Search Results (2,273)

Rising global populations, infrastructural development, and rapid urbanization have heightened the reliance on a linear economy, resulting in severe environmental and human impacts. This crisis has triggered an urgent quest for sustainable and ecologically benign innovations, as outlined in the United Nations’ Sustainable Development Goals (SDGs). This review investigates the potential of sugarcane bagasse (SCB) as a promising feedstock for advancing circular bioeconomy initiatives in South Africa. It shows how this copious bioresource can be utilized to enhance the country’s biobased value chains by producing bio-commodities, such as biofuels and platform chemicals. The review also identifies the driving forces behind the circular bioeconomy model within the South African sugarcane industry. To achieve the circular bioeconomy, it outlines essential technological prerequisites, including critical pretreatment strategies and emerging bio-innovations necessary for the effective valorization of SCB. Furthermore, it showcases the R&D and commercial strides that have been achieved in South Africa. Finally, the study covers techno-economic studies that corroborate the economic viability of this domain. In conclusion, harnessing SCB not only presents a viable biorefinery pathway towards sustainable economic growth but also contributes to environmental preservation and social well-being, aligning with global sustainability imperatives. The successful integration of these innovative approaches could play a pivotal role in transforming the South African sugarcane industry into a continental leader in circular bioeconomy innovations. Full article

Rice, wheat, and maize are important food grains consumed by most of the population in Asian countries (like India, Japan, Singapore, Malaysia, China, and Thailand). These crops’ production is affected by biotic and abiotic factors that cause diseases in several parts of the crops (including leaves, stems, roots, nodes, and panicles). A severe infection affects the growth of the plant, thereby undermining the economy of a country, if not detected at an early stage. This may cause extensive damage to crops, resulting in decreased yield and productivity. Early safeguarding methods are overlooked because of farmers’ lack of awareness and the variety of crop diseases. This causes significant crop damage and can consequently lower productivity. In this manuscript, a lightweight vision transformer (MaxViT) with 814.7 K learnable parameters and 85 layers is designed for classifying crop diseases in paddy and wheat. The MaxViT DNN architecture consists of a convolutional block attention module (CBAM), squeeze and excitation (SE), and depth-wise (DW) convolution, followed by a ConvNeXt module. This network architecture enhances feature representation by eliminating redundant information (using CBAM) and aggregating spatial information (using SE), and spatial filtering by the DW layer cumulatively enhances the overall classification performance. The proposed model was tested using a paddy dataset (with 7857 images and eight classes, obtained from local paddy farms in Lalgudi district, Tiruchirappalli) and a wheat dataset (with 5000 images and five classes, downloaded from the Kaggle platform). The model’s classification performance for various diseases has been evaluated based on accuracy, sensitivity, specificity, mean accuracy, precision, F1-score, and MCC. During training and testing, the model’s overall accuracy on the paddy dataset was 99.43% and 98.47%, respectively. Training and testing accuracies were 94% and 92.8%, respectively, for the wheat dataset. Ablation analysis was carried out to study the significant contribution of each module to improving the performance. It was found that the model’s performance was immune to the presence of noise. Additionally, there are a minimal number of parameters involved in the proposed model as compared to pre-trained networks, which ensures that the model trains faster. Full article

