Search Results (162)

Global supply chains are increasingly exposed to systemic disruptions driven by environmental pressures, geopolitical instability, and social unrest. Although Green Supply Chain Management (GSCM) is a strategic approach balancing sustainability and competitiveness, current research remains fragmented and regionally focused. Prior research has identified critical chokepoints and conceptualized disruption propagation through simulation and event system theory, yet few studies have operationalized large-scale empirical datasets to quantify cross-domain resilience. Addressing this gap, we collected and analyzed over 1.8 million news articles from more than 705 global portals spanning October 2023 to September 2025. Using GPT-based autonomous classification, approximately 67,434 disruption events directly related to GSCM were extracted and categorized by event type, geography, and significance. A system-of-systems framework was employed, linking seven domains: environment and climate, energy and resources, manufacturing and production, logistics and transportation, trade and commerce, agri-food systems, and labor and social systems. The results demonstrate that disruptions are unevenly distributed. The United States (8945 events), China (7822), and India (5311) emerged as global hubs, while Saudi Arabia acted as a single-domain chokepoint in energy. Energy and resources accounted for 22 percent of all events, followed by logistics (19 percent) and manufacturing (17 percent). Temporal analysis revealed major spikes in February 2024 (56,595 weighted intensity units) and June 2024 (10,861 units). Correlation analysis confirmed strong interdependencies across domains with average values greater than 0.7. This study contributes a globally scalable, data-driven framework to quantify disruption intensity, frequency, and interdependence in GSCM. It advances resilience research and offers actionable insights for policymakers and industry leaders. Full article

Background/Purpose: Reproductive health and fertility outcomes are essential but often overlooked aspects of occupational well-being. Organizational dysfunction, demanding workloads, and limited workplace accommodations may negatively affect fertility, while supportive policies and inclusive cultures can mitigate risks. This review aimed to map current evidence on these relationships and their economic consequences. Methodology/Approach: A scoping review was conducted using the PCC (Population–Concept–Context) framework. Systematic searches across multiple databases identified 30 eligible studies, including quantitative, qualitative, and mixed-method designs, spanning different sectors and international contexts. Findings: Four main domains emerged: shift work and circadian disruption, organizational stress and burnout, workplace flexibility and accommodations, and fertility-related policies and organizational support. Hazardous working conditions, long hours, and psychosocial stressors were consistently associated with impaired fertility, reduced fecundability, and pregnancy complications. Conversely, flexible scheduling, fertility benefits, and supportive organizational cultures were linked to improved well-being, retention, and productivity. Originality/Value: This review integrates evidence across occupational health, organizational psychology, and labor economics, offering a comprehensive overview of workplace influences on reproductive health. It highlights gaps in equity and representation—particularly for men, LGBTQ+ employees, and workers in precarious jobs—and calls for longitudinal, interdisciplinary, and intervention-based studies to inform effective workplace policies. Full article

Embodied energy flows link production systems with the energy sector, reflecting dependencies and structural risks under globalization and regional coordination. Guangdong, China’s most manufacturing-intensive, open, and energy-consuming province, is a central hub in both global value chains and domestic production networks, playing a pivotal role in national energy security. Understanding Guangdong’s embodied energy flows is essential for revealing the transmission of energy across multi-level spatial systems and the resilience of China’s energy infrastructure. This study integrates international (EXIOBASE) and Chinese inter-provincial input–output data to build a province-level nested global MRIO model, combined with Structural Path Analysis (SPA), to characterize Guangdong’s manufacturing embodied energy flows in domestic and international dual circulation from 2002 to 2017. Our findings confirm Guangdong’s pivotal bridging role in embodied energy transfers. First, flows are dual-directional and dominated by international transfers. Second, energy efficiency has improved, narrowing the intensity gap between export- and domestic-oriented industries. Third, flows have diversified spatially from concentration in developed regions toward developing regions, with domestic inter-provincial flows more dispersed. Finally, embodied energy remains highly concentrated across sectors, with leading industries shifting from labor- and capital-intensive to capital- and technology-intensive sectors. This research offers vital empirical evidence and policy reference for enhancing national energy security and optimizing spatial energy allocation. Full article

