Climate change constitutes an escalating global crisis that features significant implications for ecosystems, water resources, agriculture, and industry. Understanding its causes and potential impacts is crucial for formulating effective mitigation and adaptation strategies. This study systematically reviews recent scientific findings and scenario-based climate models to assess both the natural and anthropogenic (human-induced) factors that are driving climate change. It highlights the rising levels of atmospheric carbon, the increasing global temperatures, and the resulting environmental challenges (including extreme weather events, rising sea levels, and biodiversity losses). By drawing on more than 40 regional and global studies, this paper synthesizes results from various GCMs – particularly those that are applied in arid and semi-arid regions (where climate vulnerabilities are most pronounced). The findings revealed disparities in regional vulnerability and highlighted the necessity of selecting accurate GCMs for effective adaptation planning. By synthesizing the existing literature and climate models, this paper aims to establish a comprehensive reference for researchers and policymakers, thus facilitating informed decisionmaking that is geared toward a sustainable future.

Landslide identification and susceptibility maps play vital roles in supporting planners and decision-makers who manage disaster risks. By providing accurate information, these maps significantly contribute to minimizing the potential losses of life and property. To create effective landslide-susceptibility models, it is essential to incorporate a combination of terrain characteristics and meteorological factors, thus enhancing our understanding and preparedness for such events. This study presents a comparative analysis of three kernel functions (linear, polynomial, and RBF) of an support vector classifier (SVC) accompanied by a grid-search in order to determine optimal hyper-parameter settings. The primary objective of this methodological framework is to ensure accurate and reliable predictions for the generation of landslide-susceptibility maps in the South District of Sikkim, India. In this investigation, 14 conditioning factors were considered, including aspect, distance to streams, distance to roads, drainage density, elevation, lithology, land use/land cover (LULC), normalized difference vegetation index (NDVI), plan curvature, profile curvature, rainfall, slope, soil type, and earthquake susceptibility. The performances of the models were evaluated using a range of metrics, including the training score, testing score, kappa, sensitivity, specificity, accuracy, and area under the curve (AUC). Optimal hyper-parameter tuning for each SVC kernel was conducted through a grid-search approach. The results indicated that the SVC_poly and SVC_rbf models surpassed the linear model, achieving accuracy and AUC values of 0.907 and 0.908, respectively, in developing susceptibility maps. Consequently, both the SVC_poly and SVC_rbf models were identified as the most reliable and effective tools for landslide-susceptibility mapping in this study, making them optimal choices for predictive analyses in this domain.

Unpredictable rainfall caused by climate change and pollution directly impacts groundwater demand, making the exploitation of groundwater reserves necessary. To achieve this, a study in the synclinal basin of Essaouira (Western High Atlas) used GIS, remote sensing, and the Multi-Influencing Factors (MIF) method, to identify areas ideal for the installation of productive wells. An overlay analysis created a groundwater potential zone (GWPZ) map, showing 30% of the basin with high potential, 51% with moderate potential, and 19% with low to very low potential. The groundwater potential zone map was validated using geophysical surveys, piezometric data, and well water levels, showing a 69.3% prediction accuracy with the ROC curve.

This research reports an error analysis of close-range measurements from a Stonex X300 laser scanner in order to address range uncertainty behavior based on indoor experiments under fixed environmental conditions. The analysis includes procedures for estimating the precision and accuracy of the observational errors estimated from the Stonex X300 observations and conducted at intervals of 5 m within a range of 5 to 30 m. The laser 3D point cloud data of the individual scans is analyzed following a roughness analysis prior to the implementation of a Levenberg–Marquardt iterative closest points (LM-ICP) registration. This leads to identifying the level of roughness that was encountered due to the range-finder’s limitations in close-ranging as well as measurements that were obtained from extreme incident angle signals. The measurements were processed using a statistical outlier removal (SOR) filter to reduce the noise impact toward a smoother data set. The geometric differences and the RMSE values in the 3D coordinate directions were computed and analyzed, which showed the potential of the Stonex X300 measurements in close-ranging following a careful statistical analysis. It was found that the error differences in the vertical direction had a consistent behavior when the range increased, whereas the errors in the horizontal direction varied. However, it is more common to produce errors in the vertical direction as compared to the horizontal one.

Digital generalization of spatial data has been the goal of the research in many research centers around the world. This article presents the evolution of cartographic generalization, drawing the reader’s attention to the change of its nature from analog to digital. Despite the passage of time and developing technologies, scientists have unfortunately yet to develop a uniform automatic generalization algorithm. One of the factors that hinder this process is the high complexity and complication of the whole process. The article is an attempt to answer this problem and addresses the issue of digital cartographic generalization by creating a proposal of thresholds and stages of cartographic generalization depending on the ratios of the numbers of points of generalized objects. The publication attempts to examine the possibility of applying an objective criterion of drawing recognition by examining digital generalization algorithms and setting its thresholds. The practical aim of the publication is to present generalization thresholds on the example of Chrobak’s algorithm. The proposal to make the selection of generalization thresholds dependent on the percentage share of points is a solution that is as simple to use as it is to implement. The method of defining intervals based on the three-sigma rule is a solution that guarantees that the obtained results will be characteristic of the probability density function of the normal distribution, which will define individual intervals most objectively.

