Sustainability

Scientific and rational agricultural land suitability assessment is essential for improving land resource use efficiency and promoting sustainable agricultural development. In arid and ecologically fragile regions, agricultural production is jointly constrained by climatic conditions, water availability, soil properties, and existing land-use patterns. This study employed the Maximum Entropy (MaxEnt) model to evaluate the spatial distribution and future evolution of agricultural land suitability in the Aksu region from 2021 to 2080 under three future climate scenarios, namely SSP-126, SSP-245, and SSP-585. The evaluation framework integrated climatic variables, topographical factors, soil properties, land use, and hydrological conditions, while climatic indicators were further screened through correlation analysis. The results showed that the MaxEnt model achieved high predictive accuracy and stability across different climate scenarios and periods, with AUC values for both the training and testing datasets consistently exceeding 0.90, indicating good model applicability and discriminatory ability. Feature importance analysis revealed that land use, temperature-related variables (bio1, bio4, and bio10), precipitation seasonality (bio15), and soil factors were the main variables influencing agricultural land suitability. These findings highlight the important roles of land-use patterns, thermal conditions, precipitation stability, and soil properties in determining agricultural suitability in arid oasis regions. Furthermore, future climate scenarios did not substantially alter the overall spatial distribution pattern of agricultural land suitability, but more pronounced local improvements and degradation were observed under higher-emission scenarios. This study provides insights into future changes in agricultural land suitability and offers scientific support for climate-adaptive agricultural management and sustainable land resource utilization in arid regions.

Facility layout planning (FLP) in the agri-food sector faces unique complexities due to product perishability, biosafety regulations, and Industry 5.0 (I5.0) requirements. The reviewed literature is skewed toward static formulations that overlook detailed layout, demand uncertainty, and social sustainability. In response, a targeted review, reported following PRISMA 2020, and expert judgement yielded an ontological classification of ten characteristics influencing detailed agri-food layouts, whose relevance and relationships were supported by five external experts (S-CVI/Ave = 0.96); the system roles proposed by the authors were not reproduced in a blind assignment and remain modelling hypotheses. We then develop an eight-pillar conceptual framework addressing dynamic multi-period planning, stochastic uncertainty, multi-objective optimisation, comprehensive sustainability, human-centric ergonomics, operation-level routing, a bottom-up approach, and perishability as an endogenous variable. A diagnostic case study of an agri-food small and medium-sized enterprise (SME) illustrates how the classification structures the diagnosis of layout inefficiencies, such as products reaching customers with reduced shelf life. Aligned with I5.0 principles, this research provides a conceptual basis for models that treat perishability and human well-being as core design variables. The classification applies mainly to agri-food SMEs operating under make-to-order and just-in-time strategies; large make-to-stock manufacturers should also consider product homogeneity and storage-based product types.

Identifying the key factors associated with urban carbon metabolism is essential for understanding the mechanisms underlying low-carbon urban transformation and achieving sustainable development goals. This study applies a meta-analysis approach to quantify the major factors influencing urban carbon metabolism in China. The literature search covered the period from 1 January 2010 to 5 March 2025 and was conducted using the China National Knowledge Infrastructure (CNKI), the Weipu database, the Wanfang database, Web of Science, Elsevier, Emerald, Scopus, and ScienceDirect. Based on 38 empirical studies, 227 effect sizes, and 4495 observations, 21 influencing factors were examined and classified into three dimensions: natural factors, socioeconomic factors, and land-use and landscape factors. The results indicate that these three dimensions were significantly associated with urban carbon metabolism, with socioeconomic factors having the greatest overall effect size. Specifically, the Largest Patch Index (LPI) and building land size showed the strongest correlations with urban carbon metabolism, whereas sunshine exhibited the weakest correlation among the examined factors. These findings highlight the importance of optimizing land-use structures and promoting green socioeconomic transformation, providing quantitative evidence and practical implications for improving urban carbon governance and supporting sustainable low-carbon development. The findings may inform urban planners, policymakers, and researchers in developing evidence-based strategies for urban carbon management and low-carbon development.