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In September 2014, Kashmir witnessed a catastrophic flood resulting in a significant loss of lives and property. Such massive losses could have been avoided if any structural support such as dams were constructed in the Jhelum basin, which has a history of devastating floods. The GIS-based multicriteria analysis (MCA) model provided three suitability zones for dam locations. The final suitable dam sites were identified within the highest suitability zone based on topography (cross-sections), stream order, high suitable zone, minimum dam site interval, distance from roads, and protected area distance to the dam site. It was discovered that 10.98% of the total 4347.74 km2 area evaluated falls in the high suitability zone, 28.88% of the area falls in the medium suitability zone, and 60.14% of the area falls in the low suitability zone. Within the study area, four viable reservoir sites with a holding capacity of 4,489,367.55 m3 were revealed.

Power distribution inefficiencies and heavy reliance on diesel generators remain key challenges in Palawan. As one of the major coconut producers in the Philippines, Palawan’s coconut residues present significant biomass energy potential to address energy issues while promoting clean energy production. This study evaluates the theoretical and available energy potential of coconut residues in Palawan and identifies biomass collection points (BCPs) based on their biomass collection capacity within a 0.7-km radius and proximity to the road network. These points are used as supply sources, with candidate sites as demand points, and the road network is incorporated to optimally locate biomass conversion facilities using Geographic Information System (GIS) and spatial analysis. Results indicate an estimated theoretical potential of 15465.919 metric tons and an available potential of 85.173 MJ/ha, sufficient to supply one biomass energy facility with an approximate capacity of 1.5 MW. Twelve BCPs were identified in Southern Palawan: two in Sofronio Espanola, three in Rizal and Brooke’s Point, and four in Quezon. Additionally, eight BCPs were identified in Cuyo Island: three in Cuyo and five in Magsaysay. There are 96 candidate sites determined as potential locations for biomass conversion facilities. The optimal site for a biomass conversion facility was identified in Quezon, Palawan. In conclusion, this research effectively employed GIS and the BCP framework to locate biomass collection points and strategically determine the optimal site for a biomass conversion facility.

A sustainable biomass supply chain would require not only an effective and fluid transportation system with a reduced carbon footprint and costs, but also good soil characteristics ensuring durable biomass feedstock presence. Unlike existing approaches that fail to account for ecological factors, this work integrates ecological as well as economic factors for developing sustainable supply chain development. For feedstock to be sustainably supplied, it necessitates adequate environmental conditions, which need to be captured in supply chain analysis. Using geospatial data and heuristics, we present an integrated framework that models biomass production suitability, capturing the economic aspect via transportation network analysis and the environmental aspect via ecological indicators. Production suitability is estimated using scores, considering both ecological factors and road transportation networks. These factors include land cover/crop rotation, slope, soil properties (productivity, soil texture, and erodibility factor) and water availability. This scoring determines the spatial distribution of depots with priority to fields scoring the highest. Two methods for depot selection are presented using graph theory and a clustering algorithm to benefit from contextualized insights from both and potentially gain a more comprehensive understanding of biomass supply chain designs. Graph theory, via the clustering coefficient, helps determine dense areas in the network and indicate the most appropriate location for a depot. Clustering algorithm, via K-means, helps form clusters and determine the depot location at the center of these clusters. An application of this innovative concept is performed on a case study in the US South Atlantic, in the Piedmont region, determining distance traveled and depot locations, with implications on supply chain design. The findings from this study show that a more decentralized depot-based supply chain design with 3depots, obtained using the graph theory method, can be more economical and environmentally friendly compared to a design obtained from the clustering algorithm method with 2 depots. In the former, the distance from fields to depots totals 801,031,476 miles, while in the latter, it adds up to 1,037,606,072 miles, which represents about 30% more distance covered for feedstock transportation.

