# Supportiveness of Low-Carbon Energy Technology Policy Using Fuzzy Multicriteria Decision-Making Methodologies

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## Abstract

**:**

## 1. Introduction

_{2}at least 20% by 2020, a goal which was never reached.

## 2. Material and Methods

#### 2.1. Strengths, Weaknesses, Opportunities, and Threats (SWOT) Analysis

#### 2.2. Stochastic Fuzzy Set Theory and Fuzzy Analytic Hierarchal Process

#### 2.3. Fuzzy Technique for Order Performance by Similarity to Ideal Solution

**Step 1**Define and classify the weights of the criteria involved. Each of the decision maker experts assigns a linguistic weight to all of the predetermined criteria. The assignment is subjective to each expert, but the linguistic values used are similar to a five-Likert scale, which is given in Table 1. Typical values can be “very low importance”, “low importance”, “medium importance”, “high importance” and “very high importance” and the corresponding normalized fuzzy numbers are shown in Table 2.

**Step 2**Creation of the judgement matrix. The judgement matrix refers to each decision maker and it is constructed by the available alternative decisions ${D}_{i}$ in combination with the available criteria ${C}_{j}$.

**Step 3**Creation of the normalized judgement matrix. To achieve this transformation, we first classify the criteria set into two subsets, namely (a) the benefit criteria (BC) subset and (b) the cost criteria (CC) subset. The normalized judgement matrix NJM is created using JM, BC, and CC where

**Step 4**Construct the weighted NJM. The weighted NJM denoted as WNJM is constructed as

**Step 5**Calculate the fuzzy positive ideal solution (FPIS) and the fuzzy negative ideal solution (FNIS). These solutions are given by the calculation of two vectors respectively ${A}^{*}$ and ${A}^{-}$ where

**Step 6**Calculate the distance between FPIS and FNIS, that is, the distance between ${A}^{*}$ and ${A}^{-}$ where

**Step 7**Calculate the closeness coefficient of each of the alternative decisions and order them in descending order.

#### 2.4. Fuzzy Goal Programming

#### 2.5. Research Comparison and Novelty of our Approach

- Most of the research attempts concentrate on a specific MCDM methodology and they do not integrate a plethora of methodologies to produce a “holistic” result based on more than one mode.
- There is not any other research work on the Greek case study to the best of our knowledge.
- Our methodology extends the conventional AHP, GP, and TOPSIS methodologies in terms of integrating the opinions of most critical stakeholder bodies as fuzzy values of their linguistic values in order to solve the MCDM problem of selecting renewable energy sources.
- The proposed methodology explores the validity and applicability of FAHP, FGP, and F-TOPSIS under the existed divergence.

## 3. Case Study: Thessaly Region, Greece

#### 3.1. Characteristics of the Region and Methodological Roadmap of the Study

^{2}and a population of 725,874 inhabitants (2018). Lately, the region exhibits a sharp turn to manufacturing and industrialization from the conventional agricultural activities.

#### 3.2. SWOT Analysis

#### 3.3. Fuzzy Goal Programming Analysis

- ${X}_{1}$ be the solar annual electricity generation;
- ${X}_{2}$ be the biofuel annual electricity generation;
- ${X}_{3}$ be the hydro annual electricity generation; and
- ${X}_{4}$ be the wind annual electricity generation.

