The Evaluation Method of Low-Carbon Scenic Spots by Combining IBWM with B-DST and VIKOR in Fuzzy Environment
Abstract
:1. Introduction
2. Literature Review
3. LSSs Evaluation Criteria System
3.1. Eco-Environment
3.2. Tourist Facilities
3.3. Management Level
3.4. Participant Attitudes
4. Methodology
4.1. Fuzzy Set Theory
- (1)
- (2)
- (3) Ifis any TIFN, then
4.2. The Proposed Framework
5. Case Analysis
5.1. Background Information
5.2. Evaluate process and Results
5.3. Discussion
5.3.1. Sensitive Analyses
5.3.2. Comparative Analysis.
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A
Appendix A.1. Proof Definition 3
Appendix A.2. Proof Property (3)
References
- Gössling, S.; Scott, D.; Hall, C.M. Inter-market variability in CO2 emission-intensities in tourism: Implications for destination marketing and carbon management. Tour. Manag. 2015, 46, 203–212. [Google Scholar] [CrossRef]
- Peeters, P.; Dubois, G. Tourism travel under climate change mitigation constraints. J. Transp. Geogr. 2010, 18, 447–457. [Google Scholar] [CrossRef]
- Jin, C.; Cheng, J.; Xu, J.; Huang, Z. Self-driving tourism induced carbon emission flows and its determinants in well-developed regions: A case study of Jiangsu Province, China. J. Clean. Prod. 2018, 186, 191–202. [Google Scholar] [CrossRef] [Green Version]
- Cheng, Q.; Su, B.; Tan, J. Developing an evaluation index system for low-carbon tourist attractions in China–A case study examining the Xixi wetland. Tour. Manag. 2013, 36, 314–320. [Google Scholar] [CrossRef]
- Prideaux, B.; Yin, P. The disruptive potential of autonomous vehicles (AVs) on future low-carbon tourism mobility. Asia Pac. J. Tour. Res. 2019, 24, 459–467. [Google Scholar] [CrossRef]
- Chen, L.F. Green certification, e-commerce, and low-carbon economy for international tourist hotels. Environ. Sci. Pollut. Res. 2019, 1–9. [Google Scholar] [CrossRef]
- Rezaei, J. Best-worst multi-criteria decision-making method. Omega 2015, 53, 49–57. [Google Scholar] [CrossRef]
- Liu, A.; Ji, X.; Lu, H.; Liu, H. The selection of 3PRLs on self-service mobile recycling machine: Interval-valued pythagorean hesitant fuzzy best-worst multi-criteria group decision-making. J. Clean. Prod. 2019, 230, 734–750. [Google Scholar] [CrossRef]
- Wang, J.; Qiao, K.; Zhang, Z. An improvement for combination rule in evidence theory. Future Gener. Comput. Syst. 2019, 91, 1–9. [Google Scholar] [CrossRef]
- Bappy, M.M.; Ali, S.M.; Kabir, G.; Paul, S.K. Supply chain sustainability assessment with Dempster-Shafer evidence theory: Implications in cleaner production. J. Clean. Prod. 2019, 237. [Google Scholar] [CrossRef]
- Liang, W.Z.; Zhao, G.Y.; Hong, C.S. Performance assessment of circular economy for phosphorus chemical firms based on VIKOR-QUALIFLEX method. J. Clean. Prod. 2018, 196, 1365–1378. [Google Scholar] [CrossRef] [Green Version]
- Awasthi, A.; Govindan, K.; Gold, S. Multi-tier sustainable global supplier selection using a fuzzy AHP-VIKOR based approach. Int. J. Prod. Econ. 2018, 195, 106–117. [Google Scholar] [CrossRef]
