Details

Category:

Magazine publication

Type:

Magazine publication

Published in:

Technological and Economic Development of Economy no. 28, edition 6, pages 1739 - 1767,
ISSN: 2029-4913

Publication year:

2022

DOI:

Digital Object Identifier (open in new tab) https://doi.org/10.3846/tede.2022.17702

Bibliography: test

1. Allen, C., Metternicht, G., & Wiedmann, T. (2018). Initial progress in implementing the Sustainable Development Goals (SDGs): A review of evidence from countries. Sustainability Science, 13, 1453- 1467. https://doi.org/10.1007/s11625-018-0572-3 open in new tab
2. Allen, C., Metternicht, G., & Wiedmann, T. (2019). Prioritising SDG targets: Assessing baselines, gaps and interlinkages. Sustainability Science, 14(2), 421-438. https://doi.org/10.1007/s11625-018-0596-8 open in new tab
3. ASEM SMEs Eco-innovation Center. (2018). 2018 ASEM Eco-innovation Index. Annual report. ASEIC. Retrieved September 12, 2021, from http://aseic.org/reference/publishing.php open in new tab
4. Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30(9), 1078-1092. https://doi.org/10.1287/mnsc.30.9.1078 open in new tab
5. Carayannis, E. G., Barth, T. D., & Campbell, D. F. (2012). The Quintuple Helix innovation model: Global warming as a challenge and driver for innovation. Journal of Innovation and Entrepreneur- ship, 1, 1-12. https://doi.org/10.1186/2192-5372-1-2 open in new tab
6. Carrillo-Hermosill, J., del Río, P., & Könnölä, T. (2010). Diversity of eco-innovations: Reflections from selected case studies. Journal of Cleaner Production, 18(10-11), 1073-1083. https://doi.org/10.1016/j.jclepro.2010.02.014 open in new tab
7. Chachuli, F. S. M., Ludin, N. A., Mat, S., & Sopian, K. (2020). Renewable energy performance evaluation studies using the data envelopment analysis (DEA): A systematic review. Journal of Renewable and Sustainable Energy, 12, 062701. https://doi.org/10.1063/5.0024750 open in new tab
8. Chang, T.-S., Tone, K., & Wu, C.-H. (2021). Nested dynamic network data envelopment analysis models with infinitely many decision making units for portfolio evaluation. European Journal of Opera- tional Research, 291(2), 766-781. https://doi.org/10.1016/j.ejor.2020.09.044 open in new tab
9. Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429-444. https://doi.org/10.1016/0377-2217(78)90138-8 open in new tab
10. Cheba, K., & Bąk, I. (2021). Environmental production efficiency in the European Union countries as a tool for implementation of goal 7 of the 2030 Agenda. Energies, 14(15), 4593. https://doi.org/10.3390/en14154593 open in new tab
11. Cleantech Group. (2017). The Global Cleantech Innovation Index 2017. Retrieved September 5, 2021, from https://www.cleantech.com/indexes/the-global-cleantech-innovation-index/ open in new tab
12. Cooper, W. W., Seiford, L. M., & Tone, K. (2007). Data envelopment analysis: A comprehensive text with models, applications, references and DEA-Solver software (2 nd ed.). Springer. https://doi. org/10.1007/978-0-387-45283-8 open in new tab
13. Ding, L.-L., Lei, L., Wang, L., & Zang, L.-F. (2020). Assessing industrial circular economy performance and its dynamic evolution: An extended Malmquist index based on cooperative game network DEA. Science of the Total Environment, 731, 139001. https://doi.org/10.1016/j.scitotenv.2020.139001 open in new tab
14. European Commission. (2019). Communication from the Commission. The European Green Deal, COM(2019) 640 final. Brussels. Retrieved September 12, 2021, from https://eur-lex.europa.eu/legal- content/EN/TXT/?uri=CELEX:52019DC0640 open in new tab
15. European Commission. (2020). Circular economy indicators. Retrieved September 17, 2021, from https://ec.europa.eu/environment/ecoap/indicators/circular-economy-indicators_en open in new tab
16. European Commission. (2021). The eco-innovation scoreboard and the eco-innovation index. Retrieved September 5, 2021, from https://ec.europa.eu/environment/ecoap/indicators/index_en open in new tab
17. Eurostat. (2021). Database. Retrieved September 5, 2021, from https://ec.europa.eu/eurostat/web/main/ data/database open in new tab
18. Färe, R., & Grosskopf, S. (1996). Intertemporal production frontiers: With dynamic DEA. Kluwer Aca- demic Publishers. https://doi.org/10.1007/978-94-009-1816-0 open in new tab
19. Färe, R., & Grosskopf, S. (2000). Network DEA. Socio-Economic Planning Sciences, 34(1), 35-49. https://doi.org/10.1016/S0038-0121(99)00012-9 open in new tab
20. Färe, R., & Lovell, C. A. K. (1978). Measuring the technical efficiency of production. Journal of Eco- nomic Theory, 19(1), 150-162. https://doi.org/10.1016/0022-0531(78)90060-1 open in new tab
21. Färe, R., Grosskopf, S., Lindgren, B., & Roos, P. (1994). Productivity change in Swedish hospitals: A Malmquist output index approach. In A. Charnes, W. W. Cooper, A. Y. Lewin, & M. L. Seiford (Eds.), Data envelopment analysis: Theory, methodology and applications (pp. 253-272). Kluwer Academic Publishers. https://doi.org/10.1007/978-94-011-0637-5_13 open in new tab
22. Färe, R., Grosskopf, S., Lovell, C. A. K., & Pasurka, C. (1989). Multilateral productivity comparisons when some outputs are undesirable: A nonparametric approach. The Review of Economics and Statistics, 71(1), 90-98. https://doi.org/10.2307/1928055 open in new tab