#### 3.4. Stochastic Fuzzy AHP Analysis

#### 3.5. Fuzzy TOPSIS Analysis

## 4. Discussion of Results

- For the technological oriented criteria, criteria C2 (human capital expertise) was calculated to be the most important criterion with a normalized crisp weight of 0.3983, with the second in order criterion C1 (availability of the technological resources for application of the type) with a normalized crisp weight of 0.2123. The strategy is to, first, gain the human capital needed to build and manage the energy conversion establishments, and then think about which resources are available. Robustness of the projects is thought to be the least important factor. However, safety and the amount of electricity (GWh) that would be directed to the market are almost equally important criteria.
- For the economically oriented criteria, criteria C6 (capital cost), C7 (fuel and energy cost), and C8 (life expectancy of the system) by far are the most important factors in this specific order, with C6 obtaining a normalized crisp weight of 0.5127 and the other two obtaining weights of 0.2505 and 0.1032, respectively. As expected, the issue of initial capital is the most important factor in investing, especially in such investments of regional level and high cost. The second factor of importance is the energy costs for such factories to operate. Usually, this is calculated as a percentage of the outcome in electricity, but it is a key performance indicator for the overall operation trustworthiness. The lack of obtaining a degree of membership for C7 from the FGP methodology diminishes the trustworthiness of the FAHP method, especially relatively to the outcome of the C7 criterion. However, the value of importance of C7 is still undisputed because of its rating within the category and because of the high score received. Note, for example, that criteria C8, C9, C10, and C11 are all well below the range of 0.10 normalized weight, therefore, we can argue that the rate of C7 within the category cannot be changed and that was the reason that C7 was included in the F-TOPSIS.
- For the environmentally oriented criteria, similarly, clear weight ordering appears in this category with criterion C12 (waste production) performing much higher than the other criteria gaining a normalized crisp weight of 0.4698 and with criterion C13 (hazardous aerial emissions) in second place with a normalized crisp weight of 0.2010, outperforming criteria C14 and C15. The results show that stakeholders want to explore the opportunity to experience green and renewable energy exploitation via policies that minimize the waste production. Moreover, competent authorities and the final policy makers should integrate this approach horizontally and vertically to try to incorporate the appropriate strategic alliances towards low carbon economy.
- For the socially oriented criteria, relative to social acceptance and supportiveness of the low carbon energy policy making, the results of FAHP indicate that the primary factor for the Greek society is the creation of new jobs and how this is going to affect the regional economy with criterion C16 (new jobs) outperforming the other criteria of the category (normalized crisp weight of 0.3615). At the same time, criteria C17 (quantification of the (not in my back yard) NIMBY phenomenon) and C19 (impact on residents’ everyday life and health) are almost equally important for the social bodies of Thessaly Region with normalized crisp weights of 0.1947 and 0.2269, respectively. The remaining category criteria participate in the distribution of weights with very low percentages. Thus, although the society wants new jobs and is aware of the health and environmental benefits of the conversion into low carbon practices, people are still afraid to reside close to such energy production plants.

## 5. Conclusions and Future Challenges

## Author Contributions

## Funding

## Conflicts of Interest

## References

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Definition | Crisp Values (Intensity of Importance) | Fuzzy Triangular Scale $\tilde{\mathit{M}}=\left(\mathit{l},\mathit{m},\mathit{u}\right)$ |
---|---|---|

Equally important | 1 | (1,1,1) |

Weakly important | 3 | (1,3,5) |

Fairly important | 5 | (3,5,7) |

Strongly important | 7 | (5,7,9) |

Absolutely important | 9 | (7,9,9) |

Linguistic Value | Normalized Fuzzy Triangular Number |
---|---|

Very low importance | (0,0.1,0.3) |

Low importance | (0.1,0.3,0.5) |

Medium importance | (0.3,0.5,0.7) |

High importance | (0.5,0.7,0.9) |

Very high importance | (0.7,0.9,1) |

**Table 3.**Comparison of research results related to renewable energy selection using stochastic fuzzy analytic hierarchical process (SF-AHP), fuzzy goal programming (FGP), and fuzzy technique for order performance by similarity to ideal solution (F-TOPSIS).

Studies | Case Study | Energy Strategies | Criteria | MCDM Methods | Best Strategy |
---|---|---|---|---|---|

[52] | Lagos Central District | Numerical illustrations | Energy selection | IF-TOPSIS | Only TOPSIS |

[81] | Numerical example of small town | Multiobjective decision via function optimization | Collaboration of multiple energy types | GP | GP |

[83] | Numerical example | Biodiesel | classification | FGP | Only one is considered |

[62] | Turkey | Select the best energy | Multicriteria | FAHP, F-VICOR, F-TOPSIS | Collaboration |

[53] | Turkey | Selection among all, not only renewable | Strengths, opportunities, weaknesses, threats | F-TOPSIS, SWOT, ANP | ANP |

[74] | Oregon | Min and max of criteria | Construction cost, Production cost, land use, environmental impact | FGP | FGP |

[54] | N/A | Biomass | Profit, sales, customization, affordability, participation, experience, technology | HF-DEMATEL, HF-TOPSIS | HF-TOPSIS |

[85] | N/A | Electrochemical energy storage | Economic, social, environmental, and technological | F-TOPSIS, FGP | F-TOPSIS |

[55] | Numerical Example | Renewable energy policy selection | Technology, environmental, social, economical | VIKOR, IVPLTS | IVPLTS |

[42] | Taiwan | Solar energy | Economic development, energy exploiting, environmental protection | SEA, FDM, SAM, Fuzzy AHP | FAHP |

