# An integrated Multi-Criteria Decision Making Model for Sustainability Performance Assessment for Insurance Companies

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## Abstract

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## 1. Introduction

## 2. Literature Review

## 3. Method

#### 3.1. AHP–PCA Model

**Step 1:**Normalize the ratio matrix.

**Step 2:**Calculate the sample correlation matrix.

**Step 3:**Solve the following equation: $\left|R-\lambda {I}_{p}\right|=0,$ where i is a p × p identity matrix. We obtain the ordered p characteristic roots (eigenvalues) ${\lambda}_{1}\ge {\lambda}_{2}\ge \dots \ge {\lambda}_{p}$ with $\sum}_{k=1}^{p}{\lambda}_{k}=p$ and the related p characteristic vectors (${l}_{1}^{k},{l}_{2}^{k},\dots ,{l}_{p}^{k}$) (k = 1, …, p). Those characteristic vectors compose the principal components $p{c}_{k}$. as in Equation (5):

**Step 4:**Select the principal component by defining $\rho =\raisebox{1ex}{${{\displaystyle \sum}}_{k=1}^{M}{\lambda}_{k}$}\!\left/ \!\raisebox{-1ex}{${{\displaystyle \sum}}_{k=1}^{p}{\lambda}_{k}$}\right.=\raisebox{1ex}{${{\displaystyle \sum}}_{k=1}^{M}{\lambda}_{k}$}\!\left/ \!\raisebox{-1ex}{$p$}\right.$. The first M components may be selected by satisfying, e.g., $\rho >90\%$, i.e., the first M principal components account for 90% of the contribution to the total sample variance (the other proposal to select the principal components can be $\lambda >1$).

**Step 5**: Establish the principal of weighted PCA.

#### 3.2. Modified DEA Model

_{o}, a simplified input-oriented CCR model was proposed, as follows in Equation (10):

^{th}principal component carries the total dispersion more than the kth one does. To obtain the efficiency of the DMUs, model Equation (11) must be calculated separately for each DMU. Therefore, a new model is proposed to save time and calculate the efficiency easily in this paper. The proposed model is as follows:

## 4. Empirical Study

_{14}included two important indexes in comparison to ${d}_{30}$. However, ${w}_{{d}_{30}\text{}}$was more important than ${w}_{{d}_{14}\text{}}$, which has inconsistency with the decision-maker (DM) view. In contrast, by multiplying the input and output weights, the results obtained for d

_{14}and d

_{30}were more reasonable, as shown below:

## 5. Results and Discussion

_{0}greater than one. Three DMUs, namely DMU2 (Dana), DMU8 (Razi), and DMU11 (Dey), showed the highest performance. Among them, DMU2 (Dana) was the most efficient, and it achieved the best ranking. Also, in the PCA–DEA model, two units, DMU11 (Dey) and DMU2 (Dana), had an efficiency score higher than one. In this model, DMU2 (Dana) was dedicated to the best ranking too. At the end of 2018, Dana insurance company was converted to a private company. Therefore, with changing management strategy during 2019, it increased by 73.1% in attracting premiums, which shows public confidence in the company in the insurance market. In fact, this company can get 9% of the market share in this index. Also, it increased its performance in rights of equities and investment income by about 50% and in d

_{7}(32.7), which is very important, it had the highest performance. All of these indicators are important for decision makers and play a vital role in the ranks of DMUs. Therefore, Dana insurance company is the most sustainable company according to the indicators.

