Integrating Fuzzy MCDM Methods and ARDL Approach for Circular Economy Strategy Analysis in Romania
Abstract
:1. Introduction
2. Literature Review
2.1. The Relationship between Technological Innovation and Emissions
2.2. The Relationship between GDP and Emissions
2.3. The Relationship between Urbanization and Emissions
2.4. The Relationship between Renewable Energy and Emissions
2.5. Application of Fuzzy MCDM in Circular Economy Assessment
3. Methodology and Data Collection
3.1. Fuzzy Multi-Criteria Decision-Making Methods
3.1.1. Fuzzy Electre
- ➢
- Step 1: Defining the problem and identifying the set of criteria.
- ➢
- Step 2: Defining the set of alternatives.
- ➢
- Step 3: Building the fuzzy decision matrix .
- ➢
- Step 4: Normalization of the fuzzy decision matrix: .
- ➢
- Step 5: Determination of the weights of the criteria.
- ➢
- Step 6: The calculation of the weighted matrix , where is calculated according to relation (4):
- ➢
- Step 7: Calculation of the Concordance Matrix C.
- ➢
- Step 8: Calculation of the Discordance Matrix D.
- ➢
- Step 9: Construction of the Concordance Dominance Matrix.
- ➢
- Step 10: Construction of the Discordance Dominance Matrix.
- ➢
- Step 11: Construction of the Aggregate Dominance Matrix.
- ➢
- Step 12: Determination of Outranking Relations.
3.1.2. Fuzzy Topsis
- ➢
- Step 1: Determination of Decision Matrix.
- ➢
- Step 2: Determine the Fuzzy Positive Ideal Solution (FPIS) and Fuzzy Negative Ideal Solutions (FNIS).
- ➢
- Step 3: Calculate the Distance from FPIS and FNIS.
- ➢
- Step 4: Compute the Closeness Coefficient (CC).
3.1.3. Fuzzy Vikor
- ➢
- Step 1: Determination of Decision Matrix.
- ➢
- Step 2: Determine Fuzzy Positive Ideal Solution (FPIS) and Fuzzy Negative Ideal Solution (FNIS).
- ➢
- Step 3: Compute the Distance from FPIS and FNIS.
- ➢
- Step 4: Calculate .
- ➢
- Step 5: Rank the Alternatives.
3.1.4. Fuzzy DEMATEL
- ➢
- Step 1: Define the problem and identify criteria.
- ➢
- Step 2: Construct the Direct-Relation Matrix.
- ➢
- Step 3: Normalize the Direct-Relation Matrix.
- ➢
- Step 4: Calculate the Total-Relation Matrix.
- ➢
- Step 5: Defuzzification.
- ➢
- Step 6: Calculate Prominence and Relation.
- ➢
- Step 7: Plot the Network Relationship Map (NRM)
3.2. Autoregressive Distributed Lag Model
3.3. Data Collection
4. Empirical Results
4.1. Fuzzy Electre
4.2. Fuzzy Topsis
4.3. Fuzzy DEMATEL
4.4. Fuzzy Vikor
4.5. Sensitivity Analysis of Fuzzy Results
4.6. Autoregressive Distributed Lag Model
5. Conclusions and Policy Recommendations
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
Acronym | Definition |
CE | Circular Economy |
NSCE | National Strategy for the Circular Economy |
MCDM | Multi-criteria decision making |
ELECTRE | Elimination and Choice Translating Reality |
TOPSIS | Technique for Order Preference by Similarity to Ideal Solution |
Vikor | Multi-criteria optimization and compromise solution |
DEMATEL | Decision-Making Trial and Evaluation Laboratory |
FPIS | Fuzzy Positive Ideal Solution |
FNIS | Fuzzy Negative Ideal Solution |
CC | Closeness Coefficient |
ARDL | Autoregressive Distributed Lag |
ECM | Error correction model |
ECT | Error correction term |
ADF | Augmented Dickey–Fuller |
VAR | Vector Autoregression |
FPE | Final prediction error |
AIC | Akaike information criterion |
SC | Schwarz information criterion |
HQ | Hannan–Quinn information criterion |