Rural livelihood transformation is increasingly vital for achieving agricultural modernization, reducing poverty, and promoting sustainable development in developing countries. Despite growing attention to land consolidation as a tool for improving agricultural resource allocation and productivity, its role in shaping rural livelihoods remains insufficiently understood. Addressing this gap, this study investigates the impacts of China’s High-Standard Farmland Construction (HFC), the country’s flagship land consolidation policy, on farmers’ livelihoods, focusing on both income level and income structure. Using provincial panel data from 30 regions, we adopt a continuous difference-in-differences design and mediation effect model to identify the causal effects of HFC. The results indicate that HFC significantly promotes total household income. Specifically, HFC facilitates mechanized agricultural production by consolidating fragmented plots, reducing production costs, and improving crop yields, thereby increasing agricultural income. Simultaneously, mechanization substitutes for labor and releases surplus workers, who often move to off-farm employment, diversifying income sources and stabilizing household livelihoods. Heterogeneity analysis reveals that the benefits of HFC are unevenly distributed. Low-income households, central provinces, and major grain-producing areas experience the greatest gains, and moderate-scale implementation proves more effective than either small- or excessively large-scale projects. This study highlights mechanization as a key mechanism linking land consolidation to rural livelihood transformation. The findings demonstrate that well-planned and efficiently implemented HFC policies can not only enhance agricultural productivity but also foster diversified and inclusive rural livelihoods. Full article

Persistent regional imbalances and widening urban–rural income gaps hinder progress toward Sustainable Development Goal 10 (Reduced Inequalities). In response, China has implemented a typical regional cooperation program—East–West Cooperation (EWC). Using a balanced panel of 642 western counties from 2013 to 2020 and the staggered difference-in-differences (DIDs) model, we assess the impact of EWC on the urban–rural income gap. We show that EWC narrows the urban–rural income gap, primarily by increasing rural incomes rather than changing urban incomes. Mechanism analyses indicate that expanded rural employment and higher agricultural production efficiency are the principal channels. The greater the economic disparity and the shorter the distance between paired counties, the stronger the effect of EWC. This effect is particularly pronounced in southwestern assisted counties and in agriculture-intensive assisted counties. The above evidence suggests that horizontal regional cooperation can deliver equity-enhancing growth. Policy should prioritize rural-first resource allocation, employment-oriented labor cooperation, and agricultural upgrading, while refining pairing rules to account for the magnitude of economic gaps and geographic proximity. Full article

Pruning is labor-intensive and increases production costs, while mechanical pruning offers a promising alternative. However, research on its effectiveness remains limited. To address this gap, we evaluated the effects of mechanical pruning over two consecutive years (2023 and 2024) on tree growth, yield, and fruit quality of ‘Arisoo’ apple trees. The treatment included hand (manual) pruning (HP), mechanical pruning (MP), and combined mechanical and hand pruning (MP + HP) applied during winter pruning in a super-spindle-slender-shaped apple orchard. MP significantly reduced pruning time; however, the amount of plant biomass removed was lower in the MP treatment than in the HP and MP + HP treatments. Canopy volume was higher in the HP treatment than in MP and MP + HP treatments; however, the pruning treatments did not affect trunk cross-sectional area or tree yield. Leaf chlorophyll and nitrogen contents were slightly lower in the MP treatment than in the HP treatment in 2023 but were not affected in 2024. The MP treatment also noticeably reduced light penetration within the canopy and produced smaller fruits with lower soluble solids content and poorer coloration at harvest compared to the HP and MP + HP treatments. In contrast, the HP and MP + HP treatments showed similar effects on light penetration, yield, fruit size, and fruit quality; however, the MP + HP treatment significantly reduced the pruning time compared with the HP treatment. Overall, this study found that MP reduced light penetration and produced smaller and poorly colored fruits, whereas a follow-up combination of HP after MP improved pruning efficiency, light penetration, fruit size, and fruit quality. Full article