[76] | Philippines | Biodiesel | Max feedstock production and revenue, min of energy used and working capital, availability of land, labor, water and machine time | FGP | FGP |

[56,57] | India | Selection between nuclear, solar, hydropower, biomass, heat, and power | Efficiency, investment-operation cost, water pollution, pollutant emission, land, social acceptance, job creation | F-TOPSIS | F-TOPSIS |

[56] | India | Determine the potentiality indices for healthier exploration of wind energy resources in India | Wind power density, availability of suitable, land, government initiatives, grid connectivity, technical prowess | TOPSIS VIKOR FAHP | Combination of F-TOPSIS and F-VIKOR |

[61] | Pakistan | Rank 13 energy strategies | Multitude of criteria in MCDM process | SWOT FAHP and F-TOPSIS | Integration of the two methods |

[64] | Saudi Arabia | Eight energy strategies as candidates | Power generation, capacity, efficiency, storability, safety, air pollution, net present value | Integrated FAHP F-VIKOR, F-TOPSIS | Solar energy is the most profitable according to both F-VIKOR and F-TOPSIS |

[44] | Pakistan | solar, wind, and biomass | 4 Basic criteria (economic, environmental, technical, and socio-political), and 17 subcriteria | SWOT FAHP | SWOT FAHP |

Criteria No. | Description |
---|---|

TECHNOLOGICAL | |

C1 | Availability of the technological resources for application of the type |

C2 | Human capital expertise |

C3 | Robustness and reliability of the energy type |

C4 | Electricity supply |

C5 | Safety |

ECONOMIC | |

C6 | Capital cost |

C7 | Fuel or energy cost |

C8 | Life expectancy of the system |

C9 | Cost to connect to the electricity network |

C10 | Research and development cost |

C11 | Marketing and other cost |

ENVIRONMENTAL | |

C12 | Waste production |

C13 | Hazardous aerial emissions |

C14 | Ecology preservation |

C15 | Land use |

SOCIAL | |

C16 | New jobs |

C17 | Quantification of the (not in my back yard) NIMBY phenomenon |

C18 | Social perception and acceptance |

C19 | Impact on residents’ everyday life and health |

C20 | Feasibility of the energy plant |

C21 | Impact to regional sustainability |

Criteria | Solar | Wind | Biofuel | Hydro | Aspiration Level | Tolerance |
---|---|---|---|---|---|---|

C1 | 4,048,112 ≤ ${y}_{11}$ ≤ 4,648,112 | 2,098,741 ≤ ${y}_{12}$ ≤ 3,098,741 | 8,683,567 ≤ ${y}_{13}$ ≤ 9,500,000 | 650,000 ≤ ${y}_{14}$ ≤ 750,000 | 18,000,000 | 1,000,000 |

C2 | 40,481 ≤ ${y}_{21}$ ≤ 46,481 | 82,962 ≤ ${y}_{22}$ ≤ 92,962 | 605,571 ≤ ${y}_{23}$ ≤ 645.571 | 10,000 ≤ ${y}_{24}$ ≤ 15,000 | 6,000,000 | 600,000 |

C6 | 467,925 | 1,814,470 | 9,292,500 | 375,000 | 41,000,000 | 4,100,000 |

C7 | N/A | N/A | N/A | N/A | N/A | N/A |

C12 | 14,190 | 57,178 | 68,950 | 52,000 | 4,000,000 | 4,000,000 |

C16 | 1500 ≤ ${y}_{61}$ ≤ 2000 | 900 ≤ ${y}_{62}$ ≤ 1200 | 500 ≤ ${y}_{63}$ ≤ 800 | 500 ≤ ${y}_{64}$ ≤ 800 | 1500 | 350 |

