Search Results (105)

Under the dual strategic imperatives of carbon neutrality and digital transformation, the power industry plays a pivotal role in advancing green and low-carbon development. Green Total Factor Productivity (GTFP) provides a comprehensive measure of efficiency in the power sector under energy and environmental constraints. Using panel data from 31 Chinese provinces over the period 2012–2023, this study employs a super-efficiency Slacks-Based Measure (SBM) model, kernel density estimation, standard deviation ellipse analysis, the Gini coefficient, and fuzzy-set Qualitative Comparative Analysis (fsQCA) to systematically examine the spatiotemporal evolution, regional disparities, and digital-driven improvement pathways of power industry GTFP. The results indicate that national power-sector GTFP exhibits a fluctuating upward trend, accompanied by pronounced regional heterogeneity. A distinct spatial pattern has emerged, characterized by rapid improvement in the western region, relative stability in the eastern region, contraction in the central region, and persistent lagging in the northeastern region. Spatially, the distribution has evolved from an initial east–west dual-core structure to a three-tier gradient pattern led by the west, stabilized in the east, and depressed in the central region. Kernel density estimation reveals a clear multi-peak polarization trend, while standard deviation ellipse analysis shows a relatively stable spatial center with continuously expanding dispersion along the northeast–southwest axis. Further analysis demonstrates that interregional differences remain the primary source of overall inequality, with rapidly widening intraregional disparities in the western region. Configurational analysis identifies five digital-economy-driven pathways to high GTFP, highlighting that no single optimal configuration exists. Instead, multiple combinations of technological, organizational, and environmental conditions jointly facilitate GTFP enhancement. These findings provide empirical evidence to support differentiated and precision-oriented policy design for promoting coordinated digital transformation and green development in China’s power industry. Full article

Measuring the service levels and spatial equity of urban parks constitutes a core research topic within the field of environmental justice. Against the backdrop of low-carbon urban transformation and sustainable development, this study constructs an ecological supply indicator calculation model for parks based on landscape ecology theory. Leveraging spatio-temporal big data such as Points of Interest (POI) and second-hand property transactions, it establishes a demand evaluation indicator system centered on human activity intensity. The study employs the Gini coefficient and location entropy to gauge the spatial equity of park supply–demand balance, utilizing the Z-score method to classify supply–demand matching types. An empirical case study is conducted in Shenzhen. Findings indicate that despite Shenzhen possessing abundant global-scale park resources, a Gini coefficient of 0.489 reveals significant deficiencies in the equitable provision of park services, with spatial distribution exhibiting pronounced social stratification. Specifically: (1) location entropy values exhibit an east-high, west-low spatial pattern; (2) areas with high location entropy are predominantly concentrated in Dapeng New District, rich in green space resources, where supply exceeds demand, creating an imbalance; and (3) areas with low locational entropy values are predominantly distributed in industrial clusters such as western Bao’an and western Longgang, exhibiting contradictory characteristics of low supply and high demand. Overall, the distribution of park and green space resources exhibits a polarized pattern. Full article

