Search Results (11,263)

One of the key problems humanity faces in this age of profound digitalization is globalization-related threats, which no longer affect just one country but pose a threat to a very large area, encompassing several or even a dozen countries, or, in the case of global warming, a threat to all of humanity worldwide. This topic inspired the investigation and verification of this threat in the Baltic Sea, along with other threats operating in the Baltic Sea region. This topic is highly topical, as estimates from maritime institutions indicate that the rate of sea level rise is an irreversible process, which, when combined with other threats, could lead to the degradation of the sea and the population living in the coastal zone. This led to the delegation clarifying the main objective of the article: to demonstrate the impact of potential global threats on the investment process in the Polish coastal belt. Based on this, an analysis of threats in the Baltic Sea region was conducted, preceded by a review of the literature and data from online resources, including data from industry portals in the maritime sector. This article presents a simulation of erosion-accumulation changes in selected areas of Poland’s Southern Baltic coast, focusing on the coastal real estate market and indicating the propensity to invest in these areas. Simulating erosion changes, using a cartographic base with a generated digital terrain model and interpolation tools to visualize the changes, represents an innovative approach to issues related to the outflow of investment land in the real estate market. This emphasizes the directionality of land changes, thus providing a predictive tool for decision-making and spatial planning in the coastal area. Full article

In the context of accelerating global urbanization and sustainable development challenges, impervious surfaces, as a key component of urban land cover, are significantly associated with regional economic development. This study takes Harbin, a typical cold region city, as a research object and constructs a three-level analytical framework of “land surface classification-economic simulation-mechanism analysis.” By innovatively integrating multi-source remote sensing, demographic, and economic data, the research addresses gaps in understanding urban sustainability in cold environments. An enhanced XGBoost algorithm was employed to achieve high-precision classification of ten land surface materials, resulting in a high overall accuracy. Furthermore, a gridded GDP spatialization model developed using high-resolution population data demonstrated superior performance compared to traditional methods. Machine learning-assisted analysis revealed that asphalt and metal surfaces are the most significant impervious materials driving economic output, reflecting the respective influences of transportation infrastructure and industrial agglomeration. Spatial pattern analysis indicates that Harbin’s impervious surfaces exhibit a lower fractal dimension and a distinct grid-like morphology compared to the typical subtropical city of Guangzhou, underscoring urban form adaptations to cold climatic constraints. The strong spatial coupling between gradients of GDP intensity and the attenuation of impervious surface density is quantitatively confirmed. This study provides a quantitative basis and a transferable technical framework for optimizing land use intensity and infrastructure planning in cold cities, thereby offering a scientific foundation for sustainable, intensive land utilization in climate-vulnerable urban systems. Full article

Understanding outdoor thermal environments at fine spatial scales is essential for developing climate-responsive urban and building design strategies. This study investigates the determinants of local air temperature deviations in Seoul, Korea, using high-resolution in situ sensor data integrated with multi-source urban and building information. Hourly temperature records from 436 road-embedded sensors (March 2024–February 2025) were transformed into relative metrics representing deviations from the network-wide mean and were combined with semantic indicators derived from street-view imagery—Green View Index (GVI), Road View Index (RVI), Building View Index (BVI), Sky View Index (SVI), and Street Enclosure Index (SEI)—along with land-cover and building attributes such as impervious surface area (ISA), gross floor area (GFA), building coverage ratio (BCR), and floor area ratio (FAR). Employing an eXtreme Gradient Boosting (XGBoost)–Shapley Additive exPlanations (SHAP) framework, the study quantifies nonlinear and interactive relationships among morphological, environmental, and visual factors. SEI, BVI, and ISA emerged as dominant contributors to localized heating, while RVI, GVI, and SVI enhanced cooling potential. Seasonal contrasts reveal that built enclosure and vegetation visibility jointly shape micro-scale heat dynamics. The findings demonstrate how high-resolution, observation-based data can guide climate-responsive design strategies and support thermally adaptive urban planning. Full article