**Table 6.**SF-AHP results per criteria category for the category technological. (

**a**) Fuzzified pairwise comparison matrix; (

**b**) Criteria fuzzy and defuzzified weights.

(a) | |||||

Fuzzified Pairwise Comparison Matrix | |||||

C1 | C2 | C3 | C4 | C5 | |

C1 | (1,1,1) | (1,2,3) | (2,3,4) | (2,3,4) | (0.166,0.2,0.25) |

C2 | (0.333,0.5,1) | (1,1,1) | (6,7,8) | (2,3,4) | (6,7,8) |

C3 | (0.25,0.333,0.5) | (0.125,0.142,0.166) | (1,1,1) | (0.166,0.2,0.25) | (0.25,0.333,0.5) |

C4 | (0.25,0.333,0.5) | (0.25,0.333,0.5) | (4,5,6) | (1,1,1) | (1,1,1) |

C5 | (4,5,6) | (0.125,0.142,0.166) | (2,3,4) | (1,1,1) | (1,1,1) |

(b) | |||||

Criteria Fuzzy and Defuzzified Weights | |||||

Fuzzy Geometric Mean | Lower | Middle | Upper | Crisp | Norm Crisp |

(0.9213, 1.2919, 1.6437) | 0.1232 | 0.2145 | 0.3402 | 0.2260 | 0.2123 |

(1.8877, 2.3618, 3.0314) | 0.2524 | 0.3922 | 0.6275 | 0.4240 | 0.3983 |

(0.2645, 0.3159, 0.4010) | 0.0354 | 0.0525 | 0.0830 | 0.0569 | 0.0535 |

(0.7578, 0.8887, 1.0844) | 0.1013 | 0.1476 | 0.2245 | 0.1578 | 0.1482 |

(1, 1.1632, 1.3184) | 0.1337 | 0.1932 | 0.2729 | 0.1999 | 0.1878 |

**Table 7.**SF-AHP results per criteria category for the category economic. (

**a**) Fuzzified pairwise comparison matrix; (

**b**) Criteria fuzzy and defuzzified weights.

(a) | ||||||

Fuzzified Pairwise Comparison Matrix | ||||||

C6 | C7 | C8 | C9 | C10 | C11 | |

C6 | (1,1,1) | (6,7,8) | (6,7,8) | (7,8,9) | (7,8,9) | (6,7,8) |

C7 | (0.125,0.142,0.166) | (1,1,1) | (4,5,6) | (4,5,6) | (6,7,8) | (7,8,9) |

C8 | (0.125,0.142,0.166) | (0.142,0.2,0.25) | (1,1,1) | (3,4,5) | (2,3,4) | (3,4,5) |

C9 | (1.111,0.125,0.142) | (0.142,0.2,0.25) | (0.2,0.25,0.333) | (1,1,1) | (3,4,5) | (3,4,5) |

C10 | (1.111,0.125,0.142) | (0.125,0.142,0.166) | (0.25,0.333,0.5) | (0.2,0.25,0.333) | (1,1,1) | (0.25,0.333,0.5) |

C11 | (0.125,0.142,0.166) | (1.111,0.125,0.142) | (0.2,0.25,0.333) | (0.2,0.25,0.333) | (2,3,4) | (1,1,1) |

(b) | ||||||

Criteria Fuzzy and Defuzzified Weights | ||||||

Fuzzy Geometric Mean | Lower | Middle | Upper | Crisp | Norm Crisp | |

(4.6857, 5.2915, 5.8836) | 0.4058 | 0.5233 | 0.6331 | 0.5207 | 0.5127 | |

(2.6367, 2.4183, 2.7495) | 0.2283 | 0.2392 | 0.2958 | 0.2544 | 0.2505 | |

(0.8492, 1.0541, 1.2685) | 0.0735 | 0.1043 | 0.1365 | 0.1048 | 0.1032 | |

(0.5673, 0.6813, 0.8171) | 0.0491 | 0.0674 | 0.0879 | 0.0681 | 0.067 | |

(0.2362, 0.2814, 0.3545) | 0.0205 | 0.0278 | 0.0381 | 0.0288 | 0.0284 | |

(0.3218, 0.3868, 0.4686) | 0.0279 | 0.0383 | 0.0504 | 0.0389 | 0.0383 |

**Table 8.**SF-AHP results per criteria category for the category environmental. (

**a**) Fuzzified pairwise comparison matrix; (

**b**) Criteria fuzzy and defuzzified weights.

(a) | |||||

Fuzzified Pairwise Comparison Matrix | |||||

C12 | C13 | C14 | C15 | ||

C12 | (1,1,1) | (1,2,3) | (3,4,5) | (2,3,4) | |

C13 | (0.333,0.5,1) | (1,1,1) | (1,2,3) | (0.333,0.5,1) | |

C14 | (0.2,0.25,0.333) | (0.333,0.5,1) | (1,1,1) | (2,3,4) | |