- Lee, J.W.; Brahmasrene, T. Investigating the influence of tourism on economic growth and carbon emissions: Evidence from panel analysis of the European Union. Tour. Manag. 2013, 38, 69–76. [Google Scholar] [CrossRef]
- García-Pozo, A.; Sánchez-Ollero, J.L.; Ons-CappaM, M. ECO-innovation and economic crisis: a comparative analysis of environmental good practices and labour productivity in the Spanish hotel industry. J. Clean. Prod. 2016, 196, 131–138. [Google Scholar] [CrossRef]
- Juvan, E.; Dolnicar, S. Can tourists easily choose a low carbon footprint vacation? J. Sustain. Tour. 2014, 22, 175–194. [Google Scholar] [CrossRef]
- Dillimono, H.D.; Dickinson, J.E. Travel, tourism, climate change, and behavioral change: travelers’ perspectives from a developing country, Nigeria. J. Sustain. Tour. 2015, 23, 437–454. [Google Scholar] [CrossRef]
- Mostafanezhad, M.; Norum, R. The anthropocenic imaginary: Political ecologies of tourism in a geological epoch. J. Sustain. Tour. 2019, 27, 421–435. [Google Scholar] [CrossRef] [Green Version]
- Kuo, N.W.; Chen, P.H. Quantifying energy use, carbon dioxide emission, and other environmental loads from island tourism based on a life cycle assessment approach. J. Clean. Prod. 2009, 17, 1324–1330. [Google Scholar] [CrossRef]
- Becken, S.; Simmons, D.G.; Frampton, C. Energy use associated with different travel choices. Tour. Manag. 2003, 24, 267–277. [Google Scholar] [CrossRef]
- Lin, T.P. Carbon dioxide emissions from transport in Taiwan’s national parks. Tour. Manag. 2010, 31, 285–290. [Google Scholar] [CrossRef]
- Tol, R.S.J. The impact of a carbon tax on international tourism. Transp. Res. Part D Transp. Environ. 2007, 12, 129–142. [Google Scholar] [CrossRef] [Green Version]
- Higham, J.; Cohen, S.A.; Cavaliere, C.T.; Reis, A.; Finkler, W. Climate change, tourist air travel and radical emissions reduction. J. Clean. Prod. 2016, 111, 336–347. [Google Scholar] [CrossRef] [Green Version]
- Blancas, F.J.; González, M.; Lozano-Oyola, M.; Pérez, F. The assessment of sustainable tourism: Application to spanish coastal destinations. Ecol. Indic. 2010, 10, 484–492. [Google Scholar] [CrossRef]
- Michalena, E.; Hills, J.; Amat, J.P. Developing sustainable tourism, using a multicriteria analysis on renewable energy in mediterranean islands. Energy Sustain. Dev. 2009, 13, 129–136. [Google Scholar] [CrossRef]
- Liu, B.; Li, T.; Tsai, S.B. Low carbon strategy analysis of competing supply chains with different power structures. Sustainability 2017, 9, 835. [Google Scholar]
- Liu, A.; Zhu, Q.; Ji, X.; Lu, H.; Tsai, S.B. Novel Method for Perceiving Key Requirements of Customer Collaboration Low-Carbon Product Design. Int. J. Environ. Res. Public Health 2018, 15, 1446. [Google Scholar] [CrossRef] [Green Version]
- Opricovic, S.; Tzeng, G.H. Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. Eur. J. Oper. Res. 2004, 156, 445–455. [Google Scholar] [CrossRef]
- Çelikbilek, Y.; Tüysüz, F. An integrated grey based multi-criteria decision making approach for the evaluation of renewable energy sources. Energy 2016, 115, 1246–1258. [Google Scholar] [CrossRef]