Typical fertilizer applicators are often restricted in performance due to non-uniformity in distribution, required labor and time intensiveness, high discharge rate, chemical input wastage, and fostering weed proliferation. To address this gap in production agriculture, an automated variable-rate fertilizer applicator was developed for the cotton crop that is based on deep learning-initiated electronic control unit (ECU). The applicator comprises (a) plant recognition unit (PRU) to capture and predict presence (or absence) of cotton plants using the YOLOv7 recognition model deployed on-board Raspberry Pi microprocessor (Wale, UK), and relay decision to a microcontroller; (b) an ECU to control stepper motor of fertilizer metering unit as per received cotton-detection signal from the PRU; and (c) fertilizer metering unit that delivers precisely metered granular fertilizer to the targeted cotton plant when corresponding stepper motor is triggered by the microcontroller. The trials were conducted in the laboratory on a custom testbed using artificial cotton plants, with the camera positioned 0.21 m ahead of the discharge tube and 16 cm above the plants. The system was evaluated at forward speeds ranging from 0.2 to 1.0 km/h under lighting levels of 3000, 5000, and 7000 lux to simulate varying illumination conditions in the field. Precision, recall, F1-score, and mAP of the plant recognition model were determined as 1.00 at 0.669 confidence, 0.97 at 0.000 confidence, 0.87 at 0.151 confidence, and 0.906 at 0.5 confidence, respectively. The mean absolute percent error (MAPE) of 6.15% and 9.1%, and mean absolute deviation (MAD) of 0.81 g/plant and 1.20 g/plant, on application of urea and Diammonium Phosphate (DAP), were observed, respectively. The statistical analysis showed no significant effect of the forward speed of the conveying system on fertilizer application rate (p > 0.05), thereby offering a uniform application throughout, independent of the forward speed. The developed fertilizer applicator enhances precision in site-specific applications, minimizes fertilizer wastage, and reduces labor requirements. Eventually, this fertilizer applicator placed the fertilizer near targeted plants as per the recommended dosage. Full article

Artificial Intelligence (AI) is widely recognized as a force that will fundamentally transform traditional chicken farming models. It can reduce labor costs while ensuring welfare and at the same time increase output and quality. However, the breadth of AI’s contribution to chicken farming has not been systematically quantified on a large scale; few people know how far current AI has actually progressed or how it will improve chicken farming to enhance the sector’s sustainability. Therefore, taking “AI + sustainable chicken farming” as the theme, this study retrieved 254 research papers for a comprehensive descriptive analysis from the Web of Science (May 2003 to March 2025) and analyzed AI’s contribution to the sustainable in recent years. Results show that: In the welfare dimension, AI primarily targets disease surveillance, behavior monitoring, stress detection, and health scoring, enabling earlier, less-invasive interventions and more stable, longer productive lifespans. In economic dimension, tools such as automated counting, vision-based weighing, and precision feeding improve labor productivity and feed use while enhancing product quality. In the environmental dimension, AI supports odor prediction, ventilation monitoring, and control strategies that lower emissions and energy use, reducing farms’ environmental footprint. However, large-scale adoption remains constrained by the lack of open and interoperable model and data standards, the compute and reliability burden of continuous multi-sensor monitoring, the gap between AI-based detection and fully automated control, and economic hurdles such as high upfront costs, unclear long-term returns, and limited farmer acceptance, particularly in resource-constrained settings. Environmental applications are also underrepresented because research has been overly vision-centric while audio and IoT sensing receive less attention. Looking ahead, AI development should prioritize solutions that are low cost, robust, animal friendly, and transparent in their benefits so that return on investment is visible in practice, supported by open benchmarks and standards, edge-first deployment, and staged cost–benefit pilots. Technically, integrating video, audio, and environmental sensors into a perception–cognition–action loop and updating policies through online learning can enable full-process adaptive management that improves welfare, enhances resource efficiency, reduces emissions, and increases adoption across diverse production contexts. Full article