C15 | (0.25,0.333,0.5) | (1,2,3) | (0.25,0.333,0.5) | (1,1,1) | |

(b) | |||||

Criteria Fuzzy and Defuzzified Weights | |||||

Fuzzy Geometric Mean | Lower | Middle | Upper | Crisp | Norm Crisp |

(1.5651, 2.2134, 2.7832) | 0.2564 | 0.4892 | 0.8569 | 0.5342 | 0.4698 |

(0.5774, 0.8409, 1.3161) | 0.0946 | 0.1858 | 0.4052 | 0.2285 | 0.2010 |

(0.6043, 0.7825, 1.0746) | 0.0990 | 0.1729 | 0.3309 | 0.2009 | 0.1767 |

(0.5000, 0.6866, 0.9306) | 0.0819 | 0.1517 | 0.2865 | 0.1734 | 0.1525 |

**Table 9.**SF-AHP results per criteria category for the category social. (

**a**) Fuzzified pairwise comparison matrix; (

**b**) Criteria fuzzy and defuzzified weights.

(a) | ||||||

Fuzzified Pairwise Comparison Matrix | ||||||

C16 | C17 | C18 | C19 | C20 | C21 | |

C16 | (1,1,1) | (1,2,3) | (3,4,5) | (1,2,3) | (6,7,8) | (3,4,5) |

C17 | (0.333,0.5,1) | (1,1,1) | (1,1,1) | (1,2,3) | (2,3,4) | (3,4,5) |

C18 | (0.2,0.25,0.333) | (1,1,1) | (1,1,1) | (0.25,0.333,0.5) | (1,1,1) | (1,2,3) |

C19 | (0.333,0.5,1) | (0.333,0.5,1) | (2,3,4) | (1,1,1) | (5,6,7) | (3,4,5) |

C20 | (0.125,0.142,0.166) | (0.25,0.333,0.5) | (1,1,1) | (0.142,0.166,0.2) | (1,1,1) | (0.333,0.5,1) |

C21 | (0.2,0.25,0.333) | (0.2,0.25,0.333) | (0.333, 0.5,1) | (0.2,0.25,0.333) | (1,2,3) | (1,1,1) |

(b) | ||||||

Criteria Fuzzy and Defuzzified Weights | ||||||

Fuzzy Geometric Mean | Lower | Middle | Upper | Crisp | Norm Crisp | |

(1.9442, 2.7662, 3.4878) | 0.2003 | 0.3776 | 0.6111 | 0.3963 | 0.3615 | |

(1.1225, 1.2988, 1.9786) | 0.1156 | 0.1773 | 0.3467 | 0.2132 | 0.1945 | |

(0.7071, 0.7418, 0.7647) | 0.0728 | 0.1013 | 0.1340 | 0.1027 | 0.0937 | |

(1.2222, 1.6189, 2.2787) | 0.1259 | 0.2210 | 0.3992 | 0.2487 | 0.2269 | |

(0.3379, 0.3979, 0.5054) | 0.0348 | 0.0543 | 0.0885 | 0.0592 | 0.0540 | |

(0.3724, 0.500, 0.6934) | 0.0384 | 0.0683 | 0.1215 | 0.0760 | 0.0694 |

Criteria ID | Lower | Middle | Upper |
---|---|---|---|

C16 | 0.059532 | 0.168872987 | 0.416734861 |

C12 | 0.076205 | 0.218783542 | 0.584356247 |

C6 | 0.120609 | 0.234033989 | 0.431737589 |

C2 | 0.075016 | 0.175402504 | 0.427918712 |

C1 | 0.036617 | 0.095930233 | 0.231996727 |

C7 | 0.067854 | 0.106976744 | 0.201718494 |

**Table 11.**Calculation of the fuzzy decision matrix and the fuzzy positive ideal solution (FPIS) and fuzzy negative ideal solution (FNIS).

Normalized Fuzzy Decision Matrix Using the c_{j}^{*} and a_{j}^{−} | ||||||

S | (0.555, 0.805, 1) | (0.666, 0.833, 1) | (0.444, 0.592, 1) | (0.444, 0.533, 0.8) | (0.25, 0.444, 1) | (0.777, 0.888, 1) |

B | (0.777, 0.888, 1) | (0.777,0.888,1) | (0.444, 0.64, 0.869) | (0.571, 0.695, 1) | (0.25, 0.266, 1) | (0.111, 0.222, 0.333) |

H | (0.666, 0.833, 1) | (0.666, 0.833, 1) | (0.444, 0.533, 0.8) | (0.444, 0.5, 0.571) | (0.25, 0.4, 1) | (0.222, 0.444, 0.666) |

W | (0.666, 0.805, 1) | (0.666, 0.833, 1) | (0.444, 0.592, 1) | (0.571, 0.727, 1) | (0.25, 0.444, 1) | (0.222, 0.388, 0.666) |