Renewable energy has the potential to power the global economy and effective business models will significantly aid this goal, being among the most critical factors in spurring expansion in the energy industry. This paper reviews articles that discuss business models in the renewable energy sector. Longterm economic, social, and ecological stability is concerned. Previous studies have neglected the environmental sustainability of renewable energy business models, focusing on their technical, social, and economic aspects, primarily for energy access. The business models for solar home and pico systems relied heavily on lowering costs through creative payment plans for customers to be commercially viable. The demand for mini-grids requires end users to launch businesses that can leverage electrification initiatives to be commercially viable. The success of a mini-grid depends on the average consumption and revenue per user. Affordability, unmet energy needs, low electricity demand, lack of financing, unfamiliar business models, and immature markets have impeded energy access in Indonesia. Our analysis revealed that future studies in this field must include environmental sustainability to provide a complete picture for decision-makers. Renewable energy needs in Indonesia can be achieved through the sustainability domain, policy makers can consult this evidence set.

The design and installation of solar panels on the roofs of urban buildings often require consideration of the specific spatial conditions that affect their efficiency. The primary purpose of this work is to develop a procedure for designing and optimizing photovoltaic installations using geomatics methods and specific tools of GIS and CAD systems. The roof of the historic building A2, which is a part of the Poznań University of Technology campus, was selected as the tested object. Solar radiation modelling and determination of suitability zones were performed using SEBE (Solar Energy on Building Envelopes) in QGIS. Possible options for the placement of photovoltaic modules on the roof were simulated with CAD technique in the web-based HelioScope software. The results of the simulation show that the current roof area can generate electrical power of 99.9 MWh/year. The proposed methodology is universal for photovoltaic installations on built-up roofs and can be applied to other buildings and, consequently, the results obtained can be used to improve the content of the solar data urban geoportal.

Ferrous metallurgy enterprises have a negative impact on the air, soil, and water environment. The activities of metallurgy enterprises include a wide range of production processes (from the extraction of metals from ores to obtaining finished products) and is associated with the formation of a large amount of waste. Industrial wastewater discharge is the main source of aquatic area pollution. An assessment of the impact of wastewater discharged from the Azovstal Iron & Steel Works metallurgical plant on the state of the coastal waters of the Sea of Azov near Mariupol (Ukraine) is presented in the article. The assessment was carried out in accordance with the current Ukrainian legislation and the adopted methodology for water state assessment. The assessment was based on the available monitoring data of sea water in the area of wastewater discharges in the period 2016–2020. The assessment was carried out using the aggregated numerical indices, as well as taking into account the “limiting criterion principle”. Such a methodological approach allowed for a comprehensive assessment of the sea water quality class as well as its sanitary and ecological condition. The results of the assessment allowed us to ascertain the negative impact of industrial wastewater from the metallurgical plant on the coastal zone of the Azov Sea, which made it unsuitable for communal and recreational purposes.

Geospatial and multi-criteria decision-making techniques are applied to process and analyse datasets for determining suitable areas for multiple utilityscale solar photovoltaic farms in the city of Akure, Ondo State, southwestern Nigeria. Data processed include local electric power distribution system data, Shuttle Radar Topographic Mission elevation data, Landsat 8 and solar global horizontal irradiance. Multi-criteria decision-making techniques adopted are the analytical hierarchy process, reclassification, and overlay. These techniques were carried out considering criteria for siting solar photovoltaic farms. Criteria considered in this study are climate, topography, economic, environmental impact operational and technical while sub-criteria are solar global horizontal irradiance, slope, proximity and land cover. The outcome of the study shows that the study area covering a total extent of ~33,200 ha, 15%, 8%, 13% and 64% are highly suitable, suitable, moderately suitable, and unsuitable respectively for siting utility-scale solar photovoltaic farms within the study area. The study reveals the potential of multiple utility-scale solar photovoltaic farms in the study area. However, the proportions of areas suitable for solar photovoltaic farms are quite lower compared to findings from similar studies conducted in northwestern Nigeria. The study recommends solar photovoltaic sources as an alternative energy source in and around the study area.

One of the great challenges of achieving the shared vision of the 2030 Agenda for Sustainable Development is having high-quality, timely, comparable, and accessible data that allows to measure and report progress on the Sustainable Development Goals (SDG). Hence, in many countries, geospatial information (including Earth observation) and algorithms implemented in cloud computing platforms have become important tools to monitor indicators of the SDG thanks to their broad accessibility and global coverage. However, emerging countries still face barriers to the implementation of technologies to manage the large amounts of EO data. This article aims to show the advantages of satellite-based EO in the measurement of SDG indicators, as well as challenges emerging countries face in the use of these technological tools. It addresses why the open-source tool Open Data Cube (ODC) should be seen as a response to the said challenges. Finally, there is a description regarding the experience of Mexico with the use and application of this tool for the measurement of SDG indicators, from the development and implementation of the Mexican Geospatial Data Cube (MGDC) to the results obtained from its application in the support for the measurement of SDG indicators 6.6.1 Change in the extent of water-related ecosystems over time and 15.1.1 Forest area as a proportion of total land area.