## 6. Limitations

## 7. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## Correction Statement

## References

- Jorgenson, A.K.; Clark, B. Societies consuming nature: A panel study of the ecological footprints of nations, 1960–2003. Soc. Sci. Res.
**2011**, 40, 226–244. [Google Scholar] [CrossRef] - Li, H.; Dong, K.; Sun, R.; Yu, J.; Xu, J. Sustainability assessment of refining enterprises using a DEA-based model. Sustainability
**2017**, 9, 620. [Google Scholar] [CrossRef] - Gaziulusoy, I.; Erdoğan Öztekin, E. Design for sustainability transitions: Origins, attitudes and future directions. Sustainability
**2019**, 11, 3601. [Google Scholar] [CrossRef] - Alkhayyal, B.; Labib, W.; Alsulaiman, T.; Abdelhadi, A. Analyzing Sustainability Awareness among Higher Education Faculty Members: A Case Study in Saudi Arabia. Sustainability
**2019**, 11, 6837. [Google Scholar] [CrossRef] - McGinley, K.A.; Robertson, G.C.; Friday, K.S. Examining the Sustainability of Tropical Island Forests: Advances and Challenges in Measurement, Monitoring, and Reporting in the US Caribbean and Pacific. Forests
**2019**, 10, 946. [Google Scholar] [CrossRef] - Herghiligiu, I.V.; Robu, I.-B.; Pislaru, M.; Vilcu, A.; Asandului, A.L.; Avasilcăi, S.; Balan, C. Sustainable Environmental Management System Integration and Business Performance: A Balance Assessment Approach Using Fuzzy Logic. Sustainability
**2019**, 11, 5311. [Google Scholar] [CrossRef] - Fadel, C.; Tarabieh, K. Development of an Industrial Environmental Index to Assess the Sustainability of Industrial Solvent-Based Processes. Resources
**2019**, 8, 115. [Google Scholar] [CrossRef] - Brambilla, A.; Capolongo, S. Healthy and Sustainable Hospital Evaluation—A Review of POE Tools for Hospital Assessment in an Evidence-Based Design Framework. Buildings
**2019**, 9, 76. [Google Scholar] [CrossRef] - Doğu, F.U.; Aras, L. Measuring Social Sustainability with the Developed MCSA Model: Güzelyurt Case. Sustainability
**2019**, 11, 2503. [Google Scholar] [CrossRef] - Lin, A.J.; Chang, H.-Y. Business Sustainability Performance Evaluation for Taiwanese Banks—A Hybrid Multiple-Criteria Decision-Making Approach. Sustainability
**2019**, 11, 2236. [Google Scholar] [CrossRef] - Farooq, O.; Farooq, M.; Reynaud, E. Does Employees’ Participation in Decision Making Increase the level of Corporate Social and Environmental Sustainability? An Investigation in South Asia. Sustainability
**2019**, 11, 511. [Google Scholar] [CrossRef] - Singh, A.; Kar, S.; Pamucar, D. Stakeholder Role for Developing a Conceptual Framework of Sustainability in Organization. Sustainability
**2019**, 11, 208. [Google Scholar] [CrossRef] - Fagerlind, T.; Stefanicki, M.; Feldmann, A.; Korhonen, J. The Distribution of Sustainable Decision-Making in Multinational Manufacturing Enterprises. Sustainability
**2019**, 11, 4871. [Google Scholar] [CrossRef] - Zhang, Y.; Khan, U.; Lee, S.; Salik, M. The Influence of Management Innovation and Technological Innovation on Organization Performance. A Mediating Role of Sustainability. Sustainability
**2019**, 11, 495. [Google Scholar] [CrossRef] - Northey, S.; Haque, N.; Mudd, G. Using sustainability reporting to assess the environmental footprint of copper mining. J. Clean. Prod.
**2013**, 40, 118–128. [Google Scholar] [CrossRef] - Esty, D.C.; Emerson, J.W. Yale’s Environmental Performance Index and the rise of data-driven policymaking. In Routledge Handbook of Sustainability Indicators; Routledge: Abingdon, UK, 2018. [Google Scholar]
- Waheed, B.; Khan, F.; Veitch, B. Linkage-based frameworks for sustainability assessment: making a case for driving force-pressure-state-exposure-effect-action (DPSEEA) frameworks. Sustainability
**2009**, 1, 441–463. [Google Scholar] [CrossRef] - Büyüközkan, G.; Karabulut, Y. Sustainability performance evaluation: Literature review and future directions. J. Environ. Manag.
**2018**, 217, 253–267. [Google Scholar] [CrossRef] - Hellström, T. Dimensions of environmentally sustainable innovation: The structure of eco-innovation concepts. Sustain. Dev.
**2007**, 15, 148–159. [Google Scholar] [CrossRef] - Cavallaro, F.; Zavadskas, E.K.; Streimikiene, D.; Mardani, A. Assessment of concentrated solar power (CSP) technologies based on a modified intuitionistic fuzzy topsis and trigonometric entropy weights. Technol. Forecast. Soc. Chang.
**2019**, 140, 258–270. [Google Scholar] [CrossRef] - Cavallaro, F.; Zavadskas, E.K.; Streimikiene, D. Concentrated solar power (CSP) hybridized systems. Ranking based on an intuitionistic fuzzy multi-criteria algorithm. J. Clean. Prod.
**2018**, 179, 407–416. [Google Scholar] [CrossRef] - Cavallaro, F. A Takagi-Sugeno fuzzy inference system for developing a sustainability index of biomass. Sustainability
**2015**, 7, 12359–12371. [Google Scholar] [CrossRef] - Nilashi, M.; Cavallaro, F.; Mardani, A.; Zavadskas, E.; Samad, S.; Ibrahim, O. Measuring Country Sustainability Performance Using Ensembles of Neuro-Fuzzy Technique. Sustainability
**2018**, 10, 2707. [Google Scholar] [CrossRef] - Singh, R.K.; Murty, H.; Gupta, S.; Dikshit, A. Development of composite sustainability performance index for steel industry. Ecol. Indic.
**2007**, 7, 565–588. [Google Scholar] [CrossRef] - Tsolas, I. Derivation of mineral processing environmental sustainability indicators using a DEA weight-restricted algorithm. Min. Metall. Explor.
**2008**, 25, 199–205. [Google Scholar] [CrossRef] - Azapagic, A. Developing a framework for sustainable development indicators for the mining and minerals industry. J. Clean. Prod.
**2004**, 12, 639–662. [Google Scholar] [CrossRef] - Alizadeh, R.; Soltanisehat, L. Stay competitive in 2035: A scenario-based method to foresight in the design and manufacturing industry. Foresight
**2020**. in Press. [Google Scholar] [CrossRef] - Soltanisehat, L.; Alizadeh, R.; Mehregan, N. Research and Development Investment and Productivity Growth in Firms with Different Levels of Technology. Iran. Econ. Rev.
**2019**, 23, 795–818. [Google Scholar] - Zamani Sabzi, H.; Abudu, S.; Alizadeh, R.; Soltanisehat, L.; Dilekli, N.; King, J.P. Integration of time series forecasting in a dynamic decision support system for multiple reservoir management to conserve water sources. Energy Sour. Part A Recover. Util. Environ. Effects
**2018**, 40, 1398–1416. [Google Scholar] [CrossRef] - Krajnc, D.; Glavič, P. A model for integrated assessment of sustainable development. Resour. Conserv. Recycl.
**2005**, 43, 189–208. [Google Scholar] [CrossRef] - Alizadeh, R.; Maknoon, R.; Majidpour, M.; Salimi, J. Energy Policy in Iran and International Commitments for GHG Emission Reduction. J. Environ. Sci. Technol.
**2015**, 17, 183–198. [Google Scholar] - Alizadeh, R.; Soltanisehat, L.; Lund, P.D.; Zamanisabzi, H. Improving renewable energy policy planning and decision-making through a hybrid MCDM method. Energy Policy
**2019**. in Press, No. 111174. [Google Scholar] [CrossRef] - Tahir, A.C.; Darton, R. The process analysis method of selecting indicators to quantify the sustainability performance of a business operation. J. Clean. Prod.
**2010**, 18, 1598–1607. [Google Scholar] [CrossRef] - Hsu, C.-H.; Chang, A.-Y.; Luo, W. Identifying key performance factors for sustainability development of SMEs–integrating QFD and fuzzy MADM methods. J. Clean. Prod.
**2017**, 161, 629–645. [Google Scholar] [CrossRef] - Crutzen, N.; Zvezdov, D.; Schaltegger, S. Sustainability and management control. Exploring and theorizing control patterns in large European firms. J. Clean. Prod.
**2017**, 143, 1291–1301. [Google Scholar] [CrossRef] - Closs, D.J.; Speier, C.; Meacham, N. Sustainability to support end-to-end value chains: The role of supply chain management. J. Acad. Mark. Sci.
