Search Results (226)

Rwanda’s sustainable green economy agenda has placed irrigation and input-support schemes at the centre of efforts to raise tropical fruit productivity in the Mugesera region, yet the econometric analysis presented, in a Cobb–Douglas production function estimated by panel OLS and a Nerlove partial-adjustment model of agricultural supply response required the analyst to pre-specify a log-linear, constant-elasticity functional form. This paper asks whether machine learning methods that learn input–output relationships directly from data can cross-check those findings, and presents a documented Python version 3.10.12 pipeline for doing so. Using a synthetic panel dataset (six regions, 2009–2018, N = 346 farm-year observations) calibrated to match the elasticity magnitudes reported in the original Cobb–Douglas estimation, three models—multiple linear regression, random forest, and gradient boosting—are trained on land, fertilizer, seed, lagged price, and lagged output features, and are evaluated on held-out data. All three models explain a substantial share of variation in output (R² between 0.72 and 0.75 under a random split; for results under panel-aware validation), with tuned gradient boosting achieving the best accuracy under the random split (R² = 0.751, RMSE = 0.268). Permutation feature importance recovers the same relative ranking of input variables as the original regression—fertilizer, then seeds, then land area—without the model being given the Cobb–Douglas functional form in advance. Because the synthetic data were explicitly generated to reproduce these elasticities, this recovery demonstrates that the modelling pipeline behaves as intended; it is a calibrated methodological demonstration and not an independent empirical validation of the original findings. The results are consistent with lagged price and output operating mainly through farmers’ input decisions rather than as direct predictors of output once input quantities are known, although we present this as a hypothesis for future testing rather than a demonstrated finding. Re-running the identical pipeline on real Mugesera farm-level survey microdata is the necessary next step before these results can be treated as genuine empirical findings; this caveat applies to every quantitative result reported below, not only to the conclusion. Full article

Many coastal regions endowed with rich marine biodiversity fail to translate these resources into high-value pharmaceutical products—a “valorization gap” that obstructs progress toward SDG 14 (Life Below Water), SDG 3 (Good Health and Well-Being), and SDG 9 (Industry, Innovation and Infrastructure). This study investigates the governance barriers underlying this gap using China’s Marine Traditional Chinese Medicine (MTCM) industry as an empirical case. Guided by an integrated analytical framework synthesizing innovation systems theory, Triple Helix dynamics, global value chain analysis, and organizational ambidexterity, the study draws on semi-structured interviews with 16 stakeholders in Dalian and NVivo-based thematic analysis. Four interconnected barriers are identified: value chain lock-in at the food-product level, a fragmented innovation ecosystem with misaligned incentives, a translational “valley of death” caused by absent pilot-scale platforms and quality standardization deficits, and an underdeveloped industrial ecosystem trapped in a low-value equilibrium. Through benchmarking against Qingdao’s marine pharmaceutical model and international marine bio-pharmaceutical policies (Japan, Republic of Korea, EU), this study proposes a “Dual-Wheel Driving” sustainable governance framework that simultaneously pursues “stock upgrading” (standardization, sustainable sourcing, branding) and “increment cultivation” (strategic pharmaceutical R&D with green innovation principles), coordinated through a “one core, multiple nodes” institutional architecture. The findings offer transferable policy insights for similarly positioned coastal regions seeking to develop marine bio-pharmaceutical capabilities within a sustainable blue bioeconomy framework. Full article

The transition to a circular bioeconomy is increasingly recognised as a critical pathway for achieving climate neutrality and resource use efficiency in Ireland. However, this transition is highly dependent on the availability of a skilled workforce capable of operating across interconnected bio-based sectors. Ireland’s National Bioeconomy Action Plan recognises the critical role that skills and knowledge will play in this transition. This study develops a comprehensive taxonomy of skills required to support the transition to a circular bioeconomy, with a specific focus on key Irish primary bioeconomy sectors (agriculture, forestry, fishery and aquaculture) and traditional industries (food processing, wood, textiles, and construction). A qualitative systematic literature review was conducted following the PRISMA 2020 guidelines. Peer-reviewed articles were retrieved from the Web of Science and Scopus databases and complemented with relevant Irish and European grey literature. A total of 96 publications were analysed using thematic analysis to identify and synthesise sector-specific and cross-sectoral competencies. The taxonomy organises circular bioeconomy skills into five overarching competence domains (systems-thinking, sustainability, circularity and bioeconomy/biotechnology, and digital skills). Findings reveal that while each sector has unique skills, a set of shared skills is consistently critical across all sectors. The identified competencies were subsequently organised into a structured taxonomy and mapped to Ireland’s National Framework of Qualifications (NFQ), establishing progressive proficiency levels from awareness (NFQ 5–6) through practitioner (NFQ 7–8) to expert (NFQ 9–10). This competency progression framework represents the principal contribution of the study, translating the taxonomy into a practical tool for curriculum development, workforce planning, professional development, and policy implementation. Full article