Burrowing mammals function as ecosystem engineers by creating spatial heterogeneity in the soil structure and vegetation composition, thereby providing microhabitats for a wide range of organisms. These keystone species play a crucial role in maintaining local ecosystem functions and delivering ecosystem services. However, in Mongolia, where overgrazing has accelerated due to the expansion of a market-based economy, scientific knowledge remains limited regarding the impacts of human activities on such species. In this study, we focused on the Siberian marmot (Marmota sibirica), an ecosystem engineer inhabiting typical Mongolian steppe ecosystems. We assessed the relationship between the spatial distribution of marmot burrows and vegetation conditions both inside and outside Hustai National Park. Burrow locations were recorded in the field, and the Normalized Difference Vegetation Index (NDVI) was calculated, using Planet Lab, Dove-2 satellite imagery (3 m spatial resolution). Through a combination of remote sensing analyses and vegetation surveys, we examined how the presence or absence of anthropogenic disturbance (i.e., livestock grazing) affects the ecological functions of marmots. Our results showed that the distance between active burrows was significantly shorter inside the park (t = −2.68, p = 0.0087), indicating a higher population density. Furthermore, a statistical approach, using beta regression, revealed a significant interaction between the burrow type (active, non-active, off-colony area) and region (inside vs. outside the park) on the NDVI (e.g., outside × non-active: z = −5.229, p < 0.001). Notably, in areas with high grazing pressure outside the park, the variance in the NDVI varied significantly as a function of burrow presence or absence (e.g., July 2023, active vs. off-colony area: F = 133.46, p < 0.001). Combined with vegetation structure data from field surveys, our findings suggest that marmot burrowing activity may contribute to the enhancement of vegetation quality and spatial heterogeneity. These results indicate that the Siberian marmot remains an important component in supporting the diversity and stability of steppe ecosystems, even under intensive grazing pressure. The conservation of this species may thus provide a promising strategy for utilizing native ecosystem engineers in sustainable land-use management. Full article

In the context of the deep transformation of population structure and the coordinated advancement of high-quality development, exploring the mechanism of the impact of aging on economic growth has become a major issue related to the sustainable development of China. This study takes the 41 cities of the Yangtze River Delta urban agglomeration as a sample, using the population and economic census data from 2000 to 2020. It comprehensively applies an improved Solow model, GIS spatial analysis, spatial econometric models, and mediation effect tests to arrive at the following findings: (1) There is a significant asynchrony between economic growth and population aging in the Yangtze River Delta urban agglomeration. Economic growth has shifted from high-speed to high-quality development, while the aging process is accelerating and becoming more aged. (2) Population aging in the Yangtze River Delta has a nonlinear positive impact on economic growth. The intensity of this impact shows a characteristic of “strong-weak-strong,” with the first aging rate threshold being 11.63% and the second being 17.53%. (3) There is significant spatial autocorrelation between population aging and economic growth in the Yangtze River Delta urban agglomeration. The overall direction of the effect shows a spatial distribution pattern of “positive in the south and negative in the north.” The deepening of population aging in neighboring areas promotes local economic growth. (4) Labor productivity and optimization of the living environment constitute the core transmission pathways. Together, they account for more than 80% of the contribution and serve as the key mechanism for transforming aging pressures into growth momentum. This research provides practical guidance for solving the “rich” and “aging” contradictions in the Yangtze River Delta. It also offers a universal theoretical framework and a Chinese solution for aging economies worldwide to address the risk of growth stagnation. Full article

Globally, the accelerating aging of the rural labor force is profoundly impacting the economic resilience of the labor-intensive forestry sector. However, the intrinsic connection between the two has not been fully understood and requires further exploration. As the most populous nation globally and a top producer, trader, and consumer of forest products, China stands out as a perfect case study for this issue. Based on this, this study utilizes panel data from 30 provinces in China from 2012 to 2022 and employs a dual machine learning model to empirically examine the impact and mechanisms of rural labor force aging on forestry economic resilience from the perspective of production factors. The findings indicate: (1) overall, the increase in rural labor force aging significantly inhibits forestry economic resilience; (2) rural labor force aging enhances forestry economic resilience by promoting large-scale forest land management, driving forestry technological innovation, and increasing government capital investment; it also inhibits forestry economic resilience by reducing educational human capital and health human capital; (3) the rural force aging exerts a marked adverse effect on the resilience of the forestry economy in the eastern and central regions, major grain-producing areas, and major grain-consuming areas. Based on this, this study proposes policy recommendations in three areas: building a flexible and diversified labor supply and replacement system, exploring a “scale and technology” integration path suited to national conditions, and implementing differentiated regional strategies. The aim is to provide a reference for government departments in formulating strategies to enhance the resilience of the forestry economy in the era of aging. Full article