Urban Heat Islands (UHIs) are urbanized areas that experience significantly higher temperatures than their surroundings, contributing to thermal discomfort, increased air pollution, heightened public health risks, and greater energy demand. In Bhutan, where urban expansion is concentrated within narrow valley systems, the formation and intensification of UHIs present emerging challenges for climate-resilient urban development. Thimphu, in particular, is experiencing rapid urban growth and densification, making it highly susceptible to UHI effects. Therefore, the aim of this study was to evaluate and simulate UHI conditions for Thimphu Thromde. We carried out the simulation using a GIS, multi-temporal Landsat imagery, and an Artificial Neural Network model. Land use and land cover classes were mapped through supervised classification in the GIS, and surface temperatures associated with each class were derived from thermal bands of Landsat data. These temperature values were normalized to identify existing UHI patterns. An Artificial Neural Network (ANN) model was then applied to simulate future UHI distribution under expected land use change scenarios. The results indicate that, by 2031, built-up areas in Thimphu Thromde are expected to increase to 72.82%, while vegetation cover is projected to decline to 23.52%. Correspondingly, both UHI and extreme UHI zones are projected to expand, accounting for approximately 14.26% and 6.08% of the total area, respectively. Existing hotspots, particularly dense residential areas, commercial centers, and major institutional or public spaces, are expected to intensify. In addition, new UHI zones are likely to develop along the urban fringe, where expansion is occurring around the current hotspots. These study findings will be useful for Thimphu Thromde authorities in deciding the mitigation measures and pre-emptive strategies required to reduce UHI effects. Full article

The mineral deposits in Namibia’s Erongo region are renowned and frequently associated with complex geological environments, including calcrete-hosted paleochannels and hydrothermal alteration zones. Mineral extraction is hindered by high operational costs, restricted accessibility and stringent environmental regulations. To address these challenges, this study proposes an integrated approach that combines satellite remote sensing and machine learning to map and identify mineralisation-indicative zones. Sentinel 2 Multispectral Instrument (MSI) and Landsat 8 Operational Land Imager (OLI) multispectral data were employed due to their global coverage, spectral fidelity and suitability for geological investigations. Normalized Difference Vegetation Index (NDVI) masking was applied to minimise vegetation interference. Spectral indices—the Clay Index, Carbonate Index, Iron Oxide Index and Ferrous Iron Index—were developed and enhanced using false-colour composites. Principal Component Analysis (PCA) was used to reduce redundancy and extract significant spectral patterns. Supervised classification was performed using Support Vector Machine (SVM), Random Forest (RF) and Maximum Likelihood Classification (MLC), with validation through confusion matrices and metrics such as Overall Accuracy, User’s Accuracy, Producer’s Accuracy and the Kappa coefficient. The results showed that RF achieved the highest accuracy on Landsat 8 and MLC outperformed others on Sentinel 2, while SVM showed balanced performance. Sentinel 2’s higher spatial resolution enabled improved delineation of alteration zones. This approach supports efficient and low-impact mineral prospecting in remote environments. Full article

While artisanal gold mining (AGM) has been credited as a sector that sustains many households in Zimbabwe, it has at the same time been criticized as the chief driver of ecological degradation and social vulnerability. This study qualitatively examines the environmental and socioeconomic impacts of AGM by conducting in-depth interviews with miners, residents, and policymakers across six central mining districts. The study findings indicate that the use of mercury has resulted in severe contamination of water bodies, while clearing land to pave the way for mining has led to severe deforestation, loss of biodiversity, and declining agricultural productivity due to the loss of fertile soils. It was also found that most AGMs were unregulated, and their unregulated operations have intensified health risks, social inequality, and land-use conflicts with the local community. This study provides an insight into how dependence on AGM has perpetuated a cycle of ecological degradation and poverty among many Zimbabweans. The study, therefore, attempts to combine community narratives with policy analysis, thereby proposing a framework for sustainable AGM in Zimbabwe. This involves advocating for the use of environmentally friendly technologies and promoting participatory environmental governance among all key stakeholders. The study contributes to achieving a balance between economic benefits and environmental management by advancing the discourse on sustainable development and community resilience in resource-dependent economies. Full article