- Tian, Z.P.; Wang, J.Q.; Wang, J.; Zhang, H.Y. A multi-phase QFD-based hybrid fuzzy MCDM approach for performance evaluation: A case of smart bike-sharing programs in Changsha. J. Clean. Prod. 2018, 171, 1068–1083. [Google Scholar] [CrossRef]
- Li, N.; Zhao, H. Performance evaluation of eco-industrial thermal power plants by using fuzzy GRA-VIKOR and combination weighting techniques. J. Clean. Prod. 2016, 135, 169–183. [Google Scholar] [CrossRef]
- Han, Y.; Deng, Y.; Cao, Z.; Lin, C.T. An interval-valued Pythagorean prioritized operator-based game theoretical framework with its applications in multicriteria group decision making. Neural Comput. Appl. 2019, 1–19. [Google Scholar] [CrossRef]
- Wan, S.P.; Wang, Q.Y.; Dong, J.Y. The extended VIKOR method for multi-attribute group decision making with triangular intuitionistic fuzzy numbers. Knowl. Based Syst. 2013, 52, 65–77. [Google Scholar] [CrossRef]
- Xu, F.; Liu, J.; Lin, S.; Yuan, J. A VIKOR-based approach for assessing the service performance of electric vehicle sharing programs: A case study in Beijing. J. Clean. Prod. 2017, 148, 254–267. [Google Scholar] [CrossRef]
- Liu, A.; Ji, X.; Xu, L.; Lu, H. Research on the recycling of sharing bikes based on time dynamics series, individual regrets and group efficiency. J. Clean. Prod. 2019, 208, 666–687. [Google Scholar] [CrossRef]
- Denoeux, T. A k-nearest neighbor classification rule based on Dempster-Shafer theory. IEEE Trans. Syst. Mancybern. 1995, 25, 804–813. [Google Scholar] [CrossRef] [Green Version]
- Certa, A.; Hopps, F.; Inghilleri, R.; La Fata, C.M. A Dempster-Shafer Theory-based approach to the Failure Mode, Effects and Criticality Analysis (FMECA) under epistemic uncertainty: Application to the propulsion system of a fishing vessel. Reliab. Eng. Syst. Saf. 2017, 159, 69–79. [Google Scholar] [CrossRef]
- Tang, H. A novel fuzzy soft set approach in decision making based on grey relational analysis and Dempster–Shafer theory of evidence. Appl. Soft Comput. 2015, 31, 317–325. [Google Scholar] [CrossRef]
- Liu, P.; Gao, H. Some intuitionistic fuzzy power Bonferroni mean operators in the framework of Dempster–Shafer theory and their application to multicriteria decision making. Appl. Soft Comput. 2019. [Google Scholar] [CrossRef]
- Su, J.; Ji, D.; Lin, M.; Chen, Y.; Sun, Y.; Huo, S.; Xi, B. Developing surface water quality standards in China. Resour. Conserv. Recycl. 2017, 117, 294–303. [Google Scholar] [CrossRef]
- Rubino, L.; Capasso, C.; Veneri, O. Review on plug-in electric vehicle charging architectures integrated with distributed energy sources for sustainable mobility. Appl. Energy 2017, 207, 438–464. [Google Scholar] [CrossRef]
- Hsiao, T.Y. A study of the effects of co-branding between low-carbon islands and recreational activities. Curr. Issues Tour. 2018, 21, 529–546. [Google Scholar] [CrossRef]
- Luo, F.; Becken, S.; Zhong, Y. Changing travel patterns in China and ‘carbon footprint’ implications for a domestic tourist destination. Tour. Manag. 2018, 65, 1–13. [Google Scholar] [CrossRef]