**2011**, 39, 101–116. [Google Scholar] [CrossRef] - Beynaghi, A.; Moztarzadeh, F.; Shahmardan, A.; Alizadeh, R.; Salimi, J.; Mozafari, M. Makespan minimization for batching work and rework process on a single facility with an aging effect: A hybrid meta-heuristic algorithm for sustainable production management. J. Intell. Manuf.
**2019**, 30, 33–45. [Google Scholar] [CrossRef] - Alizadeh, R.; Lund, P.D.; Beynaghi, A.; Abolghasemi, M.; Maknoon, R. An integrated scenario-based robust planning approach for foresight and strategic management with application to energy industry. Technol. Forecast. Soc. Chang.
**2016**, 104, 162–171. [Google Scholar] [CrossRef] - Alizadeh, R.; Khodaei, R.; Maknoon, R. A Combined Model of Scenario Planning and Assumption-Based Planning for Futurology, and Robust Decision Making in the Energy Sector. Q. J. Energy Policy Plan. Res.
**2016**, 2, 7–32. [Google Scholar] - Alizadeh, R.; Majidpour, M.; Maknoon, R.; Kaleibari, S.S. Clean development mechanism in Iran: Does it need a revival? Int. J. Glob. Warm.
**2016**, 10, 196–215. [Google Scholar] [CrossRef] - Alizadeh, R.; Majidpour, M.; Maknoon, R.; Salimi, J. Iranian energy and climate policies adaptation to the Kyoto protocol. Int. J. Environ. Res.
**2015**, 9, 853–864. [Google Scholar] - Abolghasemi, M.; Alizadeh, R. A Bayesian Framework for Strategic Management In The Energy Industry. Int. J. Sci. Eng. Technol.
**2014**, 3, 1360–1366. [Google Scholar] - Nigri, G.; Del Baldo, M. Sustainability Reporting and Performance Measurement Systems: How do Small-and Medium-Sized Benefit Corporations Manage Integration? Sustainability
**2018**, 10, 4499. [Google Scholar] [CrossRef] - Jassem, S.; Azmi, A.; Zakaria, Z. Impact of Sustainability Balanced Scorecard Types on Environmental Investment Decision-Making. Sustainability
**2018**, 10, 541. [Google Scholar] [CrossRef] - Hristov, I.; Chirico, A.; Appolloni, A. Sustainability Value Creation, Survival, and Growth of the Company: A Critical Perspective in the Sustainability Balanced Scorecard (SBSC). Sustainability
**2019**, 11, 2119. [Google Scholar] [CrossRef] - Chung, C.-C.; Chao, L.-C.; Chen, C.-H.; Lou, S.-J. A balanced scorecard of sustainable management in the Taiwanese bicycle industry: Development of performance indicators and importance analysis. Sustainability
**2016**, 8, 518. [Google Scholar] [CrossRef] - Barrena Martínez, J.; López Fernández, M.; Romero Fernández, P.M. Corporate social responsibility: Evolution through institutional and stakeholder perspectives. Eur. J. Manag. Bus. Econ.
**2016**, 25, 8–14. [Google Scholar] [CrossRef] - Putzhuber, F.; Hasenauer, H. Deriving sustainability measures using statistical data: A case study from the Eisenwurzen, Austria. Ecol. Indic.
**2010**, 10, 32–38. [Google Scholar] [CrossRef] - Hung, S.-W.; He, D.-S.; Lu, W.-M. Evaluating the dynamic performances of business groups from the carry-over perspective: A case study of Taiwan׳s semiconductor industry. Omega
**2014**, 46, 1–10. [Google Scholar] [CrossRef] - Hatami-Marbini, A.; Kangi, F. An extension of fuzzy TOPSIS for a group decision making with an application to Tehran stock exchange. Appl. Soft Comput.
**2017**, 52, 1084–1097. [Google Scholar] [CrossRef] - Hsu, L.-C. Using a decision-making process to evaluate efficiency and operating performance for listed semiconductor companies. Technol. Econ. Dev. Econ.
**2015**, 21, 301–331. [Google Scholar] [CrossRef] - Tsai, C.-H.; Wu, H.-Y.; Chen, I.-S.; Chen, J.-K.; Ye, R.-W. Exploring benchmark corporations in the semiconductor industry based on efficiency. J. High Technol. Manag. Res.
**2017**, 28, 188–207. [Google Scholar] [CrossRef] - Zhou, H.; Hu, H. Sustainability evaluation of railways in China using a two-stage network DEA model with undesirable outputs and shared resources. Sustainability
**2017**, 9, 150. [Google Scholar] [CrossRef] - Halkos, G.E.; Tzeremes, N.G.; Kourtzidis, S.A. Measuring sustainability efficiency using a two-stage data envelopment analysis approach. J. Ind. Ecol.
**2016**, 20, 1159–1175. [Google Scholar] [CrossRef] - Tajbakhsh, A.; Hassini, E. Evaluating sustainability performance in fossil-fuel power plants using a two-stage data envelopment analysis. Energy Econ.
**2018**, 74, 154–178. [Google Scholar] [CrossRef] - Wu, J.; Yin, P.; Sun, J.; Chu, J.; Liang, L. Evaluating the environmental efficiency of a two-stage system with undesired outputs by a DEA approach: An interest preference perspective. Eur. J. Oper. Res.
**2016**, 254, 1047–1062. [Google Scholar] [CrossRef] - Hatami-Marbini, A.; Agrell, P.J.; Tavana, M.; Khoshnevis, P. A flexible cross-efficiency fuzzy data envelopment analysis model for sustainable sourcing. J. Clean. Prod.
**2017**, 142, 2761–2779. [Google Scholar] [CrossRef] - Hatami-Marbini, A.; Tavana, M.; Gholami, K.; Beigi, Z.G. A bounded data envelopment analysis model in a fuzzy environment with an application to safety in the semiconductor industry. J. Optim. Theory Appl.
**2015**, 164, 679–701. [Google Scholar] [CrossRef] - Chen, L.; Wang, Y.-M.; Lai, F. Semi-disposability of undesirable outputs in data envelopment analysis for environmental assessments. Eur. J. Oper. Res.
**2017**, 260, 655–664. [Google Scholar] [CrossRef] - Li, H.; He, H.; Shan, J.; Cai, J. Innovation efficiency of semiconductor industry in China: A new framework based on generalized three-stage DEA analysis. Socio Econ. Plan. Sci.
**2019**, 66, 136–148. [Google Scholar] [CrossRef] - Tourais, P.; Videira, N. Why, how and what do organizations achieve with the implementation of environmental management Systems?—Lessons from a comprehensive review on the eco-management and audit scheme. Sustainability
**2016**, 8, 283. [Google Scholar] [CrossRef] - Liu, G. Development of a general sustainability indicator for renewable energy systems: A review. Renew. Sustain. Energy Rev.
**2014**, 31, 611–621. [Google Scholar] [CrossRef] - Chen, L.; Lai, F.; Wang, Y.-M.; Huang, Y.; Wu, F.-M. A two-stage network data envelopment analysis approach for measuring and decomposing environmental efficiency. Comput. Ind. Eng.
**2018**, 119, 388–403. [Google Scholar] [CrossRef] - De Clercq, D.; Wen, Z.; Caicedo, L.; Cao, X.; Fan, F.; Xu, R. Application of DEA and statistical inference to model the determinants of biomethane production efficiency: A case study in south China. Appl. Energy
**2017**, 205, 1231–1243. [Google Scholar] [CrossRef] - Pham, M.D.; Zelenyuk, V. Weak disposability in nonparametric production analysis: A new taxonomy of reference technology sets. Eur. J. Oper. Res.
**2019**, 274, 186–198. [Google Scholar] [CrossRef] - Essid, H.; Ganouati, J.; Vigeant, S. A mean-maverick game cross-efficiency approach to portfolio selection: An application to Paris stock exchange. Expert Syst. Appl.
**2018**, 113, 161–185. [Google Scholar] [CrossRef] - Chen, L.; Wu, F.M.; Wang, Y.M.; Li, M.J. Analysis of the environmental efficiency in China based on the DEA cross-efficiency approach under different policy objectives. Expert Syst.
**2019**. [Google Scholar] [CrossRef] - Amirteimoori, H.; Amirteimoori, A.; Amirteimoori, A. Sustainability assessment in the presence of undesirable factors over time: A case on gas companies. Expert Syst.
**2018**, e12316. [Google Scholar] [CrossRef] - Sueyoshi, T.; Goto, M. The intermediate approach to sustainability enhancement and scale-related measures in environmental assessment. Eur. J. Oper. Res.
**2019**, 276, 744–756. [Google Scholar] [CrossRef] - Wu, M.-Q.; Zhang, C.-H.; Liu, X.-N.; Fan, J.-P. Green supplier selection based on DEA model in interval-valued Pythagorean fuzzy environment. IEEE Access
**2019**, 7, 108001–108013. [Google Scholar] [CrossRef] - Sueyoshi, T.; Li, A.; Liu, X. Exploring Sources of China’s CO
_{2}Emission: Decomposition Analysis under Different Technology Changes. Eur. J. Oper. Res.**2019**, 279, 984–995. [Google Scholar] [CrossRef] - Sueyoshi, T.; Wang, D.D. Rank dynamics and club convergence of sustainable development for countries around the world. J. Clean. Prod.