Small Island Developing States (SIDS) face ongoing challenges in balancing agricultural sustainability with economic growth due to limited land resources, rapid urbanisation, climate change, and reliance on food imports. This study explores the evolution of land use and the future of agriculture in Mauritius from 2002 to 2022, using satellite imagery, policy reviews, and stakeholder interviews. Findings show a 9% decrease in agricultural and non-agricultural vegetation cover, alongside a doubling of built-up areas from 10% to 20%, indicating continued land conversion pressures. The analysis highlights major barriers to agricultural sustainability, including declining food self-sufficiency, an ageing farming population, and slow movements towards sustainable practices caused by low profitability and weak institutional support. Diverging priorities among government agencies, sugar companies, smallholder farmers, and NGOs further hinder coordinated policy efforts. To address these challenges, the study identifies strategies for aligning economic and environmental goals through integrated land-use planning, boosting productivity, and providing targeted support for sustainable ecological farming systems. Policy recommendations include protecting agricultural land, encouraging agroecological practices, alleviating labour shortages, and promoting multi-stakeholder engagement within policy development. Overall, this research enhances understanding of land-use dynamics and agricultural resilience in SIDS, offering practical insights for policymakers and practitioners working towards sustainable food systems amid spatial and climatic constraints. Full article

Wildfires increasingly threaten Mediterranean landscapes, particularly in regions like Attica, Greece, where urban sprawl, agricultural abandonment, and climatic conditions heighten the risk at the Wildland–Urban Interface (WUI). The Mediterranean basin, recognized as one of the global wildfire “hotspots”, has witnessed a steady increase in both fire severity, frequency, and burned area during the last four decades, a trend amplified by urban sprawl and agricultural land abandonment. This study represents the first integrated, region-wide mapping of the WUI and associated wildfire risk in Attica, the most densely urbanized area in Greece and one of the most fire-exposed metropolitan regions in Southern Europe, utilizing advanced techniques such as Earth Observation and GIS analysis. For this purpose, various geospatial datasets were coupled, including Copernicus High Resolution Layers, multi-decadal Landsat fire history archive, UCR-STAR building footprints, and CORINE Land Cover, among others. The research delineated WUI zones into 40 interface and intermix categories, revealing that WUI encompasses 26.29% of Attica, predominantly in shrub-dominated areas. An analysis of fire frequency history from 1983 to 2023 indicated that approximately 102,366 hectares have been affected by wildfires. Risk assessments indicate that moderate hazard zones are most prevalent, covering 36.85% of the region, while approximately 25% of Attica is classified as moderate, high, or very high susceptibility zones. The integrated risk map indicates that 37.74% of Attica is situated in high- and very high-risk zones, principally concentrated in peri-urban areas. These findings underscore Attica’s designation as one of the most fire-prone metropolitan regions in Southern Europe and offer a viable methodology for enhancing land-use planning, fuel management, and civil protection efforts. Full article

The long-standing separation of agro-pastoral relations has adversely affected the agricultural economy and ecology, hindering sustainable agricultural development. The process of reconstructing agro-pastoral relations involves moving from separation to reintegration. To further verify the scientific validity of reconstructing agro-pastoral relations to improve economic and ecological benefits and enhance the capacity for sustainable agricultural development in the major corn-producing areas of Northeast China, this study used survey data from 521 sample farmers in Jilin Province, China, collected during the agricultural production cycle from 2020 to 2022. Using an endogenous switching regression (ESR) model and a counterfactual scenario, the integrated crop–livestock family farm (ICFF) model was shown to have a comparative advantage in improving economic and ecological benefits. The ICFF model can serve as a foundation for reconstructing agro-pastoral relations, thereby enhancing sustainable agricultural development capacity. Heterogeneity analysis indicates that larger-scale cultivated land, intensive cultivated land management, and higher education have a more significant impact on farmers’ choice of the ICFF model. To promote the restructuring of agro-pastoral relations through the ICFF model, farmers should be encouraged and supported to standardize the transfer of farmland, engage in livestock farming according to the principle of land-based livestock management, implement large-scale and intensive management, improve agricultural production technologies and improved varieties, strengthen publicity on the positive role of integrated crop-livestock management, and improve the financial support system. Full article