The world is progressing towards sustainable, eco-friendly, recyclable materials to enhance the circular economy and mitigate the issues of carbon footprint, overburdened landfills, and waste of natural resources. As increasing greenhouse gas (GHG) emissions are a major contributor towards climate change and given that the construction industry is one of the major producers of GHG emissions, it is crucial to meticulously quantify and lower its emissions, especially in the context of developing countries. This research presents a novel framework by combining advanced tools i.e., building information modeling (BIM), life-cycle assessment (LCA), geographic information systems (GISs), and quantification of embodied emissions to optimize construction’s design, material-selection, operations, maintenance, and waste-management processes. The effectiveness of the proposed approach has been demonstrated with the help of a real-world case study in Islamabad, Pakistan. A building model has been generated using BIM, and a comprehensive LCA has been conducted. Additionally, GIS tools have been utilized to identify the locations and accessibility of available-waste-management facilities. Based on this data, embodied emissions related to handling and transportation of waste material to disposal facilities have been computed using mathematical analyses. Furthermore, targeted mitigation strategies have been proposed and an optimized route has been designed using GIS-based route-optimization tools along the suggested facility centers in the Islamabad region. The case study has been reassessed with alleviation strategies, and the results show that 29.35% of the materialization stage, 16.04% of the operational stage, and 21.14% of the end-of-life-phase GHG emissions can be effectively reduced. Hence, pre-evaluating the environmental degradation caused by construction projects throughout their life cycle might offer an opportunity to comprehend and reduce prospective environmental impacts.

India is the largest producer, consumer and exporter of turmeric (Curcuma longa L.). The Lakadong variety of turmeric is endemic to Jaintia Hills of Meghalaya, India. It is considered as the best quality turmeric containing 7.5% curcumin, which is about three times higher than the other varieties (2–3%). This study identifies potential sites for turmeric cultivation in Jaintia Hills using geospatial techniques, viz. analytical hierarchical process (AHP) and weighted overlay analysis (WOA). WOA identified a total of 162,263.70 ha suitable for the expansion of Lakadong variety of turmeric in Jaintia Hills, of which 18% was highly suitable, 31% moderately suitable and 32% was marginally suitable. In the case of AHP, 21% area was found to be highly suitable, 25% moderately suitable and 45% marginally suitable.

Construction and Demolition Waste Management (CDWM) includes collecting, transporting, processing, and disposing construction and demolition (C&D) waste, where collection and transportation of bulky and voluminous C&D waste contribute significantly to economic and environmental impacts. Transfer station (TS) being a link between various waste management (WM) facilities plays a significant role in collection and transportation of waste. Thus, locating TS at suitable site can help in reducing the overall impacts. Employment of Geographic Information System (GIS) analysis tools in CDWM is a powerful strategy for site suitability study. A case study in Coimbatore, India, is presented in this study using GIS-based multi-criteria analysis for locating C&D waste TS. The criteria for site suitability analysis are chosen based on literature review, regulations, and experts’ opinions. Weights of the chosen criteria are estimated using analytic hierarchy process (AHP), and the final suitability map is created by weighted overlay analysis (WOA) in GIS environment. Results provide first-hand information for local decision makers to locate C&D waste transfer station in the chosen study region and report that 12% of the entire area is “highly suitable” for transfer station location.

The forecasted electricity demand in Saudi Arabia may be around 120 GW/year by 2032. As per the latest government announcement, Saudi Arabia is aiming to install 57.5 GW of renewable energy capacity by 2030. In this study, firstly, a wind map is developed based on the historical wind data, recorded over a 39-year period, followed by the development of the geographic information system (GIS)-based multi-criteria decision making (MCDM) model for suitable wind farm site selection for Hijaz, the western region of Saudi Arabia. This region is selected as it has a population density of around 25 per sq. km, the highest in Saudi Arabia. For the model, data from various ecological, environmental, and socioeconomic criteria are considered. Finally, the optimization of the wind farm layout on the identified suitable region of 5.5 km × 4 km is performed using the deep-array wake model, DAWM. The optimized layout has locations for 30 wind turbines of 3 MW rated capacity. This optimization process minimizes energy losses and costs and maximizes power production. The net and gross energy production from the wind farm are expected to be 143 GWh and 156 GWh, respectively, with an array loss of 8.25% at a cost of energy of USD 65.66 per MWh, and a capacity factor of 17.7%. The cost calculations include the capital cost of constructing the access roads and a complete collector system with two substations. The optimized turbine positions in the layout have a major and minor axis separation of 1680 m and 448 m, respectively.