The transition to a circular economy (CE) is crucial to sustainable development, necessitating tailored assessment tools to measure circularity at various levels. Recent studies assessing the CE at the municipal level by using statistical data have highlighted the challenge of comparing indicators of differently populated and resourced areas. With existing methodologies, there remains a need for comprehensive approaches that integrate both qualitative and quantitative data to ensure fair and meaningful comparisons. In 2024, Latvia developed and conducted the first CE index at the municipal level. It was based on a self-assessment from municipal governments and citizens, with results calculated into a single index value and four category indices. By applying a mixed methods statistical analysis, this research aimed to compare CE performance, measured by the CE index, and selected socioeconomic and environmental variables between 7 cities and 36 counties or rural municipalities of Latvia. The research concluded that the CE performance is significantly shaped by socioeconomic and spatial factors, with population density and unemployment emerging as consistent predictors. Urban municipalities generally performed better, emphasizing the need for tailored, context-specific CE strategies. Full article

This study presents an extended dataset on educational quality covering 101 countries, from 1970 to 2023. While existing international assessments, such as the Programme for International Student Assessment (PISA) and Trends in International Mathematics and Science Study (TIMSS), offer valuable snapshots of student performance, their limited coverage across countries and years constrains broader analyses. To address this limitation, we harmonized observed test scores across assessments and imputed missing values using both linear interpolation and machine learning (Least Absolute Shrinkage and Selection Operator (LASSO) regression). The dataset included (i) harmonized test scores for 15 year olds, (ii) annual educational quality indicators for the 15–19 age group, and (iii) educational quality indexes for the working-age population (15–64). These measures are provided in machine-readable formats and support empirical research on human capital, economic development, and global education inequalities across economies. Full article

The purpose of this study is to analyze the temporal and spatial characteristics of urban expansion and its influencing factors in Shaanxi Province, China, as well as simulate future land use and predict the situation and development stage of urban expansion. An understanding of these factors is conducive to the coordinated development of the population, resources, and the economy; the optimization of the urban spatial layout; and the high-quality development of Shaanxi Province. Research methods: With IDRISI Selva17 and the expansion intensity index, the CA–Markov model was adopted to simulate and predict the land use type based on the land use data of Shaanxi Province from 2000 to 2020. The urban built-up areas in Shaanxi Province have been continuously expanding in the past 30 years, especially since 2010, when expansion slightly accelerated, and the expansion intensity changed, first rising and then falling. The Kappa index is as high as 0.70, which further confirms the accuracy of the land use spatial evolution prediction by the CA–Markov model. By combining the urban expansion index with the simulation model, this paper provides an in-depth analysis of the internal relationship between the historical evolution of and future trends in construction land expansion because of the high-quality coordinated development of Shaanxi Province and extends the research perspective with creative ideas. Full article

Peripheral rural coastal areas in Europe face persistent structural challenges, including demographic decline, aging populations, and the collapse of traditional sectors like fishing. These are further aggravated by environmental disasters, which weaken local economies. Public sector intervention is therefore essential, not only to provide an immediate response, but also to guarantee long-term solutions. This study explores the contribution of state-led tourism infrastructures to rural development and post-disaster recovery, taking the Parador Costa da Morte in Galicia as a case study. Based on data from a survey of parador guests, it identifies tourist profiles through factor and cluster motivation analysis. The parador’s impacts on the surrounding region are assessed by examining tourists’ travel patterns and spending behavior, as well as the analysis of secondary data on regional changes in tourism supply and demand. The results show widely differing motivations and, despite varying visitor profiles, the predominance of a tourism typology that generates significant local spillovers. These findings support the potential of high-quality, publicly driven tourism initiatives to stimulate the economy of structurally disadvantaged rural areas. The article ends with recommendations for tourism strategies aligned with local recovery and development goals. Full article