Spring vegetation phenology is highly sensitive to climate change; however, climate drivers and their threshold responses at the urban scale remain insufficiently and systematically quantified. Focusing on 31 provincial capitals and municipalities in mainland China, this study integrated MODIS MCD12Q2-derived start-of-season (SOS) for spring green-up and TerraClimate climate data (2001–2023) at a 500 m grid resolution. SOS trends were characterized using the Mann–Kendall test and the Theil–Sen slope estimator. Building on these trend metrics, we developed an XGBoost–SHAP framework using the interannual rate of temperature change (tem_slope) and the interannual rate of precipitation change (pre_slope) as input features, to quantify the nonlinear contributions of climate-change rates to SOS trends and to identify key thresholds. Results indicate that the multi-year mean SOS across China’s provincial capitals and municipalities is primarily distributed between approximately DOY 74 and 138, exhibiting a clear spatial pattern of earlier green-up in the south, later green-up in the north, and delayed green-up on plateaus, with pronounced shifts in distribution centers and dispersion among climatic zones and cities. At the city level, the mean SOS trend shows an overall advancing rate of 0.81 d·year⁻¹ (i.e., the average of city-mean Sen slopes across the 31 cities). Pixel-level trend analyses show that advancing and delaying trends commonly coexist within most cities; among pixels with significant or marginally significant SOS trends identified by the Mann–Kendall test (MK p < 0.10) across all cities, advancing and delaying SOS pixels account for 75.02% and 24.98%, respectively. At the city scale, the proportions of advancing versus delaying pixels vary markedly among cities, forming directional structures characterized by advance-dominant, delay-dominant, or bidirectional coexistence patterns. SHAP dependence relationships further reveal that the effects of tem_slope and pre_slope on SOS trends are generally nonlinear and piecewise, with substantial heterogeneity across climate zones and cities. The identified tipping points and associated sensitive ranges collectively delineate spatially differentiated climate-sensitive intervals, which define the nonlinear response boundaries of spring SOS to sustained warming and precipitation changes. This study provides quantitative evidence for regional differences in urban spring phenological responses to climate change across major Chinese cities and offers a methodological reference for identifying actionable climate thresholds in urban greening design and climate-adaptive management. Full article

Nature-based tourism (NBT) is increasingly promoted as a means to contribute to conservation efforts. However, there is limited understanding of the relationship between environmental education and awareness and conservation attitudes and practices in this form of tourism that centers on green spaces. This study, therefore, aimed to explore this relationship using data from tourism operators and local communities in the Mexican Caribbean and provide useful insights for environmental sustainability in tourism destinations that depend on nature as a critical resource. The study employed a triangulation approach, which involved examination of two sets of data: firstly, household-level survey information from local communities participating in nature-based tourism; and secondly, data from semi-structured in-depth interviews with tour companies operating within the Mexican Caribbean, as well as focus group discussions with key informants including academicians, public-sector stakeholders and other opinion leaders in the tourism industry. Household surveys determined associations between potential predictor variables (environmental education and awareness, local community involvement, costs and benefits distribution) and conservation perspectives and practices in nature-based tourism. Semi-structured interviews and focus group discussions explored participants’ attitudes, experiences and views on environmental education and awareness, nature-based tourism, attitudes and practices towards conservation. Findings demonstrated that direct benefits from nature-based tourism are significant, but do not guarantee positive conservation attitudes and practices among the local communities. Other factors (indirect benefits), such as environmental education and awareness, could be more effective in achieving environmental sustainability and quality in nature-based tourism. It was also found that several tourism operators lack formal environmental education and awareness programs. The study recommends that the entire cycle of using natural resources for tourism purposes and tourists’ interaction with nature be anchored in adequate environmental education and awareness. This research contributes to valuable insights into debates, practices and policy developments related to nature-based tourism as a mechanism for environmental sustainability in biosphere reserves and tourism destinations. Full article

The high energy consumption and spatiotemporal thermal asymmetry of data center cooling systems have become critical bottlenecks constraining their green and sustainable development. Traditional point-type temperature sensors suffer from insufficient spatial coverage, while conventional feedback control strategies exhibit delayed responses and limited adaptability under dynamic workloads. To address these challenges, this study proposes a real-time thermal symmetry management framework for data centers based on distributed fiber optic temperature sensing and model predictive control (MPC). The proposed system employs Brillouin scattering-based distributed sensing to continuously acquire high-density temperature measurements from thousands of points along a single optical fiber, enabling fine-grained perception of the three-dimensional thermal field. On this basis, a hybrid prediction model integrating thermodynamic physical equations with a Temporal Convolutional Network–Bidirectional Gated Recurrent Unit (TCN–BiGRU) deep neural network is developed to achieve accurate and stable spatiotemporal temperature forecasting. Furthermore, a symmetry-aware MPC controller is designed with the dual objectives of minimizing cooling energy consumption and suppressing thermal field deviations, thereby restoring temperature uniformity through rolling-horizon optimization. Experimental validation in a production data center demonstrates that the distributed sensing system achieves a measurement deviation of 0.12 °C, while the hybrid prediction model attains a root mean square error of 0.41 °C, representing a 26.8% improvement over baseline methods. The MPC-based control strategy reduces daily cooling energy consumption by 14.4%, improves the power usage effectiveness (PUE) from 1.58 to 1.47, and significantly enhances both thermal symmetry and operational safety. The Thermal Symmetry Index (TSI) decreased from 0.060 to 0.035, indicating a 41.7% improvement in spatial temperature distribution uniformity. The TSI is defined as the ratio of spatial temperature standard deviation to mean temperature, where lower values indicate better thermal uniformity; TSI < 0.03 represents excellent symmetry, 0.03–0.05 indicates good symmetry, and TSI > 0.08 suggests significant asymmetry requiring intervention. These results provide an effective and practical solution for intelligent operation, energy-efficient control, and low-carbon transformation of next-generation green data centers. Full article