Forest-product certification now operates at the intersection of private sustainability governance, market access, and mandatory due diligence. This structured narrative review asks how Forest Stewardship Council (FSC) and Programme for the Endorsement of Forest Certification (PEFC) certification, together with the EU Deforestation Regulation (EUDR), affect competitiveness and the distribution of compliance costs in Polish business-to-business and export-oriented wood-product supply chains. A documented revision-stage search and screening procedure produced an evidence base of 55 peer-reviewed, regulatory, statistical, and sectoral sources. The analytical framework combines private-governance theory, stakeholder conflict analysis, and relationship marketing to examine information asymmetry, bargaining power, cost allocation, and market access. The Polish case is characterised by a large publicly owned forest resource, extensive but overlapping FSC and PEFC coverage, and strongly export-oriented downstream industries. Certification can reduce buyer verification costs, support supplier qualification, improve traceability, and protect access to demanding markets. However, fixed audit, documentation, digitalisation, and certified-material costs are borne disproportionately by small and medium-sized enterprises, especially where lead buyers do not share adaptation costs. EUDR strengthens these asymmetries because certified status can support, but does not replace, legal due diligence. The review contributes a governance-based explanation of why the same sustainability requirements can generate resilience and market access for digitally mature exporters while creating entry barriers, supplier exclusion, and concentration risks for smaller firms. Policy support should therefore combine clear demand signals, group certification, shared traceability infrastructure, advisory services, and buyer–supplier cost-sharing arrangements. Full article

This study investigates the structural transformation of regional livestock production systems toward an integrated Circular Bioeconomy (CBE) framework. Using an integrated Bayesian Tree–Spline approach, spatially disaggregated regional data were analyzed to evaluate the environmental, technological, and logistical determinants of sustainable livestock production. The empirical model incorporated key indicators, including infrastructure readiness, market accessibility, manure intensity, biogas conversion rates, methane emissions, alternative feed utilization, and transport-related emissions. The probabilistic classification results revealed substantial spatial heterogeneity among the analysed regions, enabling the identification of areas with high circular bioeconomy implementation potential, transitional regions, and areas requiring further technological and infrastructural development. Shannon entropy measures were applied to quantify classification uncertainty and identify transitional zones, while penalized B-splines captured non-linear saturation effects associated with alternative feed utilization and the integration of bioenergy technologies. The findings provide a data-driven basis for supporting the implementation of circular bioeconomy principles, enhancing resource-use efficiency, reducing greenhouse gas emissions, and facilitating the transition toward climate-neutral livestock production systems. Compared with the deterministic benchmark model, the proposed Bayesian Tree–Spline framework additionally quantifies classification uncertainty, captures nonlinear threshold and saturation effects, and provides probabilistic regional typologies that support more robust and evidence-based policy recommendations. Full article

Camellia osmantha is an economically valuable woody oil plant with increasing cultivation in southern China, yet its potential distribution under climate change remains poorly understood. Here, we used an optimized MaxEnt model with 27 occurrence points and 11 environmental variables (retained from 27 candidate predictors via correlation filtering and MaxEnt-based contribution and permutation importance) to predict its potential distribution in Guangxi under current and future climate scenarios (2050s, 2090s; SSP126, SSP370, SSP585). The model demonstrated acceptable predictive performance (training AUC = 0.7784, test AUC = 0.6935) with the test AUC falling within the acceptable/fair range (0.6–0.7) according to standard evaluation criteria. The dominant drivers were available water capacity (24.3%), altitude (24.1%), and temperature annual range (21.9%), with suitability declining when AWC exceeded 3.0, altitude exceeded 543 m, or bio7 fell below 23.06 °C. Current suitable habitats cover 18.53 × 10⁴ km² (89.7% of Guangxi), with high suitability areas concentrated in the northeast and southwest. Under future scenarios, total suitable area remained stable (−1.2% to +3.2%), while high suitability areas expanded under SSP370/SSP585 (+39.0%/+41.1%) and contracted under SSP126 (−30.3%) by 2090s; centroid shifts were limited (2.95–14.93 km), with core habitats remaining in central Guangxi. These findings provide a quantitative basis for conservation and sustainable cultivation strategies for this species under climate change, and highlight the importance of soil water availability alongside topographic and climatic factors in shaping its distribution. Full article