- Wu, Y.; Wu, X.; Niu, B.; Zeng, Y.; Zhu, M.; Guo, H. Facile fabrication of Ag2 (bdc)@ Ag nano-composites with strong green emission and their response to sulfide anion in aqueous medium. Sens. Actuators B: Chem. 2018, 255, 3163–3169. [Google Scholar] [CrossRef]
- Geels, F.W. Disruption and low-carbon system transformation: Progress and new challenges in socio-technical transitions research and the Multi-Level Perspective. Energy Res. Soc. Sci. 2018, 37, 224–231. [Google Scholar] [CrossRef]
- Seles, B.M.R.P.; de Sousa Jabbour, A.B.L.; Jabbour, C.J.C.; de Camargo Fiorini, P.; Mohd-Yusoff, Y.; Thomé, A.M.T. Business opportunities and challenges as the two sides of the climate change: Corporate responses and potential implications for big data management towards a low carbon society. J. Clean. Prod. 2018, 189, 763–774. [Google Scholar] [CrossRef] [Green Version]
- Büchs, M.; Bahaj, A.S.; Blunden, L.; Bourikas, L.; Falkingham, J.; James, P.; Wu, Y. Promoting low carbon behaviours through personalised information? Long-term evaluation of a carbon calculator interview. Energy Policy. 2018, 120, 284–293. [Google Scholar] [CrossRef]
- Khan, M.I. Evaluating the strategies of compressed natural gas industry using an integrated SWOT and MCDM approach. J. Clean. Prod. 2018, 172, 1035–1052. [Google Scholar] [CrossRef]
- Luo, Z.; Deng, Y. A matrix method of basic belief assignment’s negation in Dempster-Shafer theory. IEEE Trans. Fuzzy Syst. 2019. [Google Scholar] [CrossRef]
- Gao, X.; Liu, F.; Pan, L.; Deng, Y.; Tsai, S.B. Uncertainty measure based on Tsallis entropy in evidence theory. Int. J. Intell. Syst. 2019, 34, 3105–3120. [Google Scholar] [CrossRef]
Linguistic Term | TIFNs | Consistency Indices (CIs) |
---|---|---|
Equally Important(EI) | [(1,1,1;0.6), (1,1,1;0.3)] | 2.395 |
Weakly Important(WI) | [(2/3,1,3/2;0.6), (2/3,2,3/2;0.3)] | 2.427 |
Fairly Important(FI) | [(3/2,2,2/5;0.6), (3/2,2,2/5;0.3)] | 3.120 |
Important(I) | [(5/2,3,7/2;0.6), (5/2,3,7/2;0.3)] | 4.487 |
Very Important(VI) | [(7/2,4,9/2;0.6), [(7/2,4,9/2;0.3)] | 5.435 |
Absolutely Important(AI) | [(9/2,5,11/2;0.6), (9/2,5,11/2;0.3)] | 6.348 |
Linguistic Term | TIFNs |
---|---|
Absolutely Low (AL) | [(0,0,1;0.6), (0,0,1;0.3)] |
Low (L) | [(0,1,3;0.6), (0,1,3;0.3)] |
Fairly Low (FL) | [(1,3,5;0.6), (1,3,5;0.3)] |
Medium (M) | [(3,5,7;0.6), (3,5,7;0.3)] |
Fairly High (FH) | [(5,7,9;0.6), [(5,7,9;0.3)] |
High (H) | [(7,9,10;0.6), (7,9,10;0.3)] |
Absolutely High (AH) | [(9,10,10;0.6), (9,10,10;0.3)] |
0.1180 | 0.2449 | 0.2085 | 0.1966 | 0.2321 | |
0.2345 | 0.1427 | 0.2377 | 0.1998 | 0.1852 | |
0.2199 | 0.3089 | 0.1571 | 0.0366 | 0.2775 | |
0.2817 | 0.2988 | 0.0395 | 0.0395 | 0.3406 | |
0.0379 | 0.3781 | 0.2108 | 0.2108 | 0.1624 | |
0.2055 | 0.1468 | 0.2055 | 0.1468 | 0.2956 | |
0.1430 | 0.2287 | 0.2168 | 0.2168 | 0.1947 | |
0.1956 | 0.2177 | 0.1956 | 0.1956 | 0.1956 | |
0.1843 | 0.1981 | 0.1868 | 0.2327 | 0.1981 | |
0.2320 | 0.2576 | 0.0306 | 0.3057 | 0.1741 | |
0.2961 | 0.1676 | 0.0391 | 0.1676 | 0.3296 | |
0.2595 | 0.1749 | 0.1319 | 0.1749 | 0.2588 | |