Rice (Oryza sativa L.) plays a pivotal role in the Brazilian economy, serving as a staple food for more than half of the world’s population and thereby contributing to global food security. Projections of future climate change scenarios indicate an increase in extreme weather events. Among climate variables that impact the development and productivity of irrigated rice, solar radiation is one of the most important in defining productive potential. Understanding the risks imposed on agricultural production by the occurrence of days with reduced luminosity availability is crucial for guiding adequate responses that mitigate the negative impacts of climate variability. Therefore, this study aimed to investigate the effect of shade on the metabolism and productivity of irrigated rice plants, with a specific focus on the synthesis of photosynthetic pigments, carbohydrate accumulation, invertase activity, and the nutritional status and grain yield of rice. For this, the study was conducted on the field rice cultivars IRGA 424 RI, BRS PAMPA, and BRS PAMPEIRA, which were subjected to 35% shading using black nylon netting installed when the plants reached the reproductive stage (R0). The restriction was maintained until the R4 stage, and later, from the R4 stage until the R9 stage. After the imposition of treatments, evaluations took place at the phenological stages R2, R4, R6, and R8. In shaded plants, a higher content of photosynthetic pigments and a lower accumulation of carbohydrates were observed, which was reflected in an increase in the activity of invertase enzymes. These conditions were able to potentiate effects on the nutritional status of the plants, in addition to reducing productivity and 1000-grain weight and increasing spikelet sterility, due to changes in the source–sink relationship, with effects more pronounced in cultivars BRS PAMPA and BRS PAMPEIRA during the R4–R9 period. Full article

Urban vitality serves as an important indicator for evaluating the level of urban quality development and sustainability. In response to a series of urban challenges arising from rapid urban expansion, enhancing urban quality and fostering urban vitality have become key objectives in contemporary urban planning and development. This study summarizes the spatial distribution patterns of urban vitality at the street and neighborhood levels in the central area of Qingdao, and analyzes their spatial characteristics. A 5D built environment indicator system is constructed, and the effects of the built environment on urban vitality are explored using the Optimal Parameter Geographic Detector (OPGD) and the Multi-Scale Geographically Weighted Regression (MGWR) model. The aim is to propose strategies for enhancing spatial vitality at the street and neighborhood scales in central Qingdao, thereby providing references for the optimal allocation of urban spatial elements in urban regeneration and promoting sustainable urban development. The findings indicate the following: (1) At both the subdistrict and block levels, urban vitality in Qingdao exhibits significant spatial clustering, characterized by a pattern of “weak east-west, strong central, multi-center, cluster-structured,” with vitality cores closely aligned with urban commercial districts; (2) The interaction between the three factors of functional density, commercial facilities accessibility and public facilities accessibility and other factors constitutes the primary determinant influencing urban vitality intensity at both scales; (3) Commercial facilities accessibility and cultural and leisure facilities accessibility and building height exert a positive influence on urban vitality, whereas the resident population density appears to have an inhibitory effect. Additionally, factors such as building height, functional mixing degree and public facilities accessibility contribute positively to enhancing urban vitality at the block scale. (4) Future spatial planning should leverage the spillover effects of high-vitality areas, optimize population distribution, strengthen functional diversity, increase the density of metro stations and promote the coordinated development of the economy and culture. Full article

With the rapid development of the social economy, human activities have increasingly disrupted water environments, and the continuous input of pollutants poses significant challenges for water environment management. Taking the Xiaoxingkai Lake basin as the study area, this paper develops a social–economic–water environment model based on the system dynamics methodology, incorporating subsystems for population, agriculture, and water pollution. The model focuses on four key indicators of pollution severity, namely, total nitrogen (TN), total phosphorus (TP), chemical oxygen demand (COD), and ammonia nitrogen (NH₃-N), and simulates the changes in pollutant loads entering the river under five different scenarios from 2020 to 2030. The results show that agricultural non-point sources are the primary contributors to TN (79.5%) and TP (73.7%), while COD primarily originates from domestic sources (64.2%). NH₃-N is mainly influenced by urban domestic activities (44.7%) and agricultural cultivation (41.2%). Under the status quo development scenario, pollutant loads continue to rise, with more pronounced increases under the economic development scenario, thus posing significant sustainability risks. The pollution control enhancement scenario is most effective in controlling pollutants, but it does not promote socio-economic development and has high implementation costs, failing to achieve coordinated socio-economic and environmental development in the region. The dual-reinforcement scenario and moderate-reinforcement scenario achieve a balance between pollution control and economic development, with the moderate-reinforcement scenario being more suitable for long-term regional development. The findings can provide a scientific basis for water resource management and planning in the Xiaoxingkai Lake basin. Full article