Bio-based fertilizers derived from chicken manure (CM) composting show potential to meet the requirements of current European fertilizing product legislation, provided that the final product satisfies the relevant quality, stability, and hygienization criteria, although technological challenges remain. This review focuses on the critical aspects related to feedstock optimization and mitigation of odor emissions and antimicrobial resistance-related risks, including the occurrence of antibiotic residues, antibiotic-resistant bacteria, and antibiotic resistance genes. Effective CM composting depends on a balanced feedstock formulation, appropriate aeration, moisture control, temperature–time profiles, and maturation conditions. Co-composting with suitable co-substrates can optimize the carbon-to-nitrogen ratio, enhance nutrient retention, minimize nutrient losses, and improve process performance. Two critical challenges (odorous emissions and antibiotic resistance) remain inadequately characterized and regulated, posing potential risks to environmental quality and public health. Although composting typically reduces antibiotic residues and pathogenic bacteria, certain antibiotic resistance genes may persist, depending on feedstock contamination and operational parameters. This persistence highlights the need for further research and targeted mitigation strategies. Future studies should assess modified composting technologies and integrated process strategies to enhance compost maturity, stability, and microbial safety. A life cycle assessment of CM composting should be performed in future work to help identify trade-offs among operational conditions, enabling the optimization of product quality while reducing environmental impacts. Comparative evaluations with conventional inorganic fertilizers across multiple impact categories (e.g., energy demand) can further support sustainable decision-making and foster adoption of climate-resilient organic waste management practices. Full article

This exploratory pilot study analyzes the organizational and environmental determinants associated with stated intention to adopt biogas-solar microgrids within a circular bioeconomy framework. A quantitative, applied, cross-sectional design was used with 71 valid individual responses from participants linked to productive, agro-industrial, livestock, energy, and waste management organizations or projects, selected through nonprobabilistic convenience sampling. The analysis does not measure actual investment, implementation, or use; therefore, the results refer only to declared adoption intention and should not be generalized beyond the sample. The questionnaire measured perceived benefits, barriers, institutional conditions, financial feasibility, environmental value, organizational capabilities, and adoption intention. Content validity was supported by expert judgment, and psychometric reliability was assessed using Cronbach’s alpha and McDonald’s omega. Predictive modeling compared supervised classification, regression, and unsupervised segmentation techniques using train-test validation, cross-validation, and interpretability analyses. ExtraTrees achieved the best exploratory classification performance, with a test ROC-AUC of 0.889, while RandomForestRegressor showed the best regression performance; however, these values should be interpreted as sample-specific evidence rather than as a validated predictive tool. Organizational capabilities and environmental criteria emerged as the most influential predictors, and K-Means suggested two tentative readiness profiles with weak separation. The findings suggest that stated adoption intention is associated with a systemic configuration of organizational maturity, environmental legitimacy, financial feasibility, and institutional support, providing preliminary evidence for future larger sample validation and for decision-support discussions in sustainable energy transitions. Full article

The sustainable valorization of spontaneous flora biomass for the recovery of high value-added phytochemicals represents a key opportunity within the circular bioeconomy, yet it remains constrained by the environmental limitations of conventional extraction solvents and the lack of data-driven optimization frameworks. In this study, Natural Deep Eutectic Solvents (NADES) composed of betaine and 1,3-propanediol were designed and applied as bio-based extraction media for the recovery of bioactive metabolites from Artemisia annua L. spontaneous biomass in the context of green extraction and sustainable resource utilization. Two liquid–solid extraction techniques, namely vortex-assisted extraction and ultrasound-assisted extraction, were evaluated. The influence of key process parameters, including the eutectic component molar ratio, water content, solid-to-liquid (S/L) ratio, extraction temperature, and extraction time, was systematically investigated. Results demonstrated that extraction efficiency was strongly dependent on both solvent composition and process conditions, with distinct optimum parameters for different phytochemical classes. Maximum total polyphenol content (52.08 mg GAE/mL) was achieved via ultrasound-assisted extraction at 20 °C for 15 min, using a 1:3 NADES ratio with 40% water dilution and S/L = 1:5, while the highest flavonoid yield (17.34 mg QE/mL) was obtained by vortex-assisted extraction for 45 min using a 1:6 NADES ratio under the same dilution and S/L conditions. To identify extraction conditions associated with improved process efficiency, a hybrid modeling approach combining deep neural networks with the Success-History-based Adaptive Differential Evolution (SHADE) algorithm was employed, enabling high-accuracy prediction of extraction performance across a broad parameter space. The proposed framework demonstrates the feasibility of integrating green solvent design with machine learning-driven process modeling for the efficient valorization of underutilized plant biomass, contributing to the development of resource-efficient, sustainable extraction protocols, consistent with principles of process intensification and resource-efficient extraction strategies. Full article