To accurately understand and investigate carbon fluxes in cropland ecosystems, this study adopted a machine learning ensemble model for estimation. Focusing on the Jinzhou station of the ChinaFLUX, we integrated eddy covariance carbon flux observations with multi-source satellite remote sensing data to construct a machine learning-based cropland carbon flux estimation model. For environmental driver selection, a strategy combining correlation analysis with ecological mechanism understanding was employed to screen LST, NDVI, and NDMI as model input variables, effectively avoiding multicollinearity issues. Using footprint-weighted integrated data from 2005 to 2014 for model training and validation, a Stacking ensemble model was constructed with the RF model serving as the meta-learner to stack the predictions of RF, CART, and GBM. The ensemble model further reduced the prediction error (RMSE = 39.82), maintaining an R² > 0.9 in most years and effectively improving predictive performance during anomalous years where single models underperformed. Based on these findings, the model was applied to analyze the spatiotemporal evolution of NEE in Jinzhou croplands from 2005 to 2014. The analysis revealed that while the region functioned overall as a carbon sink, it exhibited significant spatiotemporal heterogeneity. Spatially, the distribution followed a pattern of “strong intensity in the northeast and center, and weak intensity in the northwest and southwest.” Temporally, the sink intensity underwent significant interannual oscillations characterized by a “strengthening–weakening–re-strengthening–declining” trajectory. The high-precision prediction method proposed in this study is of great significance for revealing spatiotemporal variations in carbon sources/sinks, guiding green agricultural development, and supporting relevant policy formulation. Full article

(1) Background: Under the dual pressures of global climate change and rapid urbanization, blue–green infrastructure as a nature-based solution is crucial for enhancing urban sustainability. However, there is still a significant cognitive gap regarding the synergy mechanism between its blue and green components and its nonlinear combined impact on sustainability. (2) Method: To fill this gap, this study takes Zhenjiang, a national sponge pilot city in China, as a case and constructs a comprehensive assessment framework. The framework combines multi-source spatio-temporal big data (remote sensing images, point of interest data, mobile phone signaling data) with spatial analysis techniques (geodetectors, Getis-Ord Gi*) to quantify the synergistic effects of blue–green infrastructure on environmental, economic, and social sustainability. (3) Results: The main findings include the following: (1) urban sustainability presents a spatial differentiation pattern of “high in the center, low in the periphery, and multi-core”, and there is a significant positive spatial correlation with the distribution of blue–green infrastructure. (2) The economic dimension, especially daytime population vitality, contributes the most to overall sustainability. (3) Crucially, the co-configuration of sponge facility density and park facility density was identified as the most influential driving mechanism (q = 0.698). In addition, the interaction between the blue infrastructure and the green sponge facilities showed obvious nonlinear enhancement characteristics. Based on spatial matching analysis, the study area was divided into three priority intervention zones: high, medium, and low. (4) Conclusions: This study confirms that it is crucial to view blue–green infrastructure as an interrelated collaborative system. The findings deepen the theoretical understanding of the synergistic empowerment mechanism of blue–green infrastructure and provide scientifically based and actionable policy support for the precise planning of ecological spaces in high-density urbanized areas. Full article