0.2041 | 0.1832 | 0.2187 | 0.1970 | 0.1970 | |
0.0433 | 0.3397 | 0.0433 | 0.2460 | 0.3278 | |
0.1508 | 0.2111 | 0.2761 | 0.2111 | 0.1508 | |
0.0959 | 0.0959 | 0.2825 | 0.2120 | 0.3137 | |
0.2056 | 0.2056 | 0.1543 | 0.2289 | 0.2056 | |
0.2632 | 0.1778 | 0.1342 | 0.2370 | 0.1878 | |
0.1270 | 0.2244 | 0.2558 | 0.1684 | 0.2244 | |
0.3553 | 0.1087 | 0.2401 | 0.2536 | 0.0423 | |
0.2379 | 0.1210 | 0.2137 | 0.2137 | 0.2137 | |
0.1864 | 0.2125 | 0.2076 | 0.2070 | 0.1864 |
0.1369 | 0.2839 | 0.0821 | 0.2279 | 0.2691 | |
0.0892 | 0.2625 | 0.2475 | 0.2080 | 0.1928 | |
0.1818 | 0.2554 | 0.0779 | 0.2554 | 0.2294 | |
0.2677 | 0.3335 | 0.0375 | 0.0375 | 0.3237 | |
0.0405 | 0.4044 | 0.2254 | 0.2254 | 0.1042 | |
0.2055 | 0.1468 | 0.2055 | 0.1468 | 0.2956 | |
0.0716 | 0.2477 | 0.2348 | 0.2348 | 0.2109 | |
0.2718 | 0.0923 | 0.2718 | 0.2718 | 0.0923 | |
0.2024 | 0.2176 | 0.2052 | 0.1724 | 0.2024 | |
0.2421 | 0.2252 | 0.0320 | 0.3190 | 0.1817 | |
0.0978 | 0.1630 | 0.3207 | 0.0978 | 0.3207 | |
0.1804 | 0.1708 | 0.1288 | 0.2673 | 0.2527 | |
0.2041 | 0.1832 | 0.2187 | 0.1970 | 0.1970 | |
0.0380 | 0.2982 | 0.0380 | 0.3380 | 0.2878 | |
0.1258 | 0.1761 | 0.2303 | 0.2610 | 0.2067 | |
0.1064 | 0.1064 | 0.3678 | 0.1064 | 0.3132 | |
0.2013 | 0.2013 | 0.2365 | 0.1595 | 0.2013 | |
0.2329 | 0.1574 | 0.2336 | 0.2098 | 0.1663 | |
0.1401 | 0.1961 | 0.2821 | 0.1857 | 0.1961 | |
0.2704 | 0.0827 | 0.1828 | 0.1930 | 0.2711 | |
0.2323 | 0.1181 | 0.2087 | 0.2087 | 0.2323 | |
0.1864 | 0.2125 | 0.2076 | 0.2070 | 0.1864 |
0.1794 | 0.3723 | 0.1076 | 0.2988 | 0.0419 | |
0.0892 | 0.2625 | 0.2475 | 0.2080 | 0.1928 | |
0.1708 | 0.2589 | 0.0790 | 0.2589 | 0.2325 | |
0.2082 | 0.2593 | 0.2517 | 0.0292 | 0.2517 | |
0.0391 | 0.3905 | 0.2349 | 0.2349 | 0.1007 | |
0.2523 | 0.1801 | 0.1801 | 0.1286 | 0.2590 | |
0.0743 | 0.2570 | 0.2436 | 0.2063 | 0.2188 | |
0.2214 | 0.1024 | 0.2389 | 0.3348 | 0.1024 | |
0.0775 | 0.2736 | 0.2150 | 0.1807 | 0.2532 | |
0.2320 | 0.2576 | 0.0306 | 0.3057 | 0.1741 | |
0.2251 | 0.1487 | 0.2089 | 0.2084 | 0.2089 | |
0.1478 | 0.1510 | 0.2414 | 0.2364 | 0.2234 | |
0.2128 | 0.1910 | 0.2280 | 0.1628 | 0.2054 | |
0.0308 | 0.2418 | 0.2200 | 0.2740 | 0.2333 | |
0.1317 | 0.1844 | 0.2411 | 0.1844 | 0.2584 | |
0.2353 | 0.1865 | 0.2764 | 0.0799 | 0.2219 | |
0.1942 | 0.2470 | 0.2419 | 0.0699 | 0.2470 | |
0.2214 | 0.1994 | 0.2219 | 0.1994 | 0.1580 | |
0.1286 | 0.1801 | 0.2590 | 0.1801 | 0.2523 | |
0.2129 | 0.2258 | 0.1694 | 0.1789 | 0.2129 | |
0.2323 | 0.1181 | 0.2087 | 0.2087 | 0.2323 | |
0.1864 | 0.2125 | 0.2076 | 0.2070 | 0.1864 |
0.0831 | 0.0822 | 0.0906 | |
0.0382 | 0.0382 | 0.0384 | |
0.0845 | 0.0823 | 0.0826 | |
0.0632 | 0.0626 | 0.0476 | |
0.0858 | 0.0879 | 0.0911 | |
0.0381 | 0.0381 | 0.0382 | |
0.0512 | 0.0509 | 0.0513 | |
0.0429 | 0.0440 | 0.0445 | |
0.0374 | 0.0374 | 0.0379 | |
0.0384 | 0.0377 | 0.0395 | |
0.0394 | 0.0365 | 0.0333 | |
0.0396 | 0.0395 | 0.0396 | |
0.0467 | 0.0467 | 0.0466 | |
0.0611 | 0.0615 | 0.0467 | |
0.0425 | 0.0425 | 0.0426 | |
0.0356 | 0.0391 | 0.0338 | |
0.0373 | 0.0373 | 0.0380 | |
0.0331 | 0.0332 | 0.0333 | |