**2019**, 119480. [Google Scholar] [CrossRef] - Azadi, M.; Jafarian, M.; Saen, R.F.; Mirhedayatian, S.M. A new fuzzy DEA model for evaluation of efficiency and effectiveness of suppliers in sustainable supply chain management context. Comput. Oper. Res.
**2015**, 54, 274–285. [Google Scholar] [CrossRef] - Jauhar, S.K.; Pant, M. Integrating DEA with DE and MODE for sustainable supplier selection. J. Comput. Sci.
**2017**, 21, 299–306. [Google Scholar] [CrossRef] - Dobos, I.; Vörösmarty, G. Green supplier selection and evaluation using DEA-type composite indicators. Int. J. Prod. Econ.
**2014**, 157, 273–278. [Google Scholar] [CrossRef] - Zhou, X.; Pedrycz, W.; Kuang, Y.; Zhang, Z. Type-2 fuzzy multi-objective DEA model: An application to sustainable supplier evaluation. Appl. Soft Comput.
**2016**, 46, 424–440. [Google Scholar] [CrossRef] - Shabanpour, H.; Yousefi, S.; Saen, R.F. Future planning for benchmarking and ranking sustainable suppliers using goal programming and robust double frontiers DEA. Transp. Res. Part D Transp. Environ.
**2017**, 50, 129–143. [Google Scholar] [CrossRef] - Yousefi, S.; Soltani, R.; Saen, R.F.; Pishvaee, M.S. A robust fuzzy possibilistic programming for a new network GP-DEA model to evaluate sustainable supply chains. J. Clean. Prod.
**2017**, 166, 537–549. [Google Scholar] [CrossRef] - Badiezadeh, T.; Saen, R.F.; Samavati, T. Assessing sustainability of supply chains by double frontier network DEA: A big data approach. Comput. Oper. Res.
**2018**, 98, 284–290. [Google Scholar] [CrossRef] - Luthra, S.; Govindan, K.; Kannan, D.; Mangla, S.K.; Garg, C.P. An integrated framework for sustainable supplier selection and evaluation in supply chains. J. Clean. Prod.
**2017**, 140, 1686–1698. [Google Scholar] [CrossRef] - Rashidi, K.; Cullinane, K. A comparison of fuzzy DEA and fuzzy TOPSIS in sustainable supplier selection: Implications for sourcing strategy. Expert Syst. Appl.
**2019**, 121, 266–281. [Google Scholar] [CrossRef] - Mavi, R.K.; Saen, R.F.; Goh, M. Joint analysis of eco-efficiency and eco-innovation with common weights in two-stage network DEA: A big data approach. Technol. Forecast. Soc. Chang.
**2019**, 144, 553–562. [Google Scholar] [CrossRef] - Liu, X.; Guo, P.; Guo, S. Assessing the eco-efficiency of a circular economy system in China’s coal mining areas: Emergy and data envelopment analysis. J. Clean. Prod.
**2019**, 206, 1101–1109. [Google Scholar] [CrossRef] - Wang, X.; Ding, H.; Liu, L. Eco-efficiency measurement of industrial sectors in China: A hybrid super-efficiency DEA analysis. J. Clean. Prod.
**2019**, 229, 53–64. [Google Scholar] [CrossRef] - Ezici, B.; Eğilmez, G.; Gedik, R. Assessing the eco-efficiency of U.S. manufacturing industries with a focus on renewable vs. non-renewable energy use: An integrated time series MRIO and DEA approach. J. Clean. Prod.
**2020**, 253, 119630. [Google Scholar] [CrossRef] - Shao, L.; Yu, X.; Feng, C. Evaluating the eco-efficiency of China’s industrial sectors: A two-stage network data envelopment analysis. J. Environ. Manag.
**2019**, 247, 551–560. [Google Scholar] [CrossRef] - Hu, W.; Guo, Y.; Tian, J.; Chen, L. Eco-efficiency of centralized wastewater treatment plants in industrial parks: A slack-based data envelopment analysis. Resour. Conserv. Recycl.
**2019**, 141, 176–186. [Google Scholar] [CrossRef] - Torres-Ruiz, A.; Ravindran, A.R. Use of interval data envelopment analysis, goal programming and dynamic eco-efficiency assessment for sustainable supplier management. Comput. Ind. Eng.
**2019**, 131, 211–226. [Google Scholar] [CrossRef] - Zhu, W.; Xu, L.; Tang, L.; Xiang, X. Eco-efficiency of the Western Taiwan Straits Economic Zone: An evaluation based on a novel eco-efficiency model and empirical analysis of influencing factors. J. Clean. Prod.
**2019**, 234, 638–652. [Google Scholar] [CrossRef] - Yu, Y.; Huang, J.; Zhang, N. Modeling the eco-efficiency of Chinese prefecture-level cities with regional heterogeneities: A comparative perspective. Ecol. Model.
**2019**, 402, 1–17. [Google Scholar] [CrossRef] - Bang, Y.-Y.; Lee, D.S.; Lim, S.-R. Analysis of corporate CO
_{2}and energy cost efficiency: The role of performance indicators and effective environmental reporting. Energy Policy**2019**, 133, 110897. [Google Scholar] [CrossRef] - Wu, J.; Li, M.; Zhu, Q.; Zhou, Z.; Liang, L. Energy and environmental efficiency measurement of China’s industrial sectors: A DEA model with non-homogeneous inputs and outputs. Energy Econ.
**2019**, 78, 468–480. [Google Scholar] [CrossRef] - Bai, X.; Salim, R.; Bloch, H. Environmental Efficiency of Apple Production in China: A Translog Stochastic Frontier Analysis. Agric. Resour. Econ. Rev.
**2019**, 48, 199–220. [Google Scholar] [CrossRef] - Halkos, G.; Petrou, K.N. Assessing 28 EU member states’ environmental efficiency in national waste generation with DEA. J. Clean. Prod.
**2019**, 208, 509–521. [Google Scholar] [CrossRef] - Zhou, Z.; Wu, H.; Song, P. Measuring the resource and environmental efficiency of industrial water consumption in China: A non-radial directional distance function. J. Clean. Prod.
**2019**, 240, 118169. [Google Scholar] [CrossRef] - Zhang, J.; Wu, Q.; Zhou, Z. A two-stage DEA model for resource allocation in industrial pollution treatment and its application in China. J. Clean. Prod.
**2019**, 228, 29–39. [Google Scholar] [CrossRef] - Liu, X.; Ji, X.; Zhang, D.; Yang, J.; Wang, Y. How public environmental concern affects the sustainable development of Chinese cities: An empirical study using extended DEA models. J. Environ. Manag.
**2019**, 251, 109619. [Google Scholar] [CrossRef] [PubMed] - Liu, X.; Chu, J.; Yin, P.; Sun, J. DEA cross-efficiency evaluation considering undesirable output and ranking priority: A case study of eco-efficiency analysis of coal-fired power plants. J. Clean. Prod.
**2017**, 142, 877–885. [Google Scholar] [CrossRef] - Long, X.; Wu, C.; Zhang, J.; Zhang, J. Environmental efficiency for 192 thermal power plants in the Yangtze River Delta considering heterogeneity: A metafrontier directional slacks-based measure approach. Renew. Sustain. Energy Rev.
**2018**, 82, 3962–3971. [Google Scholar] [CrossRef] - Jiang, Z.; Ding, Z.; Zhang, H.; Cai, W.; Liu, Y. Data-driven ecological performance evaluation for remanufacturing process. Energy Convers. Manag.
**2019**, 198, 111844. [Google Scholar] [CrossRef] - Gémar, G.; Gómez, T.; Molinos-Senante, M.; Caballero, R.; Sala-Garrido, R. Assessing changes in eco-productivity of wastewater treatment plants: The role of costs, pollutant removal efficiency, and greenhouse gas emissions. Environ. Impact Assess. Rev.
**2018**, 69, 24–31. [Google Scholar] [CrossRef] - Wu, Y.; Ke, Y.; Zhang, T.; Liu, F.; Wang, J. Performance efficiency assessment of photovoltaic poverty alleviation projects in China: A three-phase data envelopment analysis model. Energy
**2018**, 159, 599–610. [Google Scholar] [CrossRef] - Li, A.; Zhang, A.; Huang, H.; Yao, X. Measuring unified efficiency of fossil fuel power plants across provinces in China: An analysis based on non-radial directional distance functions. Energy
**2018**, 152, 549–561. [Google Scholar] [CrossRef] - Liu, H.; Wu, J.; Chu, J. Environmental efficiency and technological progress of transportation industry-based on large scale data. Technol. Forecast. Soc. Chang.
**2019**, 144, 475–482. [Google Scholar] [CrossRef] - Sun, C.; Liu, X.; Li, A. Measuring unified efficiency of Chinese fossil fuel power plants: Intermediate approach combined with group heterogeneity and window analysis. Energy Policy
**2018**, 123, 8–18. [Google Scholar] [CrossRef] - Chu, J.; Wu, J.; Chu, C. A multi-objective model for Pareto optimality in data envelopment analysis cross-efficiency evaluation. Eur. J. Oper. Res.