GDP | Gross domestic product |
PA | Patent Applications |
EPREN | Share of energy production from renewables |
URB | Urbanization |
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Authors, Year, References | Scope | Technique | Criteria |
---|---|---|---|
Husain et al., 2021, [28] | Classification of business models for the successful adoption of the CE | Fuzzy Topsis | Partnership; Activities; Resources; Value proposition; Customer Relationships; Distribution Channels; Client Segments; Cost structure; Revenue flows; |
Damgaci et al., 2017, [29] | Evaluation of Turkey’s Renewable Energy | Intuitionistic Fuzzy Topsis | Technical; Economical; Environmental; Social; |
Öztayşi and Kahraman, 2015, [30] | Evaluation of Renewable Energies Alternatives | Interval Type-2 Fuzzy AHP; Hesitant Fuzzy Topsis | Renewable energy factors; uncertainty; linguistic preference; |
Khan and Haleem, 2020, [31] | Identifying and evaluating key strategies for adopting circular economy practices | Fuzzy DEMATEL | 11 strategies for adopting the CE, including involving management, creating a vision and goals; |
Boran et al., 2012, [32] | Assessment of renewable energy technologies for electricity generation in Turkey | Intuitionistic Fuzzy Topsis | Renewable Energy Technologies: Photovoltaic, Hydro, Wind, Geothermal |
Kaya and Kahraman, 2010, [33] | Determining the best renewable energy alternative and optimal location for production in Istanbul | Integrated Fuzzy VIKOR-AHP | Criteria for the selection of renewable energy and location: technical, economic, geographical, social |
Li et al., 2024, [34] | Identifying the most suitable renewable energy source for Malaysia’s sustainable development | Fuzzy Multi-Criteria Decision Making (MCDM) based on cumulative prospect theory | Technology, economy, society, environment; Efficiency, payback period, job creation, CO2 emissions |
Riaz et al., 2023, [35] | Application of cubic bipolar fuzzy sets for the selection of the best renewable energy source | Cubic Bipolar Fuzzy Set (CBFS), CBF-VIKOR, Einstein averaging aggregation operators | Selection of renewable energy sources |
Simmhan et al., 2009, [36] | Evaluation of the development of the circular economy in the coal mining industry | Membership transformation algorithm, fuzzy evaluation | Developing the circular economy in coal mining, dynamic assessment |
Govindan et al., 2022, [37] | Prioritizing barriers to circular economy adoption in the cable and wire industry | Fuzzy Best-Worst Method (BWM), Fuzzy DEMATEL, Super matrix | Barriers to circular economy adoption: installation costs, financial limitations, lack of public awareness, etc. |
Ayçin and Kayapinar Kaya, 2021, [38] | Identification of barriers to the implementation of the zero-waste strategy in Turkey in the context of the circular economy | Fuzzy DEMATEL | 12 key barriers to zero waste implementation: uncertainty of goals, lack of financial aid, etc. |
Turgut and Tolga, 2018, [39] | Evaluation and selection of the best sustainable and/or renewable energy alternative | Fuzzy VIKOR, Fuzzy TODIM, Sensitivity Analysis | Renewable Energy: Solar, Wind, Hydroelectric, Storage Gas (LFG) |
Rejeb et al., 2022, [40] | Identifying and prioritizing barriers in the adoption of blockchain technology in the circular economy | Fuzzy Delphi, Best-Worst Method (BWM) | 16 barriers to blockchain adoption in the CE: lack of knowledge, reluctance to change, technological immaturity |