0.0331 | 0.0331 | 0.0332 | |
0.0471 | 0.0399 | 0.0398 | |
0.0396 | 0.0396 | 0.0396 | |
0.0375 | 0.0375 | 0.0375 |
0.1985 | 0.2008 | 0.2002 | 0.1999 | 0.2006 | |
0.2003 | 0.1995 | 0.2003 | 0.2000 | 0.1999 | |
0.2004 | 0.2020 | 0.1992 | 0.1970 | 0.2014 | |
0.2011 | 0.2013 | 0.1979 | 0.1979 | 0.2019 | |
0.1970 | 0.2033 | 0.2002 | 0.2002 | 0.1993 | |
0.2000 | 0.1996 | 0.2000 | 0.1996 | 0.2008 | |
0.1994 | 0.2003 | 0.2002 | 0.2002 | 0.1999 | |
0.2000 | 0.2002 | 0.2000 | 0.2000 | 0.2000 | |
0.1999 | 0.2000 | 0.1999 | 0.2003 | 0.2000 | |
0.2003 | 0.2005 | 0.1987 | 0.2008 | 0.1998 | |
0.2008 | 0.1997 | 0.1987 | 0.1997 | 0.2011 | |
0.2005 | 0.1998 | 0.1994 | 0.1998 | 0.2005 | |
0.2000 | 0.1998 | 0.2002 | 0.2000 | 0.2000 | |
0.1980 | 0.2018 | 0.1980 | 0.2006 | 0.2016 | |
0.1996 | 0.2001 | 0.2007 | 0.2001 | 0.1996 | |
0.1992 | 0.1992 | 0.2006 | 0.2001 | 0.2008 | |
0.2000 | 0.2000 | 0.1996 | 0.2002 | 0.2000 | |
0.2004 | 0.1998 | 0.1996 | 0.2003 | 0.1999 | |
0.1995 | 0.2002 | 0.2004 | 0.1998 | 0.2002 | |
0.2015 | 0.1991 | 0.2004 | 0.2005 | 0.1985 | |
0.2003 | 0.1994 | 0.2001 | 0.2001 | 0.2001 | |
0.1999 | 0.2001 | 0.2001 | 0.2001 | 0.1999 |
0.1989 | 0.2015 | 0.1979 | 0.2005 | 0.2012 | |
0.1991 | 0.2005 | 0.2004 | 0.2001 | 0.1999 | |
0.1997 | 0.2010 | 0.1978 | 0.2010 | 0.2005 | |
0.2009 | 0.2018 | 0.1979 | 0.1979 | 0.2016 | |
0.1970 | 0.2039 | 0.2005 | 0.2005 | 0.1982 | |
0.2000 | 0.1996 | 0.2000 | 0.1996 | 0.2008 | |
0.1986 | 0.2005 | 0.2004 | 0.2004 | 0.2001 | |
0.2007 | 0.1990 | 0.2007 | 0.2007 | 0.1990 | |
0.2000 | 0.2001 | 0.2000 | 0.1998 | 0.2000 | |
0.2003 | 0.2002 | 0.1987 | 0.2009 | 0.1999 | |
0.1992 | 0.1997 | 0.2009 | 0.1992 | 0.2009 | |
0.1998 | 0.1998 | 0.1994 | 0.2005 | 0.2004 | |
0.2000 | 0.1998 | 0.2002 | 0.2000 | 0.2000 | |
0.1979 | 0.2013 | 0.1979 | 0.2018 | 0.2011 | |
0.1993 | 0.1998 | 0.2003 | 0.2005 | 0.2001 | |
0.1992 | 0.1992 | 0.2014 | 0.1992 | 0.2009 | |
0.2000 | 0.2000 | 0.2003 | 0.1997 | 0.2000 | |
0.2002 | 0.1997 | 0.2002 | 0.2001 | 0.1998 | |
0.1996 | 0.2000 | 0.2006 | 0.1999 | 0.2000 | |
0.2006 | 0.1990 | 0.1999 | 0.1999 | 0.2006 | |
0.2003 | 0.1993 | 0.2001 | 0.2001 | 0.2003 | |
0.1999 | 0.2001 | 0.2001 | 0.2001 | 0.1999 |
0.1996 | 0.2034 | 0.1982 | 0.2019 | 0.1969 | |
0.1991 | 0.2005 | 0.2004 | 0.2001 | 0.1999 | |
0.1995 | 0.2010 | 0.1979 | 0.2010 | 0.2006 | |
0.2001 | 0.2006 | 0.2005 | 0.1983 | 0.2005 | |
0.1968 | 0.2037 | 0.2007 | 0.2007 | 0.1980 | |
0.2004 | 0.1998 | 0.1998 | 0.1994 | 0.2005 | |
0.1987 | 0.2006 | 0.2005 | 0.2001 | 0.2002 | |
0.2002 | 0.1991 | 0.2004 | 0.2012 | 0.1991 | |
0.1990 | 0.2006 | 0.2001 | 0.1998 | 0.2004 | |
0.2003 | 0.2005 | 0.1986 | 0.2009 | 0.1998 | |
0.2002 | 0.1996 | 0.2001 | 0.2001 | 0.2001 | |
0.1996 | 0.1996 | 0.2003 | 0.2003 | 0.2002 | |
0.2001 | 0.1999 | 0.2003 | 0.1996 | 0.2001 | |
0.1984 | 0.2004 | 0.2002 | 0.2007 | 0.2003 | |
0.1994 | 0.1999 | 0.2004 | 0.1999 | 0.2005 | |
0.2002 | 0.1999 | 0.2005 | 0.1992 | 0.2002 | |
0.2000 | 0.2004 | 0.2003 | 0.1990 | 0.2004 | |
0.2001 | 0.2000 | 0.2002 | 0.2000 | 0.1997 | |
0.1995 | 0.1999 | 0.2004 | 0.1999 | 0.2004 | |
