Conceptual Framework for Assessing the Sustainability of Forest Fuel Reduction Treatments and Their Adaptation to Climate Change

Prato, Tony

doi:10.3390/su7043571

Cited by 6 publications

(4 citation statements)

References 28 publications

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“…Consequently, fuzzy TOPSIS is provided by integrating the fuzzy theory and TOPSIS method to deal with the qualitative issues. Moreover, this method can evaluate alternatives by considering the shortest geometric distances from benefit ideal solution and the longest distance from cost ideal solution [36][37][38][39]. On the other hand, some research aims at integrating the TOPSIS method with various weighting methods, such as AHP-TOPSIS, ANP-TOPSIS and so on [40][41][42].…”

Section: Literature Reviewmentioning

confidence: 99%

Performance Evaluation for Sustainability of Strong Smart Grid by Using Stochastic AHP and Fuzzy TOPSIS Methods

Zhao

2016

Sustainability

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Abstract:As an efficient way to deal with the global climate change and energy shortage problems, a strong, self-healing, compatible, economic and integrative smart gird is under construction in China, which is supported by large amounts of investments and advanced technologies. To promote the construction, operation and sustainable development of Strong Smart Grid (SSG), a novel hybrid framework for evaluating the performance of SSG is proposed from the perspective of sustainability. Based on a literature review, experts' opinions and the technical characteristics of SSG, the evaluation model involves four sustainability criteria defined as economy, society, environment and technology aspects associated with 12 sub-criteria. Considering the ambiguity and vagueness of the subjective judgments on sub-criteria, fuzzy TOPSIS method is employed to evaluate the performance of SSG. In addition, different from previous research, this paper adopts the stochastic Analytical Hierarchy Process (AHP) method to upgrade the traditional Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) by addressing the fuzzy and stochastic factors within weights calculation. Finally, four regional smart grids in China are ranked by employing the proposed framework. The results show that the sub-criteria affiliated with environment obtain much more attention than that of economy from experts group. Moreover, the sensitivity analysis indicates the ranking list remains stable no matter how sub-criteria weights are changed, which verifies the robustness and effectiveness of the proposed model and evaluation results. This study provides a comprehensive and effective method for performance evaluation of SSG and also innovates the weights calculation for traditional TOPSIS.

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Section: Literature Reviewmentioning

confidence: 99%

Performance Evaluation for Sustainability of Strong Smart Grid by Using Stochastic AHP and Fuzzy TOPSIS Methods

Zhao

2016

Sustainability

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“…A popular approach in constructing fuzzy conversion scales for the fuzzy TOPSIS is to determine the parameters of triangular fuzzy numbers based on a value in the interval <0; 10>. An example may be the scales proposed in [14,15]. The scales are characterized by a large spread of the triangular fuzzy number assigned to the middle category.…”

Section: Introductionmentioning

confidence: 99%

“…Regardless of the area of application of the fuzzy TOPSIS in connection with fuzzy conversion scales, the method of determining the parameters of fuzzy numbers assigned to categories of the ordinal measurement scales is debatable. It usually subjectively takes place because it is most often determined by researchers or experts (see for example [7][8][9][10][11][12][13][14][15][16][17]). Therefore, the article presents a new proposal of the method for determining the parameters of fuzzy conversion scales for the fuzzy TOPSIS method based on the respondents' opinions who assess the partial criteria.…”

