Multi-criteria, multi-objective and uncertainty analysis for agro-energy spatial modelling

Tenerelli, Patrizia; Carver, Steve

doi:10.1016/j.apgeog.2011.08.013

Cited by 58 publications

(50 citation statements)

References 35 publications

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“…One specific approach to uncertainty analysis applicable to MCDA is to gain a sense of error or uncertainty in the predictions, given the uncertainty in the criterion weights (BENKE & PELIZARO 2010, FEIZIZADEH & BLASCHKE 2014. It is believed that it is essential to handle GIS-MCDA errors and uncertainty in decision-making, particularly when decisions are based on probabilistic ranges rather than on deterministic results (TENERELLI & CARVER 2012, FEIZIZADEH et al 2014a. MCDA uncertainty analysis embraces issues beyond traditional risk definitions.…”

Section: Sensitivity and Uncertainty Analysis Approach For Gis-mcda Bmentioning

confidence: 99%

Sensitivity and Uncertainty Analysis Approach for GIS-MCDA Based Economic Vulnerability Assessment

Feizizadeh¹,

Kienberger²,

Kamran³

2015

giforum

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This research aims to employ a novel methodology for modelling uncertainty in the GIS environment. The spatially explicit sensitivity and uncertainty analysis was applied on Multicriteria Decision Analysis (MCDA) for an economic vulnerability assessment within the Salzach Basin. The main objective of this research is to demonstrate how a unified approach of uncertainty and sensitivity analysis can be applied to minimize the associated uncertainty within an economic vulnerability assessment. In order to achieve this objective, the following methodology, composed four steps, was applied: (1) computation of criteria weights using Analytic Hierarchy Process (AHP); (2) Monte Carlo Simulation was applied for computing the uncertainties associated with AHP weights; (3) the Global Sensitivity Analysis (GSA) was employed in the form of the model-independent method of output variance decomposition, in which the variability of vulnerability maps is apportioned to every criterion weight, generating one first-order (S) and one total-effect (ST) sensitivity index map per criterion weight; and (4) an Ordered Weighted Averaging method was applied for producing vulnerability maps. The results of this research demonstrated the robustness of spatially explicit GSA for minimizing the uncertainty associated with GIS-MCDA models. According to the achieved results, we conclude that applying the variance based GSA leads to a spatial and statistical assessment of the importance of each input factor on the outcome of the GIS-MCDA method, which allows us to introduce and recommend GIS based GSA as a useful methodology for minimizing uncertainty of GIS-MCDA.

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Section: Sensitivity and Uncertainty Analysis Approach For Gis-mcda Bmentioning

confidence: 99%

Sensitivity and Uncertainty Analysis Approach for GIS-MCDA Based Economic Vulnerability Assessment

Feizizadeh¹,

Kienberger²,

Kamran³

2015

giforum

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“…The simulations performed in [19] also allowed to determine the criteria that were particularly sensitive to such variations. In [20] a method for determining the suitability of sites for conversion to perennial bioenergy crops is introduced. A simulation approach was applied to evaluate the influence of uncertainty of the input data and parameters on the model behaviour and outcomes.…”

Section: Literature Reviewmentioning

confidence: 99%

An Integer Programming Model to Determine Land Use Trajectories for Optimizing Regionally Integrated Ecosystem Services Delivery

Estrella

Cattrysse

Orshoven

2016

Forests

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BIOLP is an Integer Programming model based on the Balanced Compromise Programming multi-criteria decision method. The aim of BIOLP is to determine how a set of land use types should be distributed over space and time in order to optimize the multi-dimensional land performance of a region. Trajectories were defined as the succession of specific land use types over 30 years, assuming that land use changes can only occur at fixed intervals of 10 years. A database that represents the Tabacay catchment (Ecuador) as a set of land units with associated performance values was used as the input for BIOLP, which was then executed to determine the trajectories distribution that optimizes regional performance. The sensitivity of BIOLP to uncertainty in the input data, simulated through random variations on the performance values, was also tested. BIOLP showed a relative stability on its results under these conditions of stochastic, restricted changes. Additionally, the behaviour of BIOLP under different settings of its balancing and relative importance parameters was studied. Stronger variations on the outcomes were observed in this case, which indicate the influential role that such parameters play. Finally, the inclusion of performance thresholds in BIOLP was tested through the addition of sample constraints that required some of the criteria at stake to exceed predefined values. The outcome of the optimization exercises makes clear that the phenomenon of trade off between the provisioning service of the land (income) and the regulation and maintenance services (runoff, sediment, SOC) is crucial. BIOLP succeeds in accounting for this complex multi-dimensional phenomenon when determining the optimal spatio-temporal distributions of land use types. Despite this complexity, it is confirmed that the weights attributed to the provisioning or to the regulation and maintenance services are the main determinants for having the land use distributions dominated by either agriculture or forest.