The convergence of digital innovation and sustainability imperatives is transforming the architecture of agri-food systems, signaling not just a technological upgrade, but a reorganization of how food production, distribution, and governance are approached. This study presents a comprehensive bibliometric mapping of global research on sustainable and digital agri-food systems between 2004 and 2025, based on data from the Web of Science Core Collection and analyzed using the Bibliometrix within RStudio (Version: 2024.12.1+563). Through co-word analysis, bibliographic coupling, and temporal trend exploration, the study identified a marked surge in scholarly activity after 2020, driven by the alignment of digital innovation with major policy frameworks such as the European Green Deal and the Farm-to-Fork Strategy. Findings highlight Europe—particularly Italy, the Netherlands, and France—as the leading knowledge hub, demonstrating both institutional capacity and policy responsiveness. Thematic clusters revealed four dominant trajectories in recent research: digital governance, blockchain and traceability, circular economy integration, and ESG-based performance frameworks. These directions suggest a transition from narrow efficiency-centered approaches to more comprehensive, ethically informed, and technologically integrated agri-food systems. The study frames digitalization as both a technical infrastructure and a socio-organizational driver that reshapes transparency, accountability, and coordination within food value chains. It also outlines strategic entry points for improving interoperability, bridging digital divides, and advancing collaborative governance models across the agri-food sector. In addition to its empirical findings, the article contributes methodologically by positioning bibliometric analysis as a valuable tool for tracking major conceptual and structural shifts within food system research. In conclusion, digital transformation in agri-food systems is not merely about technological enhancement—it is a fundamental restructuring of processes, relationships, and governance mechanisms that define how food systems operate in an era of innovation, complexity, and sustainability challenges. Full article

Urban forests and green spaces are increasingly promoted as Nature-Based Solutions (NbS) to mitigate climate risks, enhance human well-being, and support resilient and sustainable cities. Focusing on the theme of optimizing urban green space ecosystem services to foster resilient and sustainable cities, this study systematically analyzes 861 relevant publications indexed in the Web of Science Core Collection from 2005 to 2025. Using bibliometric analysis and scientific knowledge mapping methods, the research examines publication characteristics, spatial distribution patterns, collaboration networks, knowledge bases, research hotspots, and thematic evolution trajectories. The results reveal a rapid upward trend in this field over the past two decades, with the gradual formation of a multidisciplinary knowledge system centered on environmental science and urban research. China, the United States, and several European countries have emerged as key nodes in global knowledge production and collaboration networks. Keyword co-occurrence and cluster analyses indicate that research themes are mainly concentrated in four clusters: (1) ecological foundations and green process orientation, (2) nature-based solutions and blue–green infrastructure configuration, (3) social needs and environmental justice, and (4) macro-level policies and the sustainable development agenda. Overall, the field has evolved from a focus on ecological processes and individual service functions toward a comprehensive transition emphasizing climate resilience, human well-being, and multi-actor governance. Based on these findings, this study constructs a knowledge ecosystem framework encompassing knowledge base, knowledge structure, research hotspots, frontier trends, and future pathways. It further identifies prospective research directions, including climate change adaptation, integrated planning of blue–green infrastructure, refined monitoring driven by remote sensing and spatial big data, and the embedding of urban green space ecosystem services into the Sustainable Development Goals and multi-level governance systems. These insights provide data support and decision-making references for deepening theoretical understanding of Urban Green Space Ecosystem Services (UGSES), improving urban green infrastructure planning, and enhancing urban resilience governance capacity. Full article