Khan and Ali, 2022, [41] | Creating a framework for the adoption of smart waste management in the context of the circular economy for Pakistan | Fuzzy SWARA, Fuzzy VIKOR | 16 critical enablers for the adoption of smart waste management, including regulations, industry responsibility, digitalization (ICT and IoT) |
Poonia et al., 2024, [42] | Development of a multi-objective mathematical model for the circular economy, integrating leasing and other strategies | Multi-objective Fuzzy Mixed Integer Linear Programming | Economic, environmental and social objectives; the concept of leasing, reuse, refurbishment, primary and secondary recycling |
Variable | Acronym | Measurement Unit | Source |
---|---|---|---|
emissions per capita | CO2 | Tons | Our World in Data [74] |
Patent applications | PA | Number | World Bank [75,76] |
Gross domestic product | GDP | Constant 2015 $USD | World Bank [77] |
Share of energy production from renewables | EPREN | % | Our World in Data [78] |
Urbanization | URB | % | World Bank [79] |
Criteria | Policy |
---|---|
Waste recycling rate | Waste management policy (P1): Studies show that effective waste management policies can significantly increase recycling rates. Implementation of these policies leads to more efficient waste management and reduced environmental impacts [80,81,82,83]. |
Installed capacity of renewable energy | Energy efficiency policy (P2): There is a direct link between energy efficiency policies and the increase in installed renewable energy capacity. This is due to investments in more efficient technologies and the transition to more sustainable energy sources [84,85,86,87]. |
Investments in CE technologies | Innovation and development (P3): Investments in innovation and development are essential to advance circular technologies. They enable the development of more efficient processes and products, thereby reducing the impact on resources [88,89,90,91,92]. |
Materials consumption per capita | Sustainable production and consumption (P4): By implementing policies that promote responsible consumption, per capita material consumption can be significantly reduced [93,94,95,96,97]. This includes consumer education and regulations that encourage resource efficiency [98,99]. |
GDP from circular activities | GDP growth through the CE (P5): studies show that economies that adopt circular models can see an increase in GDP due to innovation and the creation of new markets and jobs [100,101,102,103,104,105,106]. |
emissions per capita of GDP | Reducing emissions (P6): Policies to reduce emissions are fundamental to improving the carbon efficiency of the economy [107,108,109]. This is achieved by promoting green energy and optimizing industrial processes. |
Fuzzy Linguistic Terms | Triangular Fuzzy Number Interval |
---|---|
Very High Importance (VHI) | [0.8, 0.9, 1.0] |
High Importance (HI) | [0.7, 0.8, 0.9] |
Moderately High Importance (MHI) | [0.6, 0.7, 0.8] |
Medium Importance (MI) | [0.5, 0.6, 0.7] |
Moderately Low Importance (MLI) | [0.4, 0.5, 0.6] |
Low Importance (LI) | [0.3, 0.4, 0.5] |
Very Low Importance (VLI) | [0.2, 0.3, 0.4] |
Policy | Waste Recycling Rate | Installed Capacity of Renewable Energy | Investments in Circular Economy Technologies | Materials Consumption per Capita | GDP from Circular Activities | Emissions per Capita of GDP |