**2019**, 274, 471–499. [Google Scholar] [CrossRef] - Saaty, T.L. How to make a decision: The analytic hierarchy process. Eur. J. Oper. Res.
**1990**, 48, 9–26. [Google Scholar] [CrossRef] - Venkatesh, A.; Kushwaha, S. Measuring technical efficiency of passenger bus companies in India: A non-radial data envelopment analysis approach. OPSEARCH
**2017**, 54, 706–723. [Google Scholar] [CrossRef] - Bhatia, V.; Basu, S.; Mitra, S.K.; Dash, P. A review of bank efficiency and productivity. OPSEARCH
**2018**, 55, 557–600. [Google Scholar] [CrossRef] - Bose, A.; Patel, G.N. “NeuralDEA”—A framework using Neural Network to re-evaluate DEA benchmarks. OPSEARCH
**2015**, 52, 18–41. [Google Scholar] [CrossRef] - Charnes, A.; Cooper, W.W.; Rhodes, E. Measuring the efficiency of decision making units. Eur. J. Oper. Res.
**1978**, 2, 429–444. [Google Scholar] [CrossRef] - Agarwal, S. DEA-neural networks approach to assess the performance of public transport sector of India. OPSEARCH
**2016**, 53, 248–258. [Google Scholar] [CrossRef] - Liu, J.S.; Lu, L.Y.; Lu, W.-M. Research fronts in data envelopment analysis. Omega
**2016**, 58, 33–45. [Google Scholar] [CrossRef] - Wei, G.; Wang, J. A comparative study of robust efficiency analysis and data envelopment analysis with imprecise data. Expert Syst. Appl.
**2017**, 81, 28–38. [Google Scholar] [CrossRef] - Mardani, A.; Zavadskas, E.K.; Streimikiene, D.; Jusoh, A.; Khoshnoudi, M. A comprehensive review of data envelopment analysis (DEA) approach in energy efficiency. Renew. Sustain. Energy Rev.
**2017**, 70, 1298–1322. [Google Scholar] [CrossRef] - Chen, L.; Jia, G. Environmental efficiency analysis of China’s regional industry: A data envelopment analysis (DEA) based approach. J. Clean. Prod.
**2017**, 142, 846–853. [Google Scholar] [CrossRef] - Olesen, O.B.; Petersen, N.C. Stochastic data envelopment analysis—A review. Eur. J. Oper. Res.
**2016**, 251, 2–21. [Google Scholar] [CrossRef] - Banker, R.; Natarajan, R.; Zhang, D. Two-stage estimation of the impact of contextual variables in stochastic frontier production function models using data envelopment analysis: Second stage OLS versus bootstrap approaches. Eur. J. Oper. Res.
**2019**, 278, 368–384. [Google Scholar] [CrossRef] - Zhou, H.; Yang, Y.; Chen, Y.; Zhu, J. Data envelopment analysis application in sustainability: The origins, development and future directions. Eur. J. Oper. Res.
**2018**, 264, 1–16. [Google Scholar] [CrossRef] - Babazadeh, R.; Razmi, J.; Rabbani, M.; Pishvaee, M.S. An integrated data envelopment analysis–mathematical programming approach to strategic biodiesel supply chain network design problem. J. Clean. Prod.
**2017**, 147, 694–707. [Google Scholar] [CrossRef] - Sagarra, M.; Mar-Molinero, C.; Agasisti, T. Exploring the efficiency of Mexican universities: Integrating data envelopment analysis and multidimensional scaling. Omega
**2017**, 67, 123–133. [Google Scholar] [CrossRef] - Jradi, S.; Ruggiero, J. Stochastic data envelopment analysis: A quantile regression approach to estimate the production frontier. Eur. J. Oper. Res.
**2019**, 278, 385–393. [Google Scholar] [CrossRef] - Xia, M.; Chen, J.X. Data envelopment analysis based on choquet integral. Int. J. Intell. Syst.
**2017**, 32, 1312–1331. [Google Scholar] [CrossRef] - Castellet, L.; Molinos-Senante, M. Efficiency assessment of wastewater treatment plants: A data envelopment analysis approach integrating technical, economic, and environmental issues. J. Environ. Manag.
**2016**, 167, 160–166. [Google Scholar] [CrossRef] [PubMed] - Yang, W.-C.; Lee, Y.-M.; Hu, J.-L. Urban sustainability assessment of Taiwan based on data envelopment analysis. Renew. Sustain. Energy Rev.
**2016**, 61, 341–353. [Google Scholar] [CrossRef] - Sadi-Nezhad, S.; Sotoudeh-Anvari, A. A new Data Envelopment Analysis under uncertain environment with respect to fuzziness and an estimation of reliability. OPSEARCH
**2016**, 53, 103–115. [Google Scholar] [CrossRef] - Charles, V.; Aparicio, J.; Zhu, J. The curse of dimensionality of decision-making units: A simple approach to increase the discriminatory power of data envelopment analysis. Eur. J. Oper. Res.
**2019**, 279, 929–940. [Google Scholar] [CrossRef] - Moslemi, S.; Izadbakhsh, H.; Zarinbal, M. A new reliable performance evaluation model: IFB-IER–DEA. OPSEARCH
**2019**, 56, 14–31. [Google Scholar] [CrossRef] - Akbarian, D. Avoiding dissimilarity between the weights of the optimal DEA solutions. OPSEARCH
**2019**. [Google Scholar] [CrossRef] - Lashani, E.; Aryavash, K. The optimistic—Pessimistic revenue distribution in the presence of imprecise data. OPSEARCH
**2018**, 55, 288–301. [Google Scholar] [CrossRef] - Bian, Y. A Gram–Schmidt process based approach for improving DEA discrimination in the presence of large dimensionality of data set. Expert Syst. Appl.
**2012**, 39, 3793–3799. [Google Scholar] [CrossRef] - Kaleibari, S.S.; Beiragh, R.G.; Alizadeh, R.; Solimanpur, M. A framework for performance evaluation of energy supply chain by a compatible network data envelopment analysis model. Sci. Iran. Trans. E Ind. Eng.
**2016**, 23, 1904. [Google Scholar] [CrossRef] - Flegl, M.; Andrade, L.A. Measuring countries’ performance at the Summer Olympic Games in Rio 2016. OPSEARCH
**2018**, 55, 823–846. [Google Scholar] [CrossRef] - Jenkins, L.; Anderson, M. A multivariate statistical approach to reducing the number of variables in data envelopment analysis. Eur. J. Oper. Res.
**2003**, 147, 51–61. [Google Scholar] [CrossRef] - Adler, N.; Golany, B. Including principal component weights to improve discrimination in data envelopment analysis. J. Oper. Res. Soc.
**2002**, 53, 985–991. [Google Scholar] [CrossRef] - Adler, N.; Berechman, J. Measuring airport quality from the airlines’ viewpoint: An application of data envelopment analysis. Transp. Policy
**2001**, 8, 171–181. [Google Scholar] [CrossRef] - Karsak, E.E.; Karadayi, M.A. Imprecise DEA framework for evaluating health-care performance of districts. Kybernetes
**2017**, 46, 706–727. [Google Scholar] [CrossRef] - Zhu, J. Data envelopment analysis vs. principal component analysis: An illustrative study of economic performance of Chinese cities. Eur. J. Oper. Res.
**1998**, 111, 50–61. [Google Scholar] [CrossRef] - Premachandra, I. A note on DEA vs principal component analysis: An improvement to Joe Zhu’s approach. Eur. J. Oper. Res.
**2001**, 132, 553–560. [Google Scholar] [CrossRef] - Liang, L.; Li, Y.; Li, S. Increasing the discriminatory power of DEA in the presence of the undesirable outputs and large dimensionality of data sets with PCA. Expert Syst. Appl.
**2009**, 36, 5895–5899. [Google Scholar] [CrossRef] - Ueda, T.; Hoshiai, Y. Application of principal component analysis for parsimonious summarization of DEA inputs and/or outputs. J. Oper. Res. Soc. Jpn.
**1997**, 40, 466–478. [Google Scholar] [CrossRef] - Shujie, Y.; Zhongwei, H.; Genfu, F. On technical efficiency of China’s insurance industry after WTO accession. China Econ. Rev.
**2007**, 18, 66–86. [Google Scholar] - Huang, W.; Paradi, J.C. Risk-adjusted efficiency of the insurance industry: evidence from China. Serv. Ind. J.
**2011**, 31, 1871–1885. [Google Scholar] [CrossRef] - Barros, C.P.; Nektarios, M.; Assaf, A. Efficiency in the Greek insurance industry. Eur. J. Oper. Res.
**2010**, 205, 431–436. [Google Scholar] [CrossRef] - Simar, L.; Wilson, P.W. Estimation and inference in two-stage, semi-parametric models of production processes. J. Econom.
**2007**, 136, 31–64. [Google Scholar] [CrossRef] - Tone, K.; Sahoo, B.K. Evaluating cost efficiency and returns to scale in the Life Insurance Corporation of India using data envelopment analysis. Socio Econ. Plan. Sci.
**2005**, 39, 261–285. [Google Scholar] [CrossRef] - Kao, C.; Hwang, S.-N. Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan. Eur. J. Oper. Res.
**2008**, 185, 418–429. [Google Scholar] [CrossRef] - Grmanová, E.; Strunz, H. Efficiency of insurance companies: Application of DEA and Tobit analyses. J. Int. Stud.
**2017**, 10, 250–263. [Google Scholar] [CrossRef] - Malyovanyi, M.; Nepochatenko, O.; Nesterchuk, Y. Conceptual Approaches to Improving the Functioning of Non-state Social Insurance Institutions in Ukraine. Econ. Sociol.
**2018**, 11, 289–304. [Google Scholar] [CrossRef] - Horsch, A.; Sysoyeva, L.; Bogma, S. Deposit insurance systems of post-Soviet countries: A comparative analysis. J. Int. Stud.
**2018**, 11, 22–44. [Google Scholar] [CrossRef] - Simionescu, M. The evolution of the Romanian insurance market after 2000. Econ. Manag. Sustain.
**2019**, 4, 11. [Google Scholar] [CrossRef] - Jurickova, E.; Pilik, M.; Kwarteng, M.A. Efficiency measurement of National Innovation Systems of the European Union countries: DEA Model Application. J. Int. Stud.
**2019**, 12, 286–299. [Google Scholar] [CrossRef] - Trynchuk, V.; Khovrak, I.; Dankiewicz, R.; Ostrowska-Dankiewicz, A.; Chushak-Holoborodko, A. The role of universities in disseminating the social responsibility practices of insurance companies. Probl. Perspect. Manag.
**2019**, 17, 449–461. [Google Scholar] [CrossRef] - Nesterchuk, Y.; Prokopchuk, O.; Tsymbalyuk, Y.; Rolinskyi, O.; Bilan, Y. Current status and prospects of development of the system of agrarian insurance in Ukraine. Invest. Manag. Financ. Innov.
**2018**, 15, 56. [Google Scholar] [CrossRef] - Fukuyama, H. Investigating productive efficiency and productivity changes of Japanese life insurance companies. Pacif. Basin Finance J.
**1997**, 5, 481–509. [Google Scholar] [CrossRef] - Eling, M.; Luhnen, M. Efficiency in the international insurance industry: A cross-country comparison. J. Bank. Finance
**2010**, 34, 1497–1509. [Google Scholar] [CrossRef] - Hui, Z.; Honggeng, Y. Application of Weighted principal component analysis in comprehensive evaluation for power quality. In Proceedings of the 2011 IEEE Power Engineering and Automation Conference, Wuhan, China, 8–9 September 2011. [Google Scholar]
- Çalik, A.; Pehlivan, N.Y.; Kahraman, C. An integrated fuzzy AHP/DEA approach for performance evaluation of territorial units in Turkey. Technol. Econ. Dev. Econ.
**2018**, 24, 1280–1302. [Google Scholar] [CrossRef] - Wang, Y.-M.; Liu, J.; Elhag, T.M. An integrated AHP–DEA methodology for bridge risk assessment. Comput. Ind. Eng.
**2008**, 54, 513–525. [Google Scholar] [CrossRef] - Ho, W.; Ma, X. The state-of-the-art integrations and applications of the analytic hierarchy process. Eur. J. Oper. Res.
**2018**, 267, 399–414. [Google Scholar] [CrossRef] - Dos Santos, P.H.; Neves, S.M.; Sant’Anna, D.O.; de Oliveira, C.H.; Carvalho, H.D. The analytic hierarchy process supporting decision making for sustainable development: An overview of applications. J. Clean. Prod.
**2019**, 212, 119–138. [Google Scholar] [CrossRef] - Krejčí, J.; Stoklasa, J. Aggregation in the analytic hierarchy process: Why weighted geometric mean should be used instead of weighted arithmetic mean. Expert Syst. Appl.
**2018**, 114, 97–106. [Google Scholar] [CrossRef] - Omar, F.; Bushby, S.T.; Williams, R.D. Assessing the performance of residential energy management control Algorithms: Multi-criteria decision making using the analytical hierarchy process. Energy Build.
**2019**, 199, 537–546. [Google Scholar] [CrossRef] - Pearson, K. LIII. On lines and planes of closest fit to systems of points in space. Lond. Edinb. Dublin Philos. Mag. J. Sci.
**1901**, 2, 559–572. [Google Scholar] [CrossRef] - shen How, B.; Lam, H.L. Sustainability evaluation for biomass supply chain synthesis: Novel principal component analysis (PCA) aided optimisation approach. J. Clean. Prod.
**2018**, 189, 941–961. [Google Scholar] - Zhao, Z.; Shkolnisky, Y.; Singer, A. Fast Steerable Principal Component Analysis. IEEE Trans. Comput. Imaging
**2016**, 2, 1–12. [Google Scholar] [CrossRef] [PubMed] - Kamadi, V.V.; Allam, A.R.; Thummala, S.M. A computational intelligence technique for the effective diagnosis of diabetic patients using principal component analysis (PCA) and modified fuzzy SLIQ decision tree approach. Appl. Soft Comput.
**2016**, 49, 137–145. [Google Scholar] [CrossRef] - Jiang, Q.; Liu, Z.; Liu, W.; Li, T.; Cong, W.; Zhang, H.; Shi, J. A principal component analysis based three-dimensional sustainability assessment model to evaluate corporate sustainable performance. J. Clean. Prod.
**2018**, 187, 625–637. [Google Scholar] [CrossRef] - Gupta, P.; Mehlawat, M.K.; Aggarwal, U.; Charles, V. An integrated AHP-DEA multi-objective optimization model for sustainable transportation in mining industry. Resour. Policy
**2018**. [Google Scholar] [CrossRef] - Azadeh, A.; Ghaderi, S.; Izadbakhsh, H. Integration of DEA and AHP with computer simulation for railway system improvement and optimization. Appl. Math. Comput.
**2008**, 195, 775–785. [Google Scholar] [CrossRef] - Ho, W.; Xu, X.; Dey, P.K. Multi-criteria decision making approaches for supplier evaluation and selection: A literature review. Eur. J. Oper. Res.
**2010**, 202, 16–24. [Google Scholar] [CrossRef] - Emrouznejad, A.; Marra, M. The state of the art development of AHP (1979–2017): A literature review with a social network analysis. Int. J. Prod. Res.
**2017**, 55, 6653–6675. [Google Scholar] [CrossRef] - Shanmugam, R.; Johnson, C. At a crossroad of data envelopment and principal component analyses. Omega
**2007**, 35, 351–364. [Google Scholar] [CrossRef] - Scheel, H. Undesirable outputs in efficiency valuations. Eur. J. Oper. Res.
**2001**, 132, 400–410. [Google Scholar] [CrossRef] - Seiford, L.M.; Zhu, J. Modeling undesirable factors in efficiency evaluation. Eur. J. Oper. Res.
**2002**, 142, 16–20. [Google Scholar] [CrossRef] - Pastor, J.T. Translation invariance in data envelopment analysis: A generalization. Ann. Oper. Res.
**1996**, 66, 91–102. [Google Scholar] [CrossRef] - Lovell, C.K.; Pastor, J.T.; Turner, J.A. Measuring macroeconomic performance in the OECD: A comparison of European and non-European countries. Eur. J. Oper. Res.
**1995**, 87, 507–518. [Google Scholar] [CrossRef] - Hashimoto, A. A ranked voting system using a DEA/AR exclusion model: A note. Eur. J. Oper. Res.
**1997**, 97, 600–604. [Google Scholar] [CrossRef] - Foroughi, A.A.; Tamiz, M. An effective total ranking model for a ranked voting system. Omega
**2005**, 33, 491–496. [Google Scholar] [CrossRef] - Andersen, P.; Petersen, N.C. A Procedure for Ranking Efficient Units in Data Envelopment Analysis. Manag. Sci.
**1993**, 39, 1261–1264. [Google Scholar] [CrossRef] - Despotis, D.K.; Koronakos, G.; Sotiros, D. Composition versus decomposition in two-stage network DEA: A reverse approach. J. Prod. Anal.
**2016**, 45, 71–87. [Google Scholar] [CrossRef] - Chen, Y.-p.; Lin, Y.-y. Controlling the movement of crowds in computer graphics by using the mechanism of particle swarm optimization. Appl. Soft Comput.
**2009**, 9, 1170–1176. [Google Scholar] [CrossRef] - Ang, S.; Chen, C.-M. Pitfalls of decomposition weights in the additive multi-stage DEA model. Omega
**2016**, 58, 139–153. [Google Scholar] [CrossRef] - Guo, C.; Abbasi Shureshjani, R.; Foroughi, A.A.; Zhu, J. Decomposition weights and overall efficiency in two-stage additive network DEA. Eur. J. Oper. Res.
**2017**, 257, 896–906. [Google Scholar] [CrossRef] - Ishizaka, A.; Labib, A. Review of the main developments in the analytic hierarchy process. Expert Syst. Appl.
**2011**, 38, 14336–14345. [Google Scholar] [CrossRef] - Sitorus, F.; Cilliers, J.J.; Brito-Parada, P.R. Multi-criteria decision making for the choice problem in mining and mineral processing: Applications and trends. Expert Syst. Appl.
**2019**, 121, 393–417. [Google Scholar] [CrossRef]