0.2001 | 0.2002 | 0.1997 | 0.1998 | 0.2001 | |
0.2003 | 0.1993 | 0.2001 | 0.2001 | 0.2003 | |
0.1999 | 0.2001 | 0.2001 | 0.2001 | 0.1999 |
0.1966 | 0.2065 | 0.1942 | 0.1970 | 0.2056 | |
0.1915 | 0.2058 | 0.1953 | 0.2022 | 0.2052 | |
0.1915 | 0.2091 | 0.1995 | 0.2020 | 0.1979 |
Criteria | Weight | Subcriteria | Subcriteria Weight |
---|---|---|---|
Eco-environment | 0.3909 | 0.0853 | |
0.0383 | |||
0.0831 | |||
0.0578 | |||
0.0883 | |||
0.0381 | |||
Tourist facilities | 0.2937 | 0.0511 | |
0.0438 | |||
0.0376 | |||
0.0385 | |||
0.0364 | |||
0.0396 | |||
0.0466 | |||
Management level | 0.2391 | 0.0564 | |
0.0425 | |||
0.0362 | |||
0.0375 | |||
0.0332 | |||
0.0331 | |||
Participant attitudes | 0.1193 | 0.0423 | |
0.0396 | |||
0.0375 |
((3.0, 5.0, 7.0); 0.6, 0.3) | ((6.3, 8.1, 8.9); 0.8, 0.1) | ((3.0001, 5.0001, 6.6667); 0.6, 0.3) | ((6.7, 7.6, 9.3); 0.6, 0.2) | ((6.0, 7.0, 7.6667); 0.6, 0.3) | |
((3.4334, 4.9667, 6.5); 0.6, 0.3) | ((5.6666, 7.6666, 9.0); 0.6, 0.3) | ((6.7, 7.6, 9.3); 0.6, 0.2) | ((5.0, 7.0, 9.0); 0.6, 0.3) | ((7.4, 8.5, 9.2); 0.4, 0.4) | |
((5.8, 7.5, 9.0667); 0.4, 0.4) | ((9.0, 10.0, 10.0); 0.6, 0.3) | ((1.6667, 3.6667, 5.6667); 0.6, 0.3) | ((5.9999, 6.9999, 7.6666); 0.6, 0.3) | ((7.0, 9.0, 10.0); 0.6, 0.3) | |
((6.7, 7.6, 9.3); 0.6, 0.2) | ((6.5333, 8.4, 9.2667); 0.8, 0.1) | ((1.9333, 3.2667, 5.0667); 0.8, 0.1) | ((0.0, 1.0, 3.0); 0.6, 0.3) | ((5.8, 7.8, 9.2); 0.8, 0.1) | |
((0.0, 1.0, 3.0); 0.6, 0.3) | ((8.3, 8.9, 9.5); 0.8, 0.1) | ((6.6, 8.0, 9.1333); 0.6, 0.3) | ((6.6, 8.0, 9.1333); 0.6, 0.3) | ((1.6667, 3.6667, 5.6667); 0.6, 0.3) | |
((6.0, 7.5, 9.0); 0.7, 0.2) | ((3.6667, 5.6667, 7.6667); 0.6, 0.3) | ((5.0, 7.0, 9.0); 0.6, 0.3) | ((3.0, 5.0, 7.0); 0.6, 0.3) | ((5.8, 7.8, 9.2); 0.8, 0.1) | |
((3.1334, 4.8334, 6.4); 0.6, 0.3) | ((6.3, 8.1, 8.9); 0.8, 0.1) | ((9.0, 10.0, 10.0); 0.6, 0.3) | ((8.2333, 9.2, 9.7667); 0.6, 0.2) | ((7.0, 9.0, 10.0); 0.6, 0.3) | |
((7.1333, 8.8333, 9.7333); 0.4, 0.4) | ((3.6667, 5.3334, 6.6667); 0.6, 0.3) | ((6.3333, 8.3333, 9.6667); 0.6, 0.3) | ((7.3333, 8.8333, 9.6667); 0.7, 0.2) | ((3.0001, 5.0001, 6.6667); 0.6, 0.3) | |
((5.8666, 6.9333, 8.0); 0.6, 0.3) | ((7.5, 9.0667, 9.9333); 0.7, 0.2) | ((6.7, 7.6, 9.3); 0.6, 0.2) | ((5.4333, 7.3667, 8.9667); 0.6, 0.3) | ((7.7667, 8.8, 9.5); 0.7, 0.2) | |
((7.0, 9.0, 10.0); 0.6, 0.3) | ((8.1, 8.6333, 9.1667); 0.7, 0.2) | ((0.0, 1.0, 3.0); 0.6, 0.3) | ((8.3, 8.9, 9.5); 0.8, 0.1) | ((6.0, 8.0, 9.0); 0.5, 0.4) | |
((5.5001, 7.0667, 8.2667); 0.7, 0.2) | ((3.6667, 5.6667, 7.6667); 0.6, 0.3) | ((5.9999, 6.9999, 7.6666); 0.6, 0.3) | ((4.0, 5.5, 7.0); 0.7, 0.2) | ((9.0, 10.0, 10.0); 0.6, 0.3) | |
((7.1334, 8.5, 9.4); 0.4, 0.4) | ((6.0, 8.0, 9.0); 0.5, 0.4) | ((4.8333, 6.4, 7.9333); 0.7, 0.2) | ((6.2, 8.0667, 8.9333); 0.8, 0.1) | ((8.0, 8.5, 9.0); 0.7, 0.2) | |
((7.5, 8.5, 9.5); 0.6, 0.2) | ((8.3, 8.9, 9.5); 0.8, 0.6) | ((8.0, 8.5, 9.0); 0.7, 0.2) | ((6.3333, 8.3333, 9.6667); 0.6, 0.3) | ((7.0, 9.0, 10.0); 0.6, 0.3) | |
((0.0, 1.0, 3.0); 0.6, 0.3) | ((7.5, 8.5, 9.5); 0.6, 0.2) | ((2.2333, 3.2, 5.1); 0.6, 0.2) | ((6.2, 8.0667, 8.9333); 0.8, 0.1) | ((7.0, 9.0, 10.0); 0.6, 0.3) | |