Community-managed forests within agroforestry landscapes are vital for both carbon sequestration and agricultural sustainability. This study assesses the Hariyali Community Forest (HCF) in western Nepal, emphasizing its role in carbon storage within a Sal (Shorea robusta)-dominated lowland forest containing diverse native and medicinal species. Stratified field inventories combined with satellite-derived biomass and land-use/land-cover data were used to quantify carbon stocks and spatial trends. In 2022, the mean aboveground carbon density was 165 tC ha⁻¹, totaling approximately 101,640 tC (~373,017 tCO₂e), which closely matches satellite-based trends and indicates consistent carbon accumulation. Remote sensing from 2015–2022 showed a net tree cover gain of 427 ha compared to a 2000 baseline of 188 ha, evidencing effective community-led regeneration. The 615 ha Sal-dominated landscape also sustains agroforestry, small-scale horticulture, and subsistence crops, integrating livelihoods with conservation. Temporary carbon declines between 2020 and 2022, linked to localized harvesting and management shifts, highlight the need for stronger governance and local capacity. This study, among the first integrated carbon assessments in Nepal’s lowland Sal forests, demonstrates how community forestry advances REDD+ (Reducing Emissions from Deforestation and Forest Degradation, and the role of conservation, sustainable management of forests, and enhancement of forest carbon stocks in developing countries) objectives while enhancing rural resilience. Linking field inventories with satellite-derived biomass and land-cover data situates community forestry within regional environmental change and SDG (Sustainable Development Goals) targets (13, 15, and 1) through measurable ecosystem restoration and livelihood gains. Full article

Land use change is a critical factor influencing regional carbon emissions, and understanding its spatiotemporal variability is essential for supporting science-based emission-reduction strategies. In this study, we constructed an improved measurement framework by integrating high-resolution land use data, gridded anthropogenic carbon emission data, multi-source remote sensing indicators, and socioeconomic variables to quantify land use carbon emissions (LUCEs) in the Chengdu–Chongqing Urban Agglomeration (CCUA) from 2000 to 2022. We analyzed the temporal trends and spatial clustering of carbon emissions using the Mann–Kendall (MK) trend test and global/local Moran’s I statistics, and further explored the driving mechanisms through the Geodetector (GD) model, including both single-factor explanatory power and two-factor interaction effects. The results show that total LUCEs in the CCEC increased continuously during the study period, with significant spatial clustering characterized by high–high emission hotspots in the core areas of Chengdu and Chongqing and low–low clusters in western mountainous regions. Socioeconomic factors played a dominant role in shaping emission patterns, with construction land proportion, nighttime light intensity, and population density identified as the strongest drivers. Interaction detection revealed nonlinear enhancement effects among key socioeconomic variables, indicating an increasing spatial lock-in of human activities on carbon emissions. These findings provide scientific evidence for optimizing land use structure and formulating region-specific low-carbon development policies in rapidly urbanizing megaregions. Full article

The advent of digital transformation, social learning, and the increasing use of artificial intelligence is driving requisite changes in the development of data centres, which are buildings designed to process and store data. Green innovation is an integral component of the sustainable development of data centre units. Solutions utilising green and blue infrastructure in data centres are being currently introduced with the objective of optimising energy consumption and reducing energy demand. The primary aim of the research is to analyse the utilisation of biomass production and blue–green infrastructure in data centres. The article provides a consolidated set of key performance indicators (KPIs): energy efficiency, water use, waste heat utilisation, renewable energy integration, hourly carbon-free matching, embodied carbon, and land use impacts, that can be used to compare different data centre designs. Traditional PUE-centric evaluations are broadened by added metrics such as biodiversity/green area, intensity, and 24/7 CFE, reflecting the broader, multi-dimensional sustainability challenges highlighted in the current literature. Twelve international case studies described in the literature were compared and the feasibility of the Polish pilot project in Michalowo was assessed to illustrate specific cases related to energy-saving solutions and the use of renewable energy sources in data centres. Full article

Maize–alfalfa intercropping is practiced in Northeast China to improve land productivity and forage production. However, competition between the two crops can reduce system performance, which calls for an emphasis on optimal row ratio. Hence, the current study evaluated the effects of diverse maize–alfalfa row ratio configurations (1:1, 2:1, 2:2, 3:1, 3:2, and 3:3) on resource-use efficiency, physiological traits, and yield performance. It was noted that the mono-cropping system had higher physiological and agronomic values for both crops. With regard to the intercropping configuration, the 2:2 steadily outperformed all other intercropping row ratios. Whereas alfalfa grew tallest in 2:2, maize plant height peaked under the 3:1. Photosynthetic rate and chlorophyll content were highest under 2:2, for both crops. The yield results indicated that alfalfa achieved maximum forage and biomass, whereas maize performed best under a 3:1 configuration. Outstandingly, under the 2:2 ratio, the cumulative system yield exceeded alfalfa mono-cropping by 55% and maize mono-cropping by 56–57%. There was superior complementarity and land-use advantage under 2:2, as indicated by the highest resource-use indicators of LER (land equivalent ratio), LEC (land equivalent coefficient), SPI (system productivity index), and K (crowding index). Competitive Indices showed that competition was more balanced under 2:2, with maize dominating in systems with higher maize proportions. Overall, the 2:2 row ratio provided the best balance of reduced competition and enhanced complementarity, offering a more efficient and sustainable maize-alfalfa intercropping strategy. Full article