Weighted Normalized Fuzzy Decision Matrix | ||||||

S | (0.019, 0.076, 0.231) | (0.049, 0.145, 0.427) | (0.053, 0.138, 0.431) | (0.029, 0.056, 0.160) | (0.019, 0.096, 0.584) | (0.045, 0.149, 0.416) |

B | (0.028, 0.084, 0.231) | (0.058, 0.155, 0.427) | (0.053, 0.149, 0.374) | (0.038, 0.073, 0.201) | (0.019, 0.057, 0.584) | (0.006, 0.037, 0.138) |

H | (0.023, 0.079, 0.231) | (0.049, 0.145, 0.427) | (0.053, 0.124, 0.344) | (0.029, 0.053, 0.114) | (0.019, 0.087, 0.584) | (0.013, 0.074, 0.277) |

W | (0.023, 0.076, 0.231) | (0.049, 0.145, 0.427) | (0.053, 0.138, 0.431) | (0.038, 0.077, 0.201) | (0.019, 0.096, 0.584) | (0.013, 0.065, 0.277) |

FNIS and FPIS | ||||||

N | (0.028, 0.084, 0.231) | (0.058, 0.155, 0.427) | (0.053, 0.138, 0.431) | (0.038, 0.077, 0.201) | (0.019, 0.096, 0.584) | (0.045, 0.149, 0.416) |

P | (0.019, 0.076, 0.231) | (0.049, 0.145, 0.427) | (0.053, 0.138, 0.431) | (0.029, 0.056, 0.160) | (0.019, 0.057, 0.584) | (0.006, 0.037, 0.138) |

Distance of Weighted Normalized Fuzzy Decision Matrix from FNIS | ||||||

Solar | 0 | 0 | 0 | 0 | 0.02240119 | 0.174605594 |

Biofuel | 0.006699751 | 0.007370663 | 0.033285032 | 0.026082561 | 0 | 0 |

Hydro | 0.002786276 | 0 | 0.050800427 | 0.026688137 | 0.016916363 | 0.082897105 |

Wind | 0.002309401 | 0 | 0 | 0.026891263 | 0.02240119 | 0.081655598 |

Distance of Weighted Normalized Fuzzy Decision Matrix from FPIS | ||||||

Solar | 0.006699751 | 0.007370663 | 0 | 0.026891263 | 0 | 0 |

Biofuel | 0 | 0 | 0.033651548 | 0 | 0.02240119 | 0.174605594 |

Hydro | 0.003970306 | 0.007370663 | 0.050800427 | 0.052336253 | 0.005484828 | 0.093375746 |

Wind | 0.005249127 | 0.007370663 | 0 | 0 | 0 | 0.095999878 |

Energy Type | ${\tilde{\mathit{d}}}_{\mathit{i}}^{*}$ | ${\tilde{\mathit{d}}}_{\mathit{i}}^{-}$ | CCi = di-/(di- + di *) | RANK |
---|---|---|---|---|

Solar | 0.040961676 | 0.197006784 | 0.827869306 | 1 |

Biofuel | 0.230658332 | 0.073438007 | 0.24149586 | 4 |

Hydro | 0.213338222 | 0.180088307 | 0.457743172 | 3 |

Wind | 0.108619668 | 0.133257452 | 0.550930373 | 2 |

${\tilde{d}}_{i}^{*}$ and ${\tilde{d}}_{i}^{-}$ distance between FPIS and FNIS (Equations (18) and (19)) |

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## Share and Cite

**MDPI and ACS Style**

Kokkinos, K.; Karayannis, V.
Supportiveness of Low-Carbon Energy Technology Policy Using Fuzzy Multicriteria Decision-Making Methodologies. *Mathematics* **2020**, *8*, 1178.
https://doi.org/10.3390/math8071178

**AMA Style**

Kokkinos K, Karayannis V.
Supportiveness of Low-Carbon Energy Technology Policy Using Fuzzy Multicriteria Decision-Making Methodologies. *Mathematics*. 2020; 8(7):1178.
https://doi.org/10.3390/math8071178

**Chicago/Turabian Style**

Kokkinos, Konstantinos, and Vayos Karayannis.
2020. "Supportiveness of Low-Carbon Energy Technology Policy Using Fuzzy Multicriteria Decision-Making Methodologies" *Mathematics* 8, no. 7: 1178.
https://doi.org/10.3390/math8071178