This study presents a techno-economic and environmental analysis of hybrid renewable energy systems to identify the optimal configuration for supplying the planned 850 MW renewable energy plant in Yanbu city, Saudi Arabia. Ten grid-connected system designs combining photovoltaic (PV), wind turbine (WT), and battery storage were simulated and optimized using the HOMER Grid software (1.10.2 pro edition). A site suitability analysis was conducted to evaluate potential locations based on climatic, topographic, and infrastructure-related factors. A sensitivity analysis considered variations in solar irradiation, wind speed, temperature, load demand, and economic parameters. The results showed that the PV-only system with an 850 MW capacity achieved the lowest net present cost (NPC) of USD 201 million and levelized cost of energy (LCOE) of 0.0344 USD/kWh, making it the most economically feasible option. However, a hybrid WT–PV configuration of 212.5 MW WT and 637.5 MW PV was also proposed to support local manufacturing. All proposed systems provided over a 91% renewable energy contribution while reducing CO2 emissions by 53% compared to grid supply only. Up to 1152 jobs are estimated to be created through renewable energy deployment in Yanbu city.

In the recent era of globalization and climatic uncertainties, farmers are continuously adopting new strategies and crops to ensure substantial production and income. However, these adaptations always need to be based on systematic decisions; otherwise, the entire environment may degrade. The present study is one of such attempts to understand where to adopt an emerging crop (Maize) in lower Gangetic West Bengal. The decision on suitable sites was taken based on the analysis of fourteen important indicators in a GIS integrated fuzzy-MCE environment aided with different geostatistical interpolation models. The output shows that 18 and 30% of the total agricultural area of the study region is highly suitable and suitable for Maize cultivation following the FAO-based suitability classification arena. Spatially, it can be said that the farmers from a few blocks of the study area, namely Chanchal-I, II, Harishchandrapur I, II, Lalgola, Bamangola, Ratua-I, II, Suti, Burwan, etc., can ensure a sustainable profit by adopting Maize cultivation.

Energy generation from wind resources is now a mature technology with the ability to compete with traditional energy sources at utility scales in many countries, through the identification of suitable sites. However, beyond site suitability, predicting the wind resource variability of the potentially viable site presents overarching benefits in strategic and operational planning prior to site development. This study, therefore, combines geographical information systems multicriteria decision-making (GIS-MCDM) and hybrid neurofuzzy modeling tools for site suitability and resource variability forecast, respectively, in the Eastern Cape Province of South Africa. The GIS model uses two factors (climatological and environmental), and analytical hierarchical process was used for evaluating criteria degree of influence. Wind resource variability using diurnal satellite-based data for the candidate site was used on the models. Adaptive neurofuzzy inference system models hybrid with genetic algorithm (GA-ANFIS) and particle swarm optimization (PSO-ANFIS) were compared with standalone ANFIS and Levenberg–Marquardt backpropagation neural network (LMBP-ANN) using six statistical measures of error, accuracy, and variability. The GA-ANFIS and PSO-ANFIS accurately model the resource with PSO-ANFIS having lesser computational time compared to GA-ANFIS. However, LMBP-ANN is most robust and resistant in modeling the resource variability among the four models. Hence, wind resource variability investigation on a potentially viable site obtained from the GIS-MCDM model can complement on-site investigations prior to site development. Also, tuning ANFIS with evolutionary algorithms offers improved accuracy over standalone ANFIS model for wind resource forecast and further its robustness in predicting variability of the resource. From our findings, cross-boundary wind resource exploration between South Africa and Lesotho could foster regional interconnectivity.