The Chang–Zhu–Tan Metropolitan Area, the first national-level metropolitan region in central China, faces a prominent conflict between urban expansion and the quality of the ecological environment (EEQ) amid rapid urbanization. Investigating the ecological evolution of this area holds both significant scientific and practical value. This study leverages the Google Earth Engine (GEE) platform and long-term Landsat remote sensing imagery to explore the spatiotemporal variations in EEQ in the Chang–Zhu–Tan Metropolitan Area from 2002 to 2022. A modified remote sensing ecological index (MRSEI) was developed by incorporating the Air Quality Difference Index (DI), and changes in EEQ were analyzed using Sen slope estimation and the Mann–Kendall test. Apart from that, using 2022 data as an example, the Optimal Parameter Geodetector (OPGD) was employed to evaluate the impacts of multifarious driving factors on EEQ. The main findings of the study are as follows: (1) In comparison with the traditional remote sensing ecological index (RSEI), MRSEI can more effectively reflect regional differences in EEQ. (2) The overall EEQ in the region is relatively good, with over 60% of the area classified as “excellent” or “good”. The spatial distribution follows a pattern of “higher at the edges, lower in the center”. (3) The EEQ trend in the study area generally suggests reinforcement, though central areas such as Kaifu District and Tianxin District exhibit varying degrees of degradation. (4) Human factors have a greater impact on EEQ than natural factors. Land Use and Land Cover Change (LUCC) is the primary driver of the spatial differentiation in the regional ecological environment, with the interaction of these factors producing synergistic effects. The results of this study strongly support the need for ecological protection and green development in the Chang–Zhu–Tan Metropolitan Area, offering valuable insights for the sustainable development of other domestic metropolitan regions. Full article

Maintaining the stability of the mine roadway is of paramount importance, as it is critical in ensuring the daily operational continuity, personnel safety, long-term economic viability, and sustainability of the entire mining operation. Significant instability can trigger serious disruptions—such as production stoppages, equipment damage, and severe safety incidents—which ultimately compromise the project’s financial returns and future prospects. Therefore, the proactive assessment and rigorous control of roadway stability constitute a foundational element of successful and sustainable resource extraction. In China, thick and extra-thick coal seams constitute over 44% of the total recoverable coal reserves. Consequently, their safe and efficient extraction is considered vital in guaranteeing energy security and enhancing the efficiency of resource utilization. The surrounding rock of gob-side roadways in typical coal seams is often fractured due to high ground stress, intensive mining disturbances, and overhanging goaf roofs. Consequently, asymmetric failure patterns such as bolt failure, steel belt tearing, anchor cable fracture, and shoulder corner convergence are common in these entries, which pose a serious threat to mine safety and sustainable mining operations. This deformation and failure process is associated with several parameters, including the coal seam thickness, mining technology, and surrounding rock properties, and can lead to engineering hazards such as roof subsidence, rib spalling, and floor heave. This study proposes countermeasures against asymmetric deformation affecting gob-side entries under intensive mining pressure during the fully mechanized caving of extra-thick coal seams. This research selects the 8110 working face of a representative coal mine as the case study. Through integrated field investigation and engineering analysis, the principal factors governing entry stability are identified, and effective control strategies are subsequently proposed. An elastic foundation beam model is developed, and the corresponding deflection differential equation is formulated. The deflection and stress distributions of the immediate roof beam are thereby determined. A systematic analysis of the asymmetric deformation mechanism and its principal influencing factors is conducted using the control variable method. A support approach employing a mechanical constant-resistance single prop (MCRSP) has been developed and validated through practical application. The findings demonstrate that the frequently observed asymmetric deformation in gob-side entries is primarily induced by the combined effect of the working face’s front abutment pressure and the lateral pressure originating from the neighboring goaf area. It is found that parameters including the immediate roof thickness, roadway span, and its peak stress have a significant influence on entry convergence. Under both primary and secondary mining conditions, the maximum subsidence shows an inverse relationship with the immediate roof thickness, while exhibiting a positive correlation with both the roadway span and the peak stress. Based on the theoretical analysis, an advanced support scheme, which centers on the application of an MCRSP, is designed. Field monitoring data confirm that the peak roof subsidence and two-side closure are successfully limited to 663 mm and 428 mm, respectively. This support method leads to a notable reduction in roof separation and surrounding rock deformation, thereby establishing a theoretical and technical foundation for the green and safe mining of deep extra-thick coal seams. Full article

