Evaluating loblolly pine bioenergy development potential using AHP integrated weighted overlay and network optimization in Virginia, USA

Nguyen, Huynh Truong Gia; Lyttek, Erik; Lal, Pankaj; Wieczerak, Taylor; Luong, Trang

doi:10.1016/j.tfp.2022.100271

Cited by 4 publications

(4 citation statements)

References 15 publications

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“…The combination of GIS and AHP methods by adding IoT technology provides convenience for decision-making in data collection and spatial data analysis and storage in the agricultural waste biomass supply chain [34]. Different IoTbased spatial decision support systems concepts are proposed by considering spatial aspects and perspectives.…”

Section: Decision Support System (Dss) Concept For Bioenergy Supply C...mentioning

confidence: 99%

Decision Support System for Bioenergy Supply Chain Optimization: A Case Study at Lebak District, Banten Indonesia

Krisnaningsih,

Arkeman,

Marimin

et al. 2024

IJSDP

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This study aims to design a decision support system (DSS) development model in sustainable bioenergy supply chains. Our approach involves 1) identifying the bioenergy supply chain model through determining the spatial potential model of agroindustry area using geographic information system (GIS) and analytical hierarchy process (AHP); 2) determining the optimization model for aggregate production process planning using fuzzy goal programming for bioenergy production to design the biomass inventory level determination model using adaptive neuro-fuzzy inference system (ANFIS) approach; 3) designing the concept of DSS model in bioenergy supply chain. The results showed the identification of the supply chain model from the spatial model of potential agroindustry locations with three regional categories: 19.34% potential, 16.93% not potential, and 63.70% developing. Aggregate planning is appropriate based on three objective functions to be achieved in production planning for determining inventory levels using ANFIS using three input variables and comparing performance with RMSE, MAPE, and R2 inventory levels so that the model can predict inventory levels adaptively. The concept of the DSS Model on the bioenergy supply chain from agricultural centers to users by adding Internet of Things (IoT) technology can increase the effectiveness and efficiency of the bioenergy supply chain. The managerial implications of this research can provide relevant insights for the design and improvement of renewable energy management programs. Utilization of local biomass resources becomes more optimal.

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Section: Decision Support System (Dss) Concept For Bioenergy Supply C...mentioning

confidence: 99%

Decision Support System for Bioenergy Supply Chain Optimization: A Case Study at Lebak District, Banten Indonesia

Krisnaningsih,

Arkeman,

Marimin

et al. 2024

IJSDP

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“…The weighted overlay technique is a spatial modeling approach that combines multiple layers of spatial data, assigning weights to each layer to analyze their combined influence on a specific phenomenon, such as soil organic carbon (SOC) content. The weighted overlay analysis for identifying suitable locations utilizes a scale from 1 to 9, where 9 represents the best, and 1 is the worst suitability for each category [41]. The weighted overlay analysis facilitates the identification of suitable locations by generating a composite suitability map in different applications in raster data.…”

Section: Weighted Overlay Techniquementioning

confidence: 99%

Spatial Distribution of Soil Organic Carbon in Relation to Land Use, Based on the Weighted Overlay Technique in the High Andean Ecosystem of Puno—Peru