In protracted humanitarian crises, solid waste management (SWM) becomes a major challenge due to limited resources, inadequate infrastructure, and competing response priorities. Waste generated in humanitarian settings typically consist of heterogeneous streams, where plastics, biodegradable fractions, and packaging materials represent the dominant components. Proper management of this waste is essential to reduce health risks and environmental impacts on local communities. Within this framework, sustainable bio-based alternatives and compostable solutions represent promising alternatives. The EU-funded Bio4HUMAN project promotes the integration of innovative bio-based solutions aligned with humanitarian and sustainability goals. An exploratory assessment focused on analyzing waste production, material composition, and handling practices in two case study locations in Sub-Saharan Africa (Democratic Republic of Congo (DRC) and South Sudan (SS)). The results indicate that humanitarian waste cannot be clearly distinguished from household or commercial waste, as streams are typically mixed. Waste composition is dominated by organic matter (43–65%), followed by plastics (15–33%), while other fractions such as paper, glass, metals, and textiles are less significant. Further insights into challenges and opportunities were obtained through a combination of quantitative surveys (n = 29), qualitative interviews with key informants (KIIs) (44) and group discussions sessions (FDG) (9), direct observations, and literature review. Subsequently, a scoping approach was applied to map and classify suitable sustainable solutions into two main categories: bio-based products (BBPs) and organic waste valorization technologies. These were assessed through life cycle assessment (LCA) in accordance with ISO 14040 and 14044, applying SimaPro v.10.2.0.3 software and the Ecoinvent 3.10 database, and compared against fossil-based alternatives. This study compares two case scenarios: a HDPE oil bottle versus PLA alternative (functional unit 6 L), and PE water container versus PLA alternative (functional unit 10 L). For the oil bottle, PLA shows a lower carbon footprint (1.33 kg CO₂-eq) than HDPE (2.37 kg CO₂-eq). In contrast, for the water container, PLA performs worse (2.22 kg CO₂-eq) compared to PE (1.59 kg CO₂-eq), due to higher material demand. The results suggest that benefits are context-dependent and most evident for lightweight products with high leakage risks, particularly when composting infrastructure is accessible. This study advances previous work on humanitarian SWM by integrating field-based waste flow characterization with context-specific screening and life cycle assessment of bio-based alternatives, providing quantitative evidence on the conditions under which these solutions can effectively reduce environmental burdens in protracted crisis settings. Full article

Harvesting remains one of the main bottlenecks in microalgae-based technologies. Although microalgae hold great promise for industrial biotechnology, their growth in dilute suspensions makes biomass recovery challenging. Conventional harvesting methods are often energy-intensive and costly, limiting large-scale implementation. This study applies a life cycle assessment (LCA) to evaluate the environmental performance of a laboratory-scale magnetic harvesting process of Chlorella vulgaris (C. vulgaris) using Fe₃O₄ microparticles in combination with polyaluminum chloride (PAC) and polyacrylamide (PAM), followed by magnetic oscillation for particle detachment and subsequent reuse. Electricity consumption was identified as the dominant environmental hotspot across most impact categories, with the detachment step accounting for nearly two-thirds of the total energy demand, a step often overlooked in previous LCA studies. The global warming potential (GWP) is consistent with typical laboratory-scale assessments and is mainly driven by energy inefficiencies associated with small processing volumes. The values obtained and the scale-up literature indicate that further optimization and future industrial-scale production will decrease these values into a realistic and competitive range. Sensitivity analysis showed that replacing grid electricity with photovoltaic power significantly reduces environmental impacts. The use of NaOH as a reagent also contributed substantially to environmental impacts. Reusing magnetic particles (4 cycles) reduced material resource depletion by up to fourfold, which is a very relevant result bearing in mind the principles of sustainability and circularity. Full article