Section: Introductionmentioning

confidence: 99%

Item Response Theory Models for the Fuzzy TOPSIS in the Analysis of Survey Data

Jefmański

Sagan

2021

Symmetry

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The fuzzy TOPSIS (The Technique for Order of Preference by Similarity to Ideal Solution) is an attractive tool for measuring complex phenomena based on uncertain data. The original version of the method assumes that the object assessments in terms of the adopted criteria are expressed as triangular fuzzy numbers. One of the crucial stages of the fuzzy TOPSIS is selecting the fuzzy conversion scale, which is used to evaluate objects in terms of the adopted criteria. The choice of a fuzzy conversion scale may influence the results of the fuzzy TOPSIS. There is no uniform approach in constructing and selecting the fuzzy conversion scale for the fuzzy TOPSIS. The choice is subjective and made by researchers. Therefore, the aim of the article is to present a new, objective approach to the construction of fuzzy conversion scales based on Item Response Theory (IRT) models. The following models were used in the construction of fuzzy conversion scales: Polychoric Correlation Model (PM), Polytomous Rasch Model (PRM), Rating Scale Model (RSM), Partial Credit Model (PCM), Generalized Partial Credit Model (GPCM), Graded Response Model (GRM), Nominal Response Model (NRM). The usefulness of the proposed approach is presented on the example of the analysis of a survey’s results on measuring the quality of professional life of inhabitants of selected communes in Poland. The obtained results indicate that the choice of the fuzzy conversion scale has a large impact on the closeness coefficient values. A large difference was also observed in the spreads of triangular fuzzy numbers between scales based on IRT models and those used in the literature on the subject. The use of the fuzzy TOPSIS with fuzzy conversion scales built based on PRM, RSM, PCM, GPCM, and GRM models gives results with a greater range of variability than in the case of fuzzy conversion scales used in empirical research.

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“…(a) afforestation, reforestation or forest management to increase the carbon fixed by forests; (b) bioenergy or biomaterials as substitutive of fossil fuels or more energy intensive materials [4][5][6]; and (c) preservation of existing forests to avoid or reduce emissions due to anthropogenic disturbances (deforestation) or natural disturbances (pests, hurricanes, snowfalls or wildfires) through preventive and sustainable forest management.…”

Section: Introductionmentioning

confidence: 99%

Potential Analysis of Mediterranean Forestry for Offsetting GHG Emissions at Regional Level: Evidence from Valencia, Spain

et al. 2021

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Forest management is an untapped tool, yet to realize its full potential to fight against climate change. The capability of forests to act as carbon sinks makes them a key resource to reduce CO2 concentration in the atmosphere. However, carbon which has been fixed can be suddenly emitted again as a consequence of disturbances such as pests or wildfires. Mediterranean plant phenology, climatic conditions, and the accumulation of fuel biomass due to abandonment of traditional forest uses generate a scenario prone to large wildfires and consequently large greenhouse gases (GHG) emissions. The abandonment could be offset by considering the economic value of forest ecosystem services, principally carbon fixation. Nevertheless, currently existing forest carbon markets consider only anthropogenic fixation based on a business as usual scenario without disturbances that cannot be applied to Mediterranean forest reality. Thus, a methodology to monetize carbon fixed has been developed and applied. A range between 55.5 and 250 million € produced by the monetization of 16.5 million potential carbon credits has been obtained based on anthropogenic avoided emissions produced over a 10 year-period. Thereby, the potential for offsetting emissions of the pilot region was between 1.2% and 5.6% of total diffuse GHG emissions. Consequently, sustainable forest management represents an important opportunity to combat climate change, taking advantage of the margin of improvement that the Mediterranean forests currently have to avoid GHG emissions through forest fire prevention silviculture.

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Conceptual Framework for Assessing the Sustainability of Forest Fuel Reduction Treatments and Their Adaptation to Climate Change

Cited by 6 publications

References 28 publications

Performance Evaluation for Sustainability of Strong Smart Grid by Using Stochastic AHP and Fuzzy TOPSIS Methods

Performance Evaluation for Sustainability of Strong Smart Grid by Using Stochastic AHP and Fuzzy TOPSIS Methods

Item Response Theory Models for the Fuzzy TOPSIS in the Analysis of Survey Data

Potential Analysis of Mediterranean Forestry for Offsetting GHG Emissions at Regional Level: Evidence from Valencia, Spain

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