Canaza,

Calizaya,

Chambi

et al. 2023

Sustainability

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Soil organic carbon (SOC) is a crucial component of the planet and is essential for agriculture development. Our region is known for its livestock and agricultural activities. Hence, understanding the spatial distribution of SOC is crucial for sustainable land management of soils in the mountain ecosystems in the Andes. The methodology consisted of obtaining 53 soil samples from depths of 0 to 105 cm, which were analyzed to obtain SOC (Kg/m2) and organic matter (OM) (%). Ordinary kriging, a geostatistical method, was used to determine SOC. Pearson’s statistical method was applied to determine the association between SOC and precipitation, temperature, altitude, and organic matter and gave acceptable correlations of 0.38, −0.32, 0.40, and 0.59. These results were used to apply weighting criteria for climatological and environmental variables. The weighted overlay tool was used for modeling and mapping the spatial variability of SOC. The estimated spatial distribution of SOC in the micro-watershed reveals an increasing trend from south to north, specifically within the 0–20 cm depth profile. The study confirmed through the spatial analysis that regions with intensive agriculture have low reserves (<3 Kg/m2) of SOC, and areas without agricultural activity but with grazing have average resources of 3 Kg/m2 to 5 Kg/m2 of SOC. Finally, in the upper micro-watersheds where there is no agricultural activity, the reserves are high (5 Kg/m2 to 6.8 Kg/m2). Accordingly, we can promote sustainable and responsible land use practices that support long-term productivity, environmental protection, and societal well-being by prioritizing efficient land utilization, soil conservation, biodiversity conservation, land restoration, and informed land use planning in the high Andean ecosystem of Puno–Peru.

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“…The main purpose of this article is to explore and compare the application of FAHP and AHP in the Colombian context to optimize the selection of renewable energy sources. Colombia, given its geographical and climatic variability, hosts a range of options that includes solar, wind, hydroelectric, biomass and others [8]. The right choice between these sources is essential to ensure the sustainability of the energy supply and mitigate environmental impacts [9].…”

Section: Introductionmentioning

confidence: 99%

Comparison between AHP and FAHP for the best Selection of Renewable Energies in Colombia, a country with diversity in its geographical regions: Optimization and Sustainability

Rocha,

Buelavas

2024

Preprint

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This study presents a comprehensive examination of renewable energy sources across distinct Colombian regions, employing the AHP and FAHP methodologies. Tailoring the selection of alternatives, including solar photovoltaic, offshore/onshore wind, biomass, geothermal, and large- and small-scale hydropower, to each region's specific characteristics aims to provide sustainable solutions for the nation's escalating energy demand. The weighting process incorporates multidimensional criteria encompassing social, economic, technical, and environmental aspects, further broken down into sub-criteria for meticulous evaluation. Results unveil distinct weightings and hierarchies for each region, shaped by the variability in energy potential and resource availability. Methodological differences yield notable disparities in outcomes, with solar photovoltaics emerging as a leading option in most regions, securing a 60% preference due to its viability and potential. This emphasizes the necessity of considering unique regional characteristics when selecting the most suitable energy source. Furthermore, the study emphasizes the significance of introducing innovative metaheuristic methods, notably AHP and FAHP, into the Colombian energy sector. In a geographically diverse country like Colombia, where regional variability demands flexible approaches, these methodologies emerge as crucial decision-making tools. Overcoming the limitations of traditional approaches, they provide a precise and informed assessment, adeptly managing uncertainty and subjectivity in dynamic environments. This research not only offers a valuable guide for the effective implementation of renewable energies in Colombia but also underscores the importance of accommodating regional variability in the transition towards a cleaner and more sustainable energy system. The application of metaheuristic methods contributes to more effective strategic planning, fostering environmental sustainability within the Colombian context. Ultimately, the combination of detailed assessment, specific weighting, and the application of advanced methodologies presents a comprehensive approach to addressing energy challenges in a country as diverse as Colombia.

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Evaluating loblolly pine bioenergy development potential using AHP integrated weighted overlay and network optimization in Virginia, USA

Cited by 4 publications

References 15 publications

Decision Support System for Bioenergy Supply Chain Optimization: A Case Study at Lebak District, Banten Indonesia

Decision Support System for Bioenergy Supply Chain Optimization: A Case Study at Lebak District, Banten Indonesia

Spatial Distribution of Soil Organic Carbon in Relation to Land Use, Based on the Weighted Overlay Technique in the High Andean Ecosystem of Puno—Peru

Comparison between AHP and FAHP for the best Selection of Renewable Energies in Colombia, a country with diversity in its geographical regions: Optimization and Sustainability

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