Sustainable agriculture has been a focus of research for over three decades, gaining particular urgency with the escalation of global conflicts, especially the Russian–Ukrainian war. Selecting appropriate parameters for objectively assessing sustainable agricultural development remains challenging, with limited studies addressing the aggregation of all relevant indicators into a single analytical framework. Given that these indicators and their quantitative values change annually, continuous updating and analysis are essential. This study was guided by selected SALSA/PRISMA principles to structure the indicator-selection process for examining Ukraine’s agricultural sector, which is vital to both national and global food security and accounts for approximately 10% of GDP, more than 50% of exports, and nearly 17% of employment. Alongside climate change pressures, the sector faces severe disruption from military aggression, undermining its economic contribution and stability. This research identifies and selects the most relevant economic, ecological, and social indicators to assess sustainable agricultural development in Ukraine, comparing values before and during the war. Based on these, this study proposes the Sustainable Agriculture Index (ISA), an aggregated measure that integrates multiple dimensions of sustainability. The ISA was calculated using a normalized weighted aggregation approach across economic, environmental, and social indicators. This approach enables a comprehensive evaluation of Ukraine’s agricultural resilience and its capacity to contribute to sustainable development under crisis conditions. Full article

This study evaluates the scalability and sustainability impacts of integrating Napier grass cultivation with biofertilizer production and dairy systems in rural West Bengal. Field-level evidence indicates that biofertilizer application and irrigation optimization significantly enhance soil organic carbon (SOC), improving nutrient availability and enabling Napier yields of up to 500 tons/acre on fallow land. A technoeconomic model shows strong economies of scale, with production costs decreasing by 40% when area under cultivation is simulated from 1 acre to 100 acres. Statewide scaling scenarios demonstrate significant development potential. Under 10% adoption of fallow land by 2040, approximately 75 million tons of biomass can be grown annually, benefiting 3.75 million households, doubling under a 20% adoption scenario by 2050. The system enables a 2.5–4× increase in household income while delivering substantial climate co-benefits. Avoided emissions from manure management are estimated at ~40 Mt CO₂ annually by 2040, increasing to ~80 Mt CO₂ by 2050, alongside additional gains from soil carbon sequestration and reduced high-emission urea-use. Overall, the proposed circular model offers a scalable pathway for achieving multiple Sustainable Development Goals through integrated agricultural transformation. Full article

Vegetation cover plays a fundamental role in maintaining ecosystem structure and function. Understanding its spatial and temporal variability, along with its driving factors, is critical for advancing environmental studies. This research targets the subtropical Zhejiang region in southeastern China, utilizing MODIS-derived NDVI data covering 2001 to 2020. By integrating Sen’s slope estimator, Mann–Kendall trend analysis, spatial autocorrelation (Moran’s I), and the Geodetector framework, we assessed trends, patterns, and primary influencing factors of vegetation change. Our findings include: (1) a statistically significant upward trend in NDVI across 59.4% of the study area (Sen’s slope = 0.0025, p < 0.01), corresponding to an approximate annual increase of 0.44%; (2) notable spatial clustering of NDVI values, with high NDVI zones located in southwestern forested areas and low NDVI zones in expanding urban regions, indicating a clear spatial differentiation between natural and human-dominated landscapes; (3) elevation (Q = 0.64), nighttime lights (Q = 0.63), and slope (Q = 0.57) showed relatively higher explanatory power, and the interaction between nighttime lights and land use (NTL × LULC) exhibited the strongest explanatory power (Q = 0.72); (4) high-risk zones, associated with dense populations and intense urban development, coincided with lower NDVI values. These results deepen our understanding of vegetation dynamics in subtropical zones and provide insights for sustainable ecosystem and land management. Full article

This study aims to elucidate the impact of biostimulants and fungicides on onion yield and quality, utilizing a combined experimental and molecular modeling approach. The biostimulants (Humiblack^®, Agasi^®, and Tifi^®) and fungicides (mancozeb and metalaxyl) were applied to onion crops, resulting in significant improvements in onion quality and yield. The stability and environmental impact of mancozeb and metalaxyl alone and in conjunction with biostimulants were investigated. The stability of the fungicide mixture was assessed in ultrapure water and rainwater, revealing high resistance to hydrolysis. Solar stability assessments, conducted using a sun simulator to mimic environmental conditions, highlighted differences in stability between mancozeb and metalaxyl in the presence of biostimulants. Metalaxyl showed higher photostability owing to the benzene ring. It was also less susceptible to biostimulant effects and remained stable in solution. Density functional theory descriptors and frontier orbital analysis rationalized the higher photoreactivity of mancozeb (smaller HOMO–LUMO gap and broader orbital delocalization). At the same time, molecular dynamics simulations supported stronger solvation of mancozeb and short-range water structuring, consistent with enhanced aqueous susceptibility. The results link fungicide physicochemical properties with field performance and aqueous stability, supporting the use of the fungicide mixture together with a single biostimulant as a practical approach for balancing crop productivity and environmental persistence. Full article