Understanding the diversity of avocado production systems is crucial for developing effective agricultural policies and extension strategies. This study examined the Colombian avocado variety “Lorena” in the Colombian lowlands of Casanare through spatial typology analysis to inform sustainable agricultural development strategies. We employed spatial autoregressive modeling and clustering techniques to analyze data from 45 production systems, revealing heterogeneity despite small-scale operations with productivity (2.9 ton ha⁻¹) below regional (8 ton ha⁻¹) and national averages (11.03 ton ha⁻¹). Five distinct typologies emerged: transitional traditional (n = 15), intensive technical management (n = 4), experience-based traditional (n = 5), balanced management (n = 10), and comprehensive technical systems (n = 11). In contrast to conventional assumptions about economies of scale, productivity was not primarily determined by farm size, as smaller intensive technical management systems achieved the highest yields (3375 kg) despite having the smallest size (162.50 trees), followed by experience-based traditional systems (3280 kg). The spatial autoregressive model effectively captured spatial dependence in yield patterns, demonstrating the importance of geographic context in agricultural system analysis. Technology/practice adoption patterns varied markedly, with high adoption of established practices (>90%) but low foliar analysis adoption (17.78%). High organic fertilization adoption (93.33%) reflected a commitment to environmental sustainability but may partially explain productivity gaps, highlighting trade-offs between sustainability and short-term yield optimization. Socioeconomic analysis revealed characteristics of part-time farming systems, with 91.11% of producers having additional income sources and 95.56% using hired labor, suggesting evolved livelihood strategies that may enhance resilience. These findings challenge one-size-fits-all development approaches and demonstrate the need for tailored, spatially targeted interventions that account for specific production system characteristics, multiple pathways to sustainable intensification, and the complex interactions between productivity, sustainability, and socioeconomic factors in smallholder agriculture. Full article

Occupational environments often expose workers to physical and psychological stressors that compromise well-being and productivity. While biophilic design has gained attention, there remains limited systematic integration of Nature-Based Solutions (NbS) within workplace management frameworks. This review aims to map the empirical impacts of NbSs on occupational health, productivity, and environmental quality, and to identify key barriers and facilitators for their integration into comprehensive Quality, Environmental, Health, and Safety (QEHS) management systems. A scoping literature review was conducted in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) guidelines. A comprehensive search was performed in the Scopus and Web of Science databases for studies published between 2019 and 2024. A total of 2452 records were initially retrieved, with 39 studies retained for synthesis following screening, eligibility assessment, and critical appraisal using the Joanna Briggs Institute checklist. Findings indicate that NbSs can reduce stress, improve physical and cognitive health, and enhance workplace productivity. Reported benefits include reduced absenteeism, improved indoor air quality, and measurable financial returns. However, significant challenges persist, including high upfront costs, ongoing maintenance demands, a shortage of specialized labor, and methodological heterogeneity across studies. In particular, hybrid approaches combining physical natural elements and immersive technologies such as virtual reality emerged as promising alternatives for spatially constrained environments. Participatory co-design and stakeholder engagement were also identified as critical success factors for effective implementation. Integrating NbSs into QEHS frameworks has the potential to foster healthier, more resilient, and sustainable workplaces. Alignment with recognized certifications can further support systematic adoption and monitoring. Future research should prioritize longitudinal designs, standardized outcome metrics, and physiological markers, while addressing geographical gaps through studies in underrepresented regions. Embedding participatory processes and certification alignment can enhance stakeholder buy-in and practical scalability, advancing the integration of NbSs into holistic workplace management strategies. Full article

This study examines transparent concrete (TC) utilizing bibliometric analysis of articles from the Scopus database to identify its performance, knowledge gaps, limitations, and applications. TC is a new type of sustainable building material that combines optical fibers with concrete and is lighter in weight than traditional concrete. Incorporating optical fibers in concrete enables light transmission, thereby reducing the need for artificial lighting in TC structures. TC is also referred to as light-transmitting concrete due to its unique properties. By utilizing natural light resources instead of electric lighting, buildings can better harness sunlight, providing both architectural beauty and energy savings. This approach decreases reliance on non-renewable resources and ultimately conserves energy. Scholars have focused a lot of attention on the superb light transmission and decorative appeal of TC. However, its applications in the construction sector have yet to gain traction due to the time-consuming production process, high labor costs, and limited studies on its durability and mechanical properties. This article reviews the applications, production processes, types of TC, bibliometric analysis, cost analysis, and the research findings related to mechanical, thermal, energy-saving, light-transmitting, and durability properties. TC showed a substantial decrease in the building’s total energy use and maintained strength comparable to conventional concrete. It also displayed minimal water resistance, porosity, and density, making it suitable for constructing buildings and lightweight road surfaces. Additionally, it offers notable aesthetic value. The study identifies gaps in durability and standardization while highlighting significant developments in TC’s mechanical behavior, thermal and energy performance, and applications. Furthermore, it summarizes the future research paths for TC, which are likely to enhance its implementation as a promising sustainable construction material. Full article

