrastructure to structural agricultural transformation, this study extends the literature on digital development and provides policy insights for developing economies pursuing infrastructure-driven modernization strategies. Full article

Bio-CO₂ is part of the natural carbon cycle and represents a sustainable carbon source for the production of Renewable Fuels of Non-Biological Origin (RFNBOs), such as synthetic methanol. This study addresses the critical knowledge gap in aligning diverse biogenic CO₂ sources with e-methanol requirements in the EU by providing harmonized mapping, based on datasets, literature sources, and reported industrial statistics at the sectoral and country level. Bio-CO₂ streams from biogas and biogas upgrading, biomass combustion, pulp and paper, bioethanol production, and the food and beverage sector are evaluated for total emissions, CO₂ concentrations and purity, the geographical distribution, seasonality, and impurity profiles. Results show that approximately 350 Mtpa of bio-CO₂ are emitted across the EU, with highly heterogeneous characteristics. Biogas upgrading and fermentation-based processes generate highly pure CO₂ streams (>98–99%), yet their small and dispersed nature complicates logistics. In contrast, biomass-combustion and pulp and paper sectors provide large volumes (around 214.6–298.2 Mtpa and 73.9 Mtpa CO₂, respectively), but in diluted streams (typically 3–15% and 10–20%). Replacing just 10% of the EU maritime fuel demand with e-methanol would require 53.6 Mtpa of bio-CO₂ and 58 GW of electrolyzer capacity, a stark contrast to the current operational 385 MW. The findings highlight the need for infrastructure planning and aggregation hubs to enable the large-scale deployment of RFNBO methanol in the maritime sector. Full article

Despite the rapid expansion of AI and machine-learning techniques in bankruptcy prediction, classical statistical methods such as discriminant analysis and logistic regression remain relevant because of their transparency and interpretability. These characteristics are crucial for stakeholders who require understandable decision-making tools, especially in NACE Rev. 2 Section I—Accommodation and Food Service Activities, a sector characterized by high operating leverage, vulnerability to economic shocks, and strong macroeconomic importance. The study aims to evaluate and compare the predictive performance of discriminant analysis and logistic regression for bankruptcy prediction and to identify key predictors that can serve as managerial early-warning signals for companies in crisis across B9 countries. The sample of 4395 companies was used. The classification ability of all models is assessed using multiple performance metrics, including overall accuracy, sensitivity, specificity, precision, the F1-score, the F2-score, the Matthews correlation coefficient, and the area under the receiver operating characteristic curve. The results show that both approaches achieve consistently high predictive performance, with all major metrics exceeding 0.92 on the test sample of prosperous and non-prosperous enterprises. Six significant bankruptcy predictors are identified for each method, with three common indicators: financial leverage, total liabilities to assets, and return on costs. The comparative analysis results in a methodological “draw,” confirming comparable predictive power. These findings reaffirm the relevance of classical prediction models and identify key financial indicators that can be used as practical early-warning signals by managers in the sector. Full article