---|---|---|---|---|---|---|
Waste management (P1) | [0.7, 0.8, 0.9] (HI) | [0.3, 0.4, 0.5] (LI) | [0.4, 0.5, 0.6] (MLI) | [0.2, 0.3, 0.4] (VLI) | [0.6, 0.7, 0.8] (MHI) | [0.4, 0.5, 0.6] (MLI) |
Energy efficiency (P2) | [0.5, 0.6, 0.7] (MI) | [0.7, 0.8, 0.9] (HI) | [0.3, 0.4, 0.5] (LI) | [0.3, 0.4, 0.5] (LI) | [0.5, 0.6, 0.7] (MI) | [0.5, 0.6, 0.7] (MI) |
Innovation and development (P3) | [0.6, 0.7, 0.8] (MHI) | [0.5, 0.6, 0.7] (MI) | [0.8, 0.9, 1.00] (VHI) | [0.4, 0.5, 0.6] (MLI) | [0.6, 0.7, 0.8] (MHI) | [0.3, 0.4, 0.5] (LI) |
Sustainable production and consumption (P4) | [0.3, 0.4, 0.5] (LI) | [0.4, 0.5, 0.6] (MLI) | [0.5, 0.6, 0.7] (MI) | [0.7, 0.8, 0.9] (HI) | [0.4, 0.5, 0.6] (MLI) | [0.6, 0.7, 0.8] (MHI) |
GDP growth through CE (P5) | [0.4, 0.5, 0.6] (MLI) | [0.6, 0.7, 0.8] (MHI) | [0.7, 0.8, 0.9] (HI) | [0.5, 0.6, 0.7] (MI) | [0.8, 0.9, 1.00] (VHI) | [0.2, 0.3, 0.4] (VLI) |
Reducing CO2 emissions (P6) | [0.5, 0.6, 0.7] (MI) | [0.8, 0.9, 1.00] (VHI) | [0.6, 0.7, 0.8] (MHI) | [0.6, 0.7, 0.8] (MHI) | [0.7, 0.8, 0.9] (HI) | [0.4, 0.5, 0.6] (MLI) |
Criteria | Fuzzy Weights | Linguistic Term |
---|---|---|
Waste recycling rate | [0.1, 0.2, 0.3] | Moderate importance |
Installed capacity of renewable energy | [0.2, 0.3, 0.4] | Higher importance |
Investments in CE technologies | [0.15, 0.25, 0.35] | Medium importance |
Materials consumption per capita | [0.1, 0.2, 0.3] | Moderate importance |
GDP from circular activities | [0.25, 0.35, 0.45] | Higher importance |
emissions per capita of GDP | [0.2, 0.3, 0.4] | Higher importance |
P1 | P2 | P3 | P4 | P5 | P6 | |
---|---|---|---|---|---|---|
P1 | 0.00 | 0.50 | 0.50 | 0.33 | 0.33 | 0.33 |
P2 | 0.50 | 0.00 | 0.33 | 0.50 | 0.50 | 0.33 |
P3 | 0.67 | 0.67 | 0.00 | 0.67 | 0.50 | 0.33 |
P4 | 0.67 | 0.50 | 0.33 | 0.00 | 0.33 | 0.33 |
P5 | 0.67 | 0.50 | 0.50 | 0.67 | 0.00 | 0.33 |
P6 | 0.83 | 0.83 | 0.67 | 0.67 | 0.67 | 0.00 |
P1 | P2 | P3 | P4 | P5 | P6 | |
---|---|---|---|---|---|---|
P1 | 0.00 | 0.50 | 0.50 | 0.67 | 0.67 | 0.67 |
P2 | 0.50 | 0.00 | 0.67 | 0.50 | 0.50 | 0.67 |
P3 | 0.33 | 0.33 | 0.00 | 0.33 | 0.50 | 0.67 |
P4 | 0.33 | 0.50 | 0.67 | 0.00 | 0.67 | 0.67 |
P5 | 0.33 | 0.50 | 0.50 | 0.33 | 0.00 | 0.67 |
P6 | 0.17 | 0.17 | 0.33 | 0.33 | 0.33 | 0.00 |
Criteria | Closeness Coefficient (CC) | Rank |
---|---|---|
P1 | 0.35 | 6 |
P2 | 0.42 | 5 |
P3 | 0.53 | 3 |
P4 | 0.41 | 2 |
P5 | 0.56 | 4 |
P6 | 0.70 | 1 |
Criteria | Centrality (D + R) | Causality (D − R) |
---|---|---|
P1 | −2.61 | 0.24 |
P2 | −2.73 | 0.18 |
P3 | −2.97 | 0.14 |
P4 | −2.56 | −0.27 |
P5 | −3.11 | 0.24 |
P6 | −2.61 | −0.54 |
Policy | S | R | Q | Rank |
---|---|---|---|---|
P1 | 1.02 | 0.30 | 0.87 | 5 |
P2 | 0.90 | 0.35 | 0.67 | 4 |
P3 | 0.75 | 0.26 | 0.43 | 2 |
P4 | 0.94 | 0.15 | 0.92 | 6 |
P5 | 0.70 | 0.22 | 0.57 | 3 |
P6 | 0.47 | 0.30 | 0.00 | 1 |
CO2 | GDP | PA | URB | EPREN | |
---|---|---|---|---|---|
Mean | 1.53 | 8.82 | 7.18 | 3.98 | 3.44 |
Median | 1.51 | 8.87 | 7.05 | 3.98 | 3.38 |
Maximum | 2.04 | 9.42 | 8.02 | 4.00 | 3.92 |
Minimum | 1.31 | 8.30 | 6.57 | 3.96 | 2.87 |
Std. Dev. | 0.16 | 0.36 | 0.39 | 0.01 | 0.24 |
Skewness | 0.88 | 0.10 | 0.48 | −0.21 | −0.01 |
Kurtosis | 3.60 | 1.58 | 2.10 | 2.24 | 2.26 |
Jarque–Bera | 4.96 | 2.90 | 2.48 | 1.06 | 0.77 |
Probability | 0.08 | 0.23 | 0.28 | 0.58 | 0.68 |
Variables | Level | First Difference | Order of Integration |