((3.0, 5.0, 7.0); 0.6, 0.3) | ((5.0, 7.0, 9.0); 0.6, 0.3) | ((7.5, 8.5, 9.5); 0.6, 0.2) | ((5.4333, 7.3667, 8.9667); 0.6, 0.3) | ((6.4333, 7.4666, 8.5); 0.7, 0.2) | |
((3.0, 5.0, 6.6667); 0.6, 0.3) | ((2.3333, 4.3333, 6.3333); 0.6, 0.3) | ((6.5333, 8.4, 9.2667); 0.8, 0.1) | ((2.6667, 4.6667, 6.3334); 0.6, 0.3) | ((7.2333, 8.3667, 9.4333); 0.6, 0.2) | |
((6.9, 8.5333, 9.7667); 0.6, 0.2) | ((7.5, 9.0667, 9.9333); 0.7, 0.2) | ((6.2, 8.0667, 8.9333); 0.8, 0.1) | ((5.0, 6.6667, 8.0); 0.6, 0.3) | ((7.5, 9.0667, 9.9333); 0.7, 0.2) | |
((8.0, 8.5, 9.0); 0.7, 0.2) | ((6.3333, 8.3333, 9.3333); 0.6, 0.3) | ((6.9999, 8.3333, 9.0); 0.6, 0.3) | ((7.0, 9.0, 10.0); 0.6, 0.3) | ((5.0, 7.0, 9.0); 0.6, 0.3) | |
((3.0, 5.0, 7.0); 0.6, 0.3) | ((5.6667, 7.6667, 9.3333); 0.6, 0.3) | ((5.8, 7.8, 9.2); 0.8, 0.1) | ((5.6667, 7.6667, 9.0); 0.6, 0.3) | ((6.6667, 8.1667, 9.3333); 0.7, 0.2) | |
((7.5667, 8.2, 9.1); 0.6, 0.2) | ((3.0, 5.0, 6.6667); 0.6, 0.3) | ((6.0, 8.0, 9.0); 0.5, 0.4) | ((5.0, 7.0, 9.0); 0.6, 0.3) | ((5.2332, 6.1999, 7.4333); 0.6, 0.2) | |
((9.0, 10.0, 10.0); 0.6, 0.3) | ((3.0, 5.0, 7.0); 0.6, 0.3) | ((7.0, 9.0, 10.0); 0.6, 0.3) | ((7.0, 9.0, 10.0); 0.6, 0.3) | ((8.3333, 9.6667, 10.0); 0.6, 0.3) | |
((7.0, 9.0, 10.0); 0.6, 0.3) | ((5.8, 7.8, 9.2); 0.8, 0.1) | ((9.0, 10.0, 10.0); 0.6, 0.3) | ((8.0, 8.5, 9.0); 0.7, 0.2) | ((7.0, 9.0, 10.0); 0.6, 0.3) |
Final Ranking | ||||
---|---|---|---|---|
0.5701 | 0.9747 | 0.0128 | 1 | |
0.3652 | 0.9600 | 0.6883 | 4 | |
0.4734 | 0.9753 | 0.1765 | 2 | |
0.4343 | 0.9720 | 0.3164 | 3 | |
0.2961 | 0.9510 | 1.0000 | 5 |
Ranking Orders | Best Candidates | ||||||
---|---|---|---|---|---|---|---|
0.1 | 0.0231 | 0.6405 | 0.0353 | 0.1732 | 1.0000 | ||
0.2 | 0.0205 | 0.6524 | 0.0706 | 0.2090 | 1.0000 | ||
0.3 | 0.0180 | 0.6644 | 0.1059 | 0.2448 | 1.0000 | ||
0.4 | 0.0154 | 0.6763 | 0.1412 | 0.2806 | 1.0000 | ||
0.5 | 0.0128 | 0.6883 | 0.1765 | 0.3164 | 1.0000 | ||
0.6 | 0.0103 | 0.7002 | 0.2118 | 0.3522 | 1.0000 | ||
0.7 | 0.0077 | 0.7122 | 0.2471 | 0.3881 | 1.0000 | ||
0.8 | 0.0051 | 0.7241 | 0.2824 | 0.4239 | 1.0000 | ||
0.9 | 0.0026 | 0.7361 | 0.3177 | 0.4597 | 1.0000 |
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Share and Cite
Liu, A.; Liu, T.; Ji, X.; Lu, H.; Li, F. The Evaluation Method of Low-Carbon Scenic Spots by Combining IBWM with B-DST and VIKOR in Fuzzy Environment. Int. J. Environ. Res. Public Health 2020, 17, 89. https://doi.org/10.3390/ijerph17010089
Liu A, Liu T, Ji X, Lu H, Li F. The Evaluation Method of Low-Carbon Scenic Spots by Combining IBWM with B-DST and VIKOR in Fuzzy Environment. International Journal of Environmental Research and Public Health. 2020; 17(1):89. https://doi.org/10.3390/ijerph17010089
Chicago/Turabian StyleLiu, Aijun, Taoning Liu, Xiaohui Ji, Hui Lu, and Feng Li. 2020. "The Evaluation Method of Low-Carbon Scenic Spots by Combining IBWM with B-DST and VIKOR in Fuzzy Environment" International Journal of Environmental Research and Public Health 17, no. 1: 89. https://doi.org/10.3390/ijerph17010089