Verbal Judgments of Preferences | Extremely Preferred | Very Strongly to Extremely | Very Strongly Preferred | Strongly to Very Strongly | Strongly Preferred | Moderately to Strongly | Moderately Preferred | Equally to Moderately | Equally Preferred |
---|---|---|---|---|---|---|---|---|---|

Numerical rating | 9 | 8 | 7 | 6 | 5 | 4 | 3 | 2 | 1 |

Variable | Sustainability Aspect | Description |
---|---|---|

I_{1}: Number of agents | Econ | Individuals or corporates that can provide insurance services |

I_{2}: Number of branches | Econ/soc | Providers of insurance services, inspection, control, support, and brokers |

I_{3}: Investment in green projects | Env | Sum of total investment in environmentally friendly projects. |

I_{4}: Operating costs | Econ | All official, personnel, public expenses, and commission fees. |

I_{5}: Investment costs | Econ/Env/Soc | Premiums received from insurers cannot be consumed for compensations immediately and remain in companies for a while. |

I_{6}: Total assets | Econ | These assets include tangible fixed assets and intangible assets. |

I_{7}: corporate social responsibility | Soc | Corporate social responsibility encompasses investments as the company’s charitable contributions and role in the community. |

O_{1}: Premium of Insurance issued | Econ/Env/Soc | Direct premiums are received by insurance companies or branches, while indirect ones are presented by brokers of insurance companies. |

O_{2}: Net profit | Econ | Subtractions of the number of incomes with operating costs and tax. |

O_{3}: Investment income | Econ | The total proceeds from short-term and long-term investments. |

O_{4}: Total debt | Econ | Total debt to representatives, brokers, and other companies |

O_{5}: Number of issued insurances | Econ | Penetration rate of insurance policies and all types of insurances. |

O_{6}: Total payable compensations | Econ | Compensations during the review period paid by insurance companies are called payable compensation. |

I_{1} | I_{2} | I_{3} | I_{4} | I_{5} | I_{6} | I_{7} | O_{1} | O_{2} | O_{3} | O_{4} | O_{5} | O_{6} | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|

Iran | 5538 | 4614 | 205 | 1,736,982 | 1,472,429 | 23,795,357 | 593,449 | 2,831,972 | 320,291 | 332432 | 3,258,517 | 1,562,637 | 2,198,107 |

Dana | 952 | 1581 | 54 | 3,191,983 | 763,081 | 37,756,571 | 157,684 | 5,152,121 | 69,739 | 30,922 | 5,025,753 | 1085032 | 1,703,533 |

Asia | 1924 | 2699 | 89 | 4,016,051 | 2,018,250 | 36,501,402 | 684,520 | 6,665,153 | 127,191 | 276,430 | 7,127,567 | 3,670,365 | 391,057 |

Alborz | 1501 | 1302 | 51 | 1,858,603 | 1,638,024 | 36,881,628 | 883,040 | 3,530,870 | 262,414 | 341,119 | 4,429,534 | 1,850,647 | 206,050 |

Moalem | 791 | 638 | 39 | 921,117 | 575,624 | 37,944,028 | 516,386 | 900,379 | 45,484 | 99,297 | 1,995,053 | 444,989 | 315,901 |

Parsian | 1497 | 659 | 39 | 224,8568 | 1,679,712 | 36,839,940 | 199,779 | 3,622,715 | 506,228 | 321,737 | 5,473,480 | 2,407,920 | 254,405 |

Toseie | 1607 | 440 | 32 | 920,430 | 1,476,394 | 37,043,258 | 554,768 | 2,126,782 | 199,932 | 147,008 | 2,249,591 | 1,585,899 | 111,615 |

Razi | 577 | 459 | 28 | 653,290 | 643,645 | 37,876,007 | 345,151 | 844,021 | 15,311 | 667,55 | 1,285,289 | 1,045,294 | 75,427 |

Karafarin | 1072 | 534 | 26 | 990,953 | 1,392,337 | 37,127,315 | 472,436 | 1,813,073 | 80,495 | 176,130 | 2,802,203 | 628,401 | 41,311 |

Sina | 142 | 350 | 80 | 894,794 | 324,842 | 38,194,810 | 494,335 | 1,671,061 | 44,782 | 38,640 | 2,278,848 | 170,750 | 354,580 |

Dey | 561 | 312 | 34 | 342,660 | 643,930 | 37,875,722 | 663,702 | 601,800 | 34,753 | 71,696 | 806,664 | 151,087 | 43,425 |

Saman | 671 | 378 | 16 | 205,445 | 516,421 | 38,003,231 | 408,974 | 809,493 | 96,110 | 53,548 | 982,730 | 418,116 | 28,637 |

Novin | 1216 | 338 | 31 | 601,038 | 769,237 | 37,750,415 | 290,191 | 1,160,416 | 95,833 | 125,504 | 1,280,979 | 1,109,931 | 133,316 |

Pasargard | 993 | 299 | 33 | 275,321 | 109,278 | 37,426,875 | 728,682 | 1,042,748 | 159,726 | 128,464 | 1,164,519 | 1,168,666 | 39,580 |

Indices | I_{1} | I_{2} | I_{3} | I_{4} | I_{5} | I_{6} | I_{7} | O_{1} | O_{2} | O_{3} | O_{4} | O_{5} | O_{6} |

Weights | 0.02608 | 0.0208 | 0.03304 | 0.08705 | 0.05368 | 0.06844 | 0.21478 | 0.11029 | 0.1738 | 0.13898 | 0.04208 | 0.01689 | 0.01409 |