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“…The AESC management requires to analyze the problems inherent production, processing, logistics (harvesting, transport and storage), marketing and channel diversifications, and the most efficient organization in order to coordinate the vertical integration and impose the hierarchical decisions to the member (Menard and Valceschini, 2005), in an environment characterized by the asymmetric distribution of information among partners and contingent risks caused by production and markets (Epperson and Estes, 1999). For this purposes, the spatial distribution of biomass and supply must be compatible with the demand and costs (production, processing, transport and distribution) (Grassano et al, 2011;Tenerelli and Carver, 2012). This paper proposes an integrated and interdisciplinary approach to planning the biofuel supply chain at a regional level aimed to evaluate the potential use of the land for the energy production and side effects, to supply the existing processing plants and accomplish with the economic, energy and environmental targets.…”

Section: Introductionmentioning

confidence: 99%

“…The increasing demand for energy and expected shortage in the long term, solicit new energy strategies to fill the increasing demand-supply gap (European Commission, 2009;Tenerelli and Carver, 2012). European Commission intends to implement these strategies in a contest where the environmental and social goals are considered as well.…”

Section: Introductionmentioning

confidence: 99%

Agro-energy supply chain planning: a procedure to evaluate economic, energy and environmental sustainability

et al. 2012

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The increasing demand for energy and expected shortage in the medium term, solicit innovative energy strategies to fulfill the increasing gap between demand-supply. For this purpose it is important to evaluate the potential supply of the energy crops and finding the areas of EU where it is most convenient. This paper proposes an agro-energy supply chain approach to planning the biofuel supply chain at a regional level. The proposed methodology is the result of an interdisciplinary team work and is aimed to evaluate the potential supply of land for the energy production and the efficiency of the processing plants considering simultaneously economic, energy and environmental targets. The crop simulation, on the basis of this approach, takes into account environmental and agricultural variables (soil, climate, crop, agronomic technique) that affect yields, energy and economic costs of the agricultural phase. The use of the Dijkstra's algorithm allows minimizing the biomass transport path from farm to collecting points and the processing plant, to reduce both the transport cost and the energy consumption. Finally, a global sustainability index (ACSI, Agro-energy Chain Sustainability Index) is computed combining economic, energy and environmental aspects to evaluate the sustainability of the Agroenergy supply chain (AESC) on the territory. The empirical part consists in a pilot study applied to the whole plain of Friuli Venezia Giulia (FVG) a region situated in the North-Eastern part of Italy covering about 161,300 ha. The simulation has been applied to the maize cultivation using three different technologies (different levels of irrigation and nitrogen fertilization: low, medium and high input). The higher input technologies allow to achieve higher crop yields, but affect negatively both the economic and energy balances. Low input levels provides, on the average, the most favourable energy and economic balances. ACSI indicates that low inputs levels ensure a more widespread sustainability of the agro-energy chain in the region. High ACSI values for high input levels are observed only for areas with very high yields or near the processing plant.

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Multi-criteria, multi-objective and uncertainty analysis for agro-energy spatial modelling

Cited by 58 publications

References 35 publications

Sensitivity and Uncertainty Analysis Approach for GIS-MCDA Based Economic Vulnerability Assessment

Sensitivity and Uncertainty Analysis Approach for GIS-MCDA Based Economic Vulnerability Assessment

An Integer Programming Model to Determine Land Use Trajectories for Optimizing Regionally Integrated Ecosystem Services Delivery

Agro-energy supply chain planning: a procedure to evaluate economic, energy and environmental sustainability

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