---|---|---|---|
T-Statistics | T-Statistics | ||
CO2 | −3.31 ** (0.02) | −4.88 *** (0.00) | I (0) |
GDP | 0.88 (0.99) | −4.50 *** (0.00) | I (1) |
PA | −1.76 (0.39) | −5.16 *** (0.00) | I (1) |
URB | 0.00 (0.95) | −3.92 ** (0.02) | I (1) |
EPREN | −2.13 (0.23) | −5.52 *** (0.00) | I (1) |
Lag | LogL | LR | FPE | AIC | SC | HQ |
---|---|---|---|---|---|---|
0 | 163.43 | N/A | −10.22 | −9.99 | −10.14 | |
1 | 333.27 | 273.92 | −19.56 | −18.17 * | −19.11 | |
2 | 365.60 | 41.71 | −20.03 | −17.49 | −19.20 | |
3 | 411.86 | 44.77 * | * | −21.41 * | −17.71 | −20.20 * |
Tests | Engle–Granger (EG) | Johansen (J) | Banerjee (BA) | Boswijk (BO) |
---|---|---|---|---|
Test statistic | −3.47 | 66.72 | −7.69 | 98.99 |
p-value | 0.32 | 0.00 | 0.00 | 0.00 |
EG-J | 57.50 | 5% critical value, 10.57 | ||
EG-J-BA-BO | 168.02 | 5% critical value, 20.14 |
Test Statistic | Value | K (Number of Regressors) |
---|---|---|
F-statistic | 8.66 | 4 |
Critical value bounds | ||
Significance | I (0) | I (1) |
10% | 2.20 | 3.09 |
5% | 2.56 | 3.49 |
1% | 3.29 | 4.37 |
Variables | Coefficient | T-Statistics | Prob. |
---|---|---|---|
GDP | −0.14 | −0.91 | 0.38 |
PA | 0.35 | 2.48 | 0.03 ** |
URB | 9.23 | 3.70 | 0.00 *** |
EPREN | −0.42 | −3.24 | 0.00 *** |
C | −35.21 | −3.89 | 0.00 *** |
Variables | Coefficient | T-Statistics | Prob. |
---|---|---|---|
D(CO2(-1)) | 0.005 | 0.04 | 0.962 |
D(CO2(-2)) | 0.28 | 2.33 | 0.039 ** |
D(GDP) | 0.91 | −6.91 | 0.000 *** |
D(GDP(-1)) | 0.05 | 0.35 | 0.728 |
D(GDP(-2)) | 0.56 | 3.14 | 0.009 *** |
D(PA) | 0.15 | 4.83 | 0.005 *** |
D(PA(-1)) | −0.09 | −2.36 | 0.037 ** |
D(PA(-2)) | −0.26 | −6.91 | 0.000 *** |
D(URB) | 28.79 | 4.65 | 0.000 *** |
D(URB(-1)) | 3.07 | 0.38 | 0.704 |
D(URB(-2)) | −8.12 | −2.20 | 0.049 ** |
D(EPREN) | −0.30 | −6.75 | 0.000 *** |
D(EPREN(-1)) | 0.01 | 0.274 | 0.788 |
D(EPREN(-2)) | 0.18 | 3.85 | 0.002 *** |
CointEq(-1) | −0.72 | −8.62 | 0.000 *** |
R-squared | 0.93 | ||
Adjusted R-squared | 0.87 |
Diagnostic Test | Decision Statistic [p-Value] | |
---|---|---|
Serial Correlation | There is no serial correlation in the residuals | Accept 0.39 [0.54] |
Heteroscedasticity (GLEJSER) | There is no autoregressive conditional heteroscedasticity | Accept 0.96 [0.54] |
Jarque–Bera | Normal distribution | Accept 1.15 [0.56] |
Ramsey Reset | Absence of model misspecification | Accept 0.56 [0.58] |
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Delcea, C.; Nica, I.; Georgescu, I.; Chiriță, N.; Ciurea, C. Integrating Fuzzy MCDM Methods and ARDL Approach for Circular Economy Strategy Analysis in Romania. Mathematics 2024, 12, 2997. https://doi.org/10.3390/math12192997
Delcea C, Nica I, Georgescu I, Chiriță N, Ciurea C. Integrating Fuzzy MCDM Methods and ARDL Approach for Circular Economy Strategy Analysis in Romania. Mathematics. 2024; 12(19):2997. https://doi.org/10.3390/math12192997
Chicago/Turabian StyleDelcea, Camelia, Ionuț Nica, Irina Georgescu, Nora Chiriță, and Cristian Ciurea. 2024. "Integrating Fuzzy MCDM Methods and ARDL Approach for Circular Economy Strategy Analysis in Romania" Mathematics 12, no. 19: 2997. https://doi.org/10.3390/math12192997
APA StyleDelcea, C., Nica, I., Georgescu, I., Chiriță, N., & Ciurea, C. (2024). Integrating Fuzzy MCDM Methods and ARDL Approach for Circular Economy Strategy Analysis in Romania. Mathematics, 12(19), 2997. https://doi.org/10.3390/math12192997