Optimizing resource allocation is crucial for enhancing Total Factor Productivity (TFP). This study investigates the impact of differentiated industrial land supply (DILS) on industrial Total Factor Productivity (ITFP), a topic essential for optimizing territorial spatial layouts and promoting high-quality industrial development. Using panel data from 282 Chinese cities (2007–2021) and a Spatial Durbin Model (SDM), we analyze the spatiotemporal effects of this factor. The results indicate a weakening trend in DILS over time, with a spatial pattern of lower intensity in the east and higher intensity in the west, while ITFP shows an upward trend, with higher levels in the east. Nationally, increased DILS impedes ITFP growth, a finding with robust implications for alternative approaches. This impact demonstrates significant spatiotemporal heterogeneity: at the macro-scale, eastern China shows an inverted U-shape, while the central and western regions exhibit negative impacts. At the meso-scale, the Yangtze River Economic Belt shows negative effects, while the Yellow River Basin displays an inverted U-shape. At the micro-scale, major city clusters show varied relationships (inverted U-shaped, positive, or negative). We conclude that DILS generally hinders ITFP, with effects intensifying and varying significantly across narrowing spatial scales, underscoring the need for region-specific land policies to support high-quality industrial development. This study enriches our theoretical understanding of how resource allocation affects ITFP and provides practical guidance for optimizing industrial land use. Full article

Background: Overweight and obesity frequently occur as comorbid conditions in people with asthma, particularly among those with poor disease control or more severe clinical profiles. However, the extent to which exposure to grey spaces may influence the link between overweight/obesity and asthma remains insufficiently explored. Aim: To assess the association between overweight/obesity and asthma in an Italian general population sample and the influence of residential grey space on such relationship. Methods: A total of 2841 individuals (54.7% women; age range 8–97 years) residing in Pisa, Italy, were surveyed in 1991–1993 using a standardised questionnaire on health conditions and relevant risk factors. The proportion of grey space within a 1000 m buffer around each participant’s home was quantified using the CORINE Land Cover database. Multinomial logistic regression models were applied to assess the association between asthma status (1. asthma symptoms without doctor diagnosis, 2. diagnosis ± symptoms, 3. no diagnosis/symptoms − reference category) and overweight/obesity, adjusting for sex, age, educational level, smoking, physical activity and grey space exposure. Analyses were further stratified according to high vs. low grey space exposure (above vs. below 63%, corresponding to the second tertile). Mediation and interaction analyses were also performed. Results: The prevalence of asthma diagnosis ± symptoms, overweight and obesity was 18.7%, 35.8% and 12.8%, respectively. In the full sample, asthma symptoms without medical diagnosis were positively associated with overweight (Odds Ratio—OR 1.43; 95% Confidence Interval—CI 1.08–1.88), obesity (OR 1.99; 95% CI 1.38–2.88) and residential grey space (OR 1.06; 95% CI 1.01–1.13). Stratified models showed that, among participants with high exposure to grey areas, asthma symptoms were linked to both overweight (OR 2.03; 95% CI 1.29–3.19) and obesity (OR 2.57; 95% CI 1.36–4.86). In individuals with low grey space exposure, an association was observed only with obesity (OR 1.80; 95% CI 1.15–2.82). Mediation analysis did not reveal any weight-related effect modification. Measures of additive interaction indicated that 32% of asthma symptoms were attributable to the interaction between excess body weight and high grey space exposure. Conclusions: This study showed that overweight/obesity and grey space exposure are factors associated with asthma symptoms. These findings advocate for an early identification of overweight/obese-asthma symptom phenotype since it may help prevent the onset or worsening of asthma, particularly in urban environments. These insights highlight the need for integrated public health and urban planning strategies to promote more sustainable, health-supportive environments. Full article