Indices | d1 | d2 | d3 | d4 | d5 | d6 | d7 | d8 | d9 |
---|---|---|---|---|---|---|---|---|---|

Weights | 0.00960667 | 0.0075 | 0.01250 | 0.03647744 | 0.02241847 | 0.02749435 | 0.10391165 | 0.01622057 | 0.012664 |

Indices | 10 | d11 | d12 | d13 | d14 | d15 | d16 | d17 | d18 |

Weights | 0.02111 | 0.06159103 | 0.03785289 | 0.04642336 | 0.17545161 | 0.01249634 | 0.009757 | 0.016263 | 0.047449 |

Indices | d19 | d20 | d21 | d22 | d23 | d24 | d25 | d26 | d27 |

Weights | 0.02916191 | 0.03576460 | 0.13516809 | 0.00327214 | 0.002555 | 0.004258 | 0.012424617 | 0.007635978 | 0.0093649 |

Indices | d28 | d29 | d30 | d31 | d32 | d33 | d34 | d35 | d36 |

Weights | 0.03539345 | 0.0011668 | 0.000911 | 0.001518 | 0.00443043 | 0.00272288 | 0.003339 | 0.01262078 | 0.0009218 |

Indices | d37 | d38 | d39 | d40 | d41 | d42 | |||

Weights | 0.00072 | 0.0012 | 0.00350018 | 0.00215115 | 0.00263821 | 0.00997079 |

PC1 | PC2 | PC3 | PC4 | PC5 | PC1 | PC2 | PC3 | PC4 | PC5 | ||
---|---|---|---|---|---|---|---|---|---|---|---|

Iran | −0.2915 | 0.05842 | 0.07073 | 0.02801 | 0.10622 | Sina | 0.07800 | 0.02444 | −0.1039 | 0.01313 | −0.04603 |

Dana | 0.04090 | 0.20939 | 0.15311 | −0.0719 | 0.00982 | Dey | 0.08962 | −0.05341 | −0.0731 | 0.03889 | −0.01485 |

Asia | 0.01852 | 0.0453 | 0.04547 | 0.07397 | −0.0126 | Saman | 0.02535 | −0.0763 | −0.0317 | −0.1202 | 0.031898 |

Alborz | −0.0380 | −0.0043 | 0.02141 | 0.02407 | −0.0048 | Novin | −0.0278 | −0.00923 | 0.05027 | 0.01689 | 0.000952 |

Moalem | 0.06831 | −0.0163 | −0.0526 | 0.07582 | −0.0368 | Pasargard | 0.00546 | −0.11215 | −0.0248 | −0.1379 | 0.054345 |

Parsian | −0.0491 | −0.0207 | −0.0469 | −0.0414 | −0.0504 | Eigenvalue | 15.3556 | 11.93489 | 4.54013 | 3.90396 | 2.090281 |

Toseie | −0.0195 | −0.0094 | 0.02708 | −0.0392 | −0.0017 | VCR (%) | 36.56 | 28.42 | 10.81 | 9.3 | 4.98 |

Razi | 0.087259 | −0.01891 | −0.0399 | 0.08253 | −0.033 | CVCR (%) | 36.56 | 64.98 | 75.79 | 85.08 | 90.06 |

Karafarin | 0.01259 | −0.01689 | 0.00493 | 0.0575 | −0.0031 |

PC1 | PC2 | PC3 | PC4 | PC5 | PC1 | PC2 | PC3 | PC4 | PC5 | ||
---|---|---|---|---|---|---|---|---|---|---|---|

Iran | 1 | 1.349975 | 1.362292 | 1.319567 | 1.397775 | Razi | 1.378818 | 1.272651 | 1.25165 | 1.374092 | 1.25856 |

Dana | 1.332463 | 1.500943 | 1.444667 | 1.219567 | 1.301373 | Karafarin | 1.304147 | 1.274666 | 1.296486 | 1.349058 | 1.288459 |

Asia | 1.310081 | 1.336888 | 1.337025 | 1.365533 | 1.278937 | Sina | 1.36956 | 1.315998 | 1.187669 | 1.304684 | 1.245532 |

Alborz | 1.253526 | 1.287289 | 1.312964 | 1.315628 | 1.286779 | Dey | 1.38118 | 1.238148 | 1.21847 | 1.330447 | 1.276709 |

Moalem | 1.359872 | 1.27522 | 1.238946 | 1.367385 | 1.254797 | Saman | 1.316908 | 1.215256 | 1.259825 | 1.171331 | 1.323457 |

Parsian | 1.242379 | 1.270878 | 1.24459 | 1.25014 | 1.24113 | Novin | 1.263836 | 1.282326 | 1.341828 | 1.308451 | 1.29251 |

Toseie | 1.272032 | 1.282173 | 1.31864 | 1.252318 | 1.289895 | Pasargard | 1.297014 | 1.179404 | 1.266761 | 1.153614 | 1.345904 |

PCA–DEA | AHP–PCA–DEA | ||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

DMU | Insurance Company | W_{0} | Rank | DMU | Insurance Company | W_{0} | Rank | DMU | Insurance Company | W_{0} | Rank | DMU | Insurance Company | W_{0} | Rank |

1 | Iran | 0.7332 | 13 | 8 | Razi | 0.9477 | 5 | 1 | Iran | 0.9446 | 13 | 8 | Razi | 1.006 | 2 |

2 | Dana | 1.2905 | 1 | 9 | Karafarin | 0.8474 | 8 | 2 | Dana | 1.0732 | 1 | 9 | Karafarin | 0.969 | 7 |

3 | Asia | 0.8967 | 6 | 10 | Sina | 0.9582 | 4 | 3 | Asia | 0.9790 | 6 | 10 | Sina | 0.999 | 4 |

4 | Alborz | 0.8220 | 10 | 11 | Dey | 1.0189 | 2 | 4 | Alborz | 0.9492 | 11 | 11 | Dey | 1.001 | 3 |

5 | Moalem | 0.9826 | 3 | 12 | Saman | 0.8928 | 7 | 5 | Moalem | 0.9897 | 5 | 12 | Saman | 0.959 | 8 |

6 | Parsian | 0.6903 | 14 | 13 | Novin | 0.8314 | 9 | 6 | Parsian | 0.9259 | 14 | 13 | Novin | 0.954 | 9 |

7 | Toseie | 0.7824 | 12 | 14 | Pasargard | 0.7965 | 11 | 7 | Toseie | 0.9491 | 10 | 14 | Pasargard | 0.947 | 12 |

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Gharizadeh Beiragh, R.; Alizadeh, R.; Shafiei Kaleibari, S.; Cavallaro, F.; Zolfani, S.H.; Bausys, R.; Mardani, A.
An integrated Multi-Criteria Decision Making Model for Sustainability Performance Assessment for Insurance Companies. *Sustainability* **2020**, *12*, 789.
https://doi.org/10.3390/su12030789

**AMA Style**

Gharizadeh Beiragh R, Alizadeh R, Shafiei Kaleibari S, Cavallaro F, Zolfani SH, Bausys R, Mardani A.
An integrated Multi-Criteria Decision Making Model for Sustainability Performance Assessment for Insurance Companies. *Sustainability*. 2020; 12(3):789.
https://doi.org/10.3390/su12030789

**Chicago/Turabian Style**

Gharizadeh Beiragh, Ramin, Reza Alizadeh, Saeid Shafiei Kaleibari, Fausto Cavallaro, Sarfaraz Hashemkhani Zolfani, Romualdas Bausys, and Abbas Mardani.
2020. "An integrated Multi-Criteria Decision Making Model for Sustainability Performance Assessment for Insurance Companies" *Sustainability* 12, no. 3: 789.
https://doi.org/10.3390/su12030789