This study assesses the impact of the Green Morocco Plan (GMP) on cereal production in Morocco between 1994 and 2020, focusing on spatial and temporal variations and their relationship with seasonal rainfall. Given the limited availability of other potentially influential factors, this study focuses on two main drivers: rainfall and cultivated surface area changes. The analysis centers on three cereal crops, durum wheat (Dw), soft wheat (Sw), and barley (Br), given their crucial role in Morocco’s food security. Three major cereal-producing regions, Tangier–Tetouan–Al Hoceima (TTH), Fes–Meknes (FM), and Rabat–Sale–Kenitra (RSK), accounting for 84% of national cereal output, were analyzed to capture contrasting agro-climatic conditions. Using regional production data and rainfall records, combined with breakpoint detection and correlation analyses, the study identifies the principal drivers of production shifts associated with the implementation of the GMP. Results reveal a significant structural change in cereal production around 2008, coinciding with the GMP launch. In TTH, mean annual production of Dw increased by 117% and Sw by 153%, while Br grew by 53%. In FM, gains reached 81% for Sw, 46% for Dw, and 52% for Br, whereas in RSK the respective increases were 63%, 39%, and 50%. These improvements occurred despite recurrent droughts and reductions in cultivated areas, indicating enhanced resilience supported by irrigation expansion and improved inputs under the GMP. Correlation analyses show that mid-season rainfall (January–May) strongly influences production, with significant coefficients for durum wheat (r = 0.6) and barley (r = 0.7), whereas soft wheat shows weaker rainfall dependence, likely reflecting irrigation prioritization and market-driven management. The results also suggest that rainfall timing and intra-seasonal distribution exert greater influence on production than total rainfall. Overall, the findings demonstrate that the GMP substantially strengthened cereal productivity and resilience, while decoupling production from direct rainfall dependence and revealing emerging regional contrasts in land-use trajectories. Full article

Innovation in the new energy industry serves not only as a key accelerator for the global green and low-carbon energy transition but also as a core driving force of the ongoing energy revolution. This study utilizes data on publications, patents, and the spatial distribution of representative innovation enterprises in the new energy industry of the Yangtze River Delta urban agglomeration from 2009 to 2023 to construct a multilayer science–technology–industry innovation network. Social network analysis is employed to examine its evolutionary dynamics and structural characteristics, and the Quadratic Assignment Procedure (QAP) is used to investigate the factors shaping intercity innovation linkages. The results reveal that the multilayer innovation network has continuously expanded in scale, gradually forming a multi-core radiative structure with Shanghai, Nanjing, and Hangzhou at the center. At the cohesive subgroup level, the scientific and technological layers exhibit clear hierarchical differentiation, where core cities tend to engage in strong mutual collaborations, while the industrial layer shows a hub-and-spoke pattern combining large, medium, and small cities. In terms of layer relationships, the centrality of the scientific layer increasingly surpasses that of the technological and industrial layers. Inter-layer degree correlations and overlaps also display a strengthening trend. Furthermore, differences in regional higher education scale, urban economic density, and geographic proximity are found to exert significant influences on scientific, technological, and industrial innovation linkages among cities. In response, this study recommends enhancing the leadership role of core cities, leveraging the bridging and intermediary functions of peripheral cities, and promoting application-driven cross-regional innovation collaboration, thereby building efficient science–technology–industry networks and enhancing intercity innovation linkages and the flow of innovation resources, and ultimately promoting the high-quality development of the regional new energy industry. Full article