The upper reaches of the Yangtze River face the challenge of balancing livestock development and ecological protection. As a significant livestock production region in China, optimizing the livestock ecological efficiency (LEE) of Sichuan Province (SP) is of strategic importance for regional sustainable development. Livestock carbon emissions and related pollution indices were utilized as undesirable output indicators within the super-efficiency SBM model to measure SP’s LEE over the 2010–2022 period. Kernel density estimation was combined with the Theil index to analyze spatiotemporal variation characteristics. A STIRPAT model was constructed to explore the influencing factors of SP’s LEE, and a grey forecasting GM (1,1) model was employed for prediction. Key findings reveal the following: (1) LEE increased by 25.9%, with high-efficiency regions expanding from 19.0% to 57.1%; (2) regional disparities persist, driven by labor redundancy and environmental governance gaps; (3) per capita GDP, industrial agglomeration, and technology advancement significantly promoted efficiency, while government subsidies and carbon intensity suppressed it. Projections show LEE reaching 0.923 by 2035. Key recommendations include the following: (1) implementing region-specific strategies for resource optimization, (2) restructuring agricultural subsidies to incentivize emission reduction, and (3) promoting cross-regional technology diffusion. These provide actionable pathways for sustainable livestock management in ecologically fragile zones. Full article

Innovation-driven labor markets play a pivotal role in economic development, yet significant disparities exist in how efficiently countries transform innovation inputs into labor market outcomes. This study addresses the critical gap in benchmarking multi-stage innovation efficiency by developing an integrated framework combining Data Envelopment Analysis (DEA) Super Slack-Based Measure (Super-SBM) for static efficiency evaluation and the Malmquist Productivity Index (MPI) for dynamic productivity decomposition, enhanced with cooperative game theory for robustness testing. Focusing on the top 20 innovative economies over a 5-year period, we analyze key inputs (Innovation Index, GDP, trade openness) and outputs (labor force, unemployment rates), revealing stark efficiency contrasts: China, Luxembourg, and the U.S. demonstrate optimal performance (mean scores > 1.9), while Singapore and the Netherlands show significant underutilization (scores < 0.4). Our results identify a critical productivity shift period (average MPI = 1.325) driven primarily by technological advancements. This study contributes a replicable, data-driven model for cross-domain efficiency assessment and provides empirical evidence for policymakers to optimize innovation-labor market conversion. The methodological framework offers scalable applications for future research in computational economics and productivity analysis. Full article

Despite the growing relevance of subnational development strategies in emerging economies, the literature lacks empirical applications of classical trade models to territorial productive specialization. This study addresses this gap by adapting the Heckscher–Ohlin–Samuelson (HOS) model to identify optimal specialization patterns in intermediate municipalities with asymmetric factor endowments. Using data from 2017 to 2021 for Tunja and Chiquinquirá (Colombia), we estimate capital-to-labor ratios and sectoral factor intensities to detect specialization aligned with local comparative advantages. The results show that Tunja exhibits capital-abundant conditions favoring specialization in sectors such as real estate, construction, and financial services, while Chiquinquirá demonstrates labor-intensive dynamics suitable for tourism and service industries. Methodologically, the study extends the HOS model to subnational scales, offering a robust analytical tool for regional policy formulation. This article contributes to the academic debate by bridging international trade theory and regional development, and it provides empirical evidence to support place-based industrial policies. Our findings emphasize the importance of aligning productive strategies with structural endowments to foster inclusive and sustainable development in emerging territories. Full article

The global transition towards a low-carbon energy system urgently demands efficient and safe energy storage solutions. Aqueous zinc-ion batteries (AZIBs) are considered a promising alternative to lithium-ion batteries due to their inherent safety and environmental friendliness. However, conventional manufacturing methods are costly and labor-intensive, hindering their large-scale production. Recent advances in 3D printing technology offer innovative pathways to address these challenges. By combining design flexibility with material optimization, 3D printing holds the potential to enhance battery performance and enable customized structures. This review systematically examines the application of 3D printing technology in fabricating key AZIB components, including electrodes, electrolytes, and integrated battery designs. We critically compare the advantages and disadvantages of different 3D printing techniques for these components, discuss the potential and mechanisms by which 3D-printed structures enhance ion transport and electrochemical stability, highlight critical existing scientific questions and research gaps, and explore potential strategies for optimizing the manufacturing process. Full article