To evaluate water pollution status and sustainable development potential in the Huai River Basin, this study focused on the spatiotemporal evolution and influencing factors of the grey water footprint (GWF) across 35 cities in the basin from 2005 to 2020. This study quantifies the GWF from agricultural, industrial, and domestic perspectives and analyzes its spatial disparities by incorporating spatial autocorrelation analysis. The Tapio decoupling model was applied to explore the relationship between pollution and economic growth, and geographic detectors along with the STIRPAT model were utilized to identify driving factors. The results revealed no significant global spatial clustering of GWF in the basin, but a pattern of “high in the east and west, low in the north and south” emerged, with high-value areas concentrated in southern Henan and northern Jiangsu. By 2020, 85.7% of cities achieved strong decoupling, indicating improved coordination between the environment and economy. Key driving factors included primary industry output, crop sown area, and grey water footprint intensity, with a notable interaction between agricultural output and grey water footprint intensity. The quantitative analysis based on the STIRPAT model demonstrated that seven factors, including grey water footprint intensity and total crop sown area, exhibited significant contributions to influencing variations. Ranked by importance, these factors were grey water footprint intensity > total crop sown area > urbanization rate > population size > secondary industry output > primary industry output > industrial wastewater discharge, collectively explaining 90.2% of the variability in GWF. The study provides a robust scientific basis for water pollution control and differentiated management in the river basin and holds significant importance for promoting sustainable development of the basin. Full article

The restrictions on business and social activity during the COVID-19 pandemic have led to significant changes in consumption patterns worldwide. Such changes are causing structural shifts in the markets of goods and services, thus affecting regional resilience. In this article, we aim to assess the changing structure of the consumption of goods and services in highly urbanized Russian regions under the impact of the COVID-19 pandemic and to analyze its effects on the lifestyle of the population. According to our results, some Russian regions demonstrate a return to previous consumption levels, while others exhibit the emergence of new dynamics. The conclusion is made that COVID restrictions have invoked a paradigm shift in consumer behavior toward investment in self-development, safety, and comfort. This observation should be taken into account when developing strategies for the recovery growth of regional economies. Full article

Agriculture is the backbone of Punjab’s economy, and with much of India’s population dependent on agriculture, the requirement for accurate and timely monitoring of land has become even more crucial. Blending remote sensing with state-of-the-art machine learning algorithms enables the detailed classification of agricultural lands through thematic mapping, which is critical for crop monitoring, land management, and sustainable development. Here, a Hyper-tuned Deep Neural Network (Hy-DNN) model was created and used for land use and land cover (LULC) classification into four classes: agricultural land, vegetation, water bodies, and built-up areas. The technique made use of multispectral data from Sentinel-2 and Landsat-8, processed on the Google Earth Engine (GEE) platform. To measure classification performance, Hy-DNN was contrasted with traditional classifiers—Convolutional Neural Network (CNN), Random Forest (RF), Classification and Regression Tree (CART), Minimum Distance Classifier (MDC), and Naive Bayes (NB)—using performance metrics including producer’s and consumer’s accuracy, Kappa coefficient, and overall accuracy. Hy-DNN performed the best, with overall accuracy being 97.60% using Sentinel-2 and 91.10% using Landsat-8, outperforming all base models. These results further highlight the superiority of the optimised Hy-DNN in agricultural land mapping and its potential use in crop health monitoring, disease diagnosis, and strategic agricultural planning. Full article