The Qinba Mountains in China span six provinces, characterized by a large population, rugged terrain, steep peaks, deep valleys, and scarce flat land, making large-scale agricultural development challenging. Terraced fields serve as the core cropland type in this region, playing a vital role in preventing soil erosion on sloping farmland and expanding agricultural production space. They also function as a crucial medium for sustaining the ecosystem services of mountainous areas. As a transitional zone between China’s northern and southern climates and a vital ecological barrier, the Qinba Mountains’ terraced ecosystems have undergone significant spatial changes over the past two decades due to compound factors including the Grain-for-Green Program, urban expansion, and population outflow. However, current large-scale, long-term, high-resolution monitoring studies of terraced fields in this region still face technical bottlenecks. On one hand, traditional remote sensing interpretation methods rely on manually designed features, making them ill-suited for the complex scenarios of fragmented, multi-scale distribution, and terrain shadow interference in Qinba terraced fields. On the other hand, the lack of high-resolution historical imagery means that low-resolution data suffers from insufficient accuracy and spatial detail for capturing dynamic changes in terraced fields. This study aims to fill the technical gap in detailed dynamic monitoring of terraced fields in the Qinba Mountains. By creating image tiles from Landsat-8 satellite imagery collected between 2017 and 2020, it employs three deep learning semantic segmentation models—DeepLabV3 based on ResNet-34, U-Net, and PSPNet deep learning semantic segmentation models. Through optimization strategies such as data augmentation and transfer learning, the study achieves 15-m-resolution remote sensing interpretation of terraced field information in the Qinba Mountains from 2000 to 2020. Comparative results revealed DeepLabV3 demonstrated significant advantages in identifying terraced field types: Mean Pixel Accuracy (MPA) reached 79.42%, Intersection over Union (IoU) was 77.26%, F1 score attained 80.98, and Kappa coefficient reached 0.7148—all outperforming U-Net and PSPNet models. The model’s accuracy is not uniform but is instead highly contingent on the topographic context. The model excels in environments that are archetypal for mid-altitudes with moderately steep slopes. Based on it we create a set of tiles integrating multi-source data from RBG and DEM. The fusion model, which incorporates DEM-derived topographic data, demonstrates improvement across these aspects. Dynamic monitoring based on the optimal model indicates that terraced fields in the Qinba Mountains expanded between 2000 and 2020: the total area was 57.834 km² in 2000, and by 2020, this had increased to 63,742 km², representing an approximate growth rate of 8.36%. Sichuan, Gansu, and Shaanxi provinces contributed the majority of this expansion, accounting for 71% of the newly added terraced fields. Over the 20-year period, the center of gravity of terraced fields shifted upward. The area of terraced fields above 500 m in elevation increased, while that below 500 m decreased. Terraced fields surrounding urban areas declined, and mountainous slopes at higher elevations became the primary source of newly constructed terraces. This study not only establishes a technical paradigm for the refined monitoring of terraced field resources in mountainous regions but also provides critical data support and theoretical foundations for implementing sustainable land development in the Qinba Mountains. It holds significant practical value for advancing regional sustainable development. Full article

Population aging and rising rehabilitation demands highlight the need for advanced assistive devices to improve mobility in individuals with motor impairments. Existing back-support lifting nursing beds often lack adequate human–machine adaptability, safety, and emotional consideration. This study presents a human-centered, data-driven optimization pipeline that integrates behavior–emotion dual recognition, simulation verification, and parameter optimization with user demand mining, biomechanical simulation, and sustainable practices. The design utilizes GreenAI, focusing on low-power algorithms and eco-friendly materials, ensuring energy-efficient AI models and reducing the environmental footprint. A dual-channel data fusion method was developed, combining movement parameters from sit-to-lie transitions with emotional needs extracted from e-commerce reviews using the Term Frequency-Inverse Document Frequency (TF-IDF) and Latent Dirichlet Allocation (LDA) models. The fuzzy Kano model prioritized design objectives, identifying multi-position adjustment, joint protection, armrest optimization, and interaction comfort as key targets. An AnyBody-based human–device model quantified muscle (erector spinae, rectus abdominis, trapezius) and hip joint loads during posture changes. Simulations verified the design’s ability to improve load distribution, reduce joint stress, and enhance comfort. The optimized nursing bed demonstrated improved adaptability across user profiles while maintaining functional reliability. This framework offers a scalable paradigm for intelligent rehabilitation equipment design, with potential extension toward AI-driven adaptive control and clinical validation. This sustainable methodology ensures that the device not only meets rehabilitation goals but also contributes to a more environmentally responsible healthcare solution, aligning with global sustainability efforts. Full article