23. Glavi, P., Lesjak, M., & Hirsbak, S. (2012, May 2-4). European training course on eco-efficiency. In 15 th European Roundtable on Sustainable Consumption and Production. Bregenz, Austria. Retrieved September 12, 2021, from https://vbn.aau.dk/en/publications/european-training-course-on-eco- efficiency open in new tab
24. Grochová, L. I., & Litzman, M. (2021). The efficiency in meeting measurable sustainable development goals. International Journal of Sustainable Development & World Ecology, 28(8), 709-719. https://doi.org/10.1080/13504509.2021.1882606 open in new tab
25. Guo, X., Lu, C. C., Lee, J. H., & Chiu, Y. H. (2017). Applying the dynamic DEA model to evaluate the energy efficiency of OECD countries and China. Energy, 134, 392-399. https://doi.org/10.1016/j.energy.2017.06.040 open in new tab
26. Jankiewicz, M., & Pietrzak, M. B. (2020). Assesment of trends in the share of expenditure on services and food in the Visegrad Group member states. International Journal of Business and Society, 21(2), 977-996. https://doi.org/10.33736/ijbs.3306.2020 open in new tab
27. Kalra, M., Panicker, D., Dixit, A., Jain, R., & Thakur, B. K. (2021). Ensuring access to sustainable and affordable energy to all. In W. Leal Filho, A. Azul, L. Brandli, A. Lange Salvia, & T. Wall (Eds.), Affordable and clean energy, encyclopedia of the UN Sustainable Development Goals (pp. 619-629). open in new tab
28. Springer. https://doi.org/10.1007/978-3-319-71057-0_139-1 open in new tab
29. Kao, C. (2014). Network data envelopment analysis: A review. European Journal of Operational Research, 239(1), 1-16. https://doi.org/10.1016/j.ejor.2014.02.039 open in new tab
30. Kiani Mavi, R., & Kiani Mavi, N. (2021). National eco-innovation analysis with big data: A common- weights model for dynamic DEA. Technological Forecasting and Social Change, 162, 120369. https://doi.org/10.1016/j.techfore.2020.120369 open in new tab
31. Kiani Mavi, R., Saen, R., & Goh, M. (2019). Joint analysis of eco-efficiency and eco-innovation with common weights in two-stage network DEA: A big data approach. Technological Forecasting and Social Change, 144, 553-562. https://doi.org/10.1016/j.techfore.2018.01.035 open in new tab
32. Kemp, R., Arundel, A., Rammer, C., Miedzinski, M., Tapia, C., Barbieri, N., Tűrkeli, S., Bassi, A. M., Mazzanti, M., Chapman, D., Diaz López, F., & McDowall, W. (2019). Maastricht manual on measur- ing eco-innovation for a green economy. Innovation for Sustainable Development Network. Maas- tricht, Netherlands. open in new tab
33. Koronakos, G. (2019). A taxonomy and review of the network data envelopment analysis literature. In G. Tsihrintzis, M. Virvou, E. Sakkopoulos, & L. Jain (Eds.), Machine learning paradigms. Learning and analytics in intelligent systems (pp. 255-311). Springer. https://doi.org/10.1007/978-3-030-15628-2_9 open in new tab
34. Kuosmanen, T. (2005). Measurement and analysis of eco-efficiency: An economist's perspective. Journal of Industrial Ecology, 9(4), 15-18. https://doi.org/10.1162/108819805775248025 open in new tab
35. Łącka, I., & Brzezicki, Ł. (2021). The efficiency and productivity evaluation of national innovation sys- tems in Europe. European Research Studies Journal, 24(3), 471-496. https://doi.10.35808/ersj/2440 open in new tab
36. Leal Filho, W., Fritzen, B., Ruiz Vargas, V., Paço, A., Zhang, Q., Doni, F., Azul, A. M., Vasconcelos, C. R. P., Nikolaou, I. E., Skouloudis, A., Weresa, M. A., Marczewska, M., Price, E., Anholon, R., Rampasso, I., Quelhas, O., Salvia, A. L., Ozuyar, P. G., Moggi, S., & Wu, Y. J. (2021). Social innovation for sus- tainable development: Assessing current trends. International Journal of Sustainable Development & World Ecology, 29(4), 311-322. https://doi.org/10.1080/13504509.2021.2013974 open in new tab
37. Li, H., Pang, S., Cao, Y., & Gao, J. (2021). Research on the evaluation of comprehensive efficiency of technological innovation and eco-environment in China. Journal of Cleaner Production, 283, 124603. https://doi.org/10.1016/j.jclepro.2020.124603 open in new tab
38. Łozowicka, A. (2020). Evaluation of the efficiency of sustainable development policy implementation in selected EU member states using DEA. The ecological dimension. Sustainability, 12(1), 435. https://doi.org/10.3390/su12010435 open in new tab
39. Madaleno, M., Moutinho, V., & Robaina, M. (2016). Economic and environmental assessment: EU cross-country. Efficiency ranking analysis. Energy Procedia, 106, 134-154. https://doi.org/10.1016/j.egypro.2016.12.111 open in new tab
40. Mardani, A., Streimikiene, D., Balezentis, T., Saman, M. Z. M., Nor, K. M., & Khoshnava, S. M. (2018). Data envelopment analysis in energy and environmental economics: An overview of the state-of- the-art and recent development trends. Energies, 11(8), 2002. https://doi.org/10.3390/en11082002 open in new tab
41. Mariz, F. B., Almeida, M. R., & Aloise, D. (2018). A review of Dynamic Data Envelopment Analysis: state of the art and applications. International Transactions in Operational Research, 25(2), 469-505. https://doi.org/10.1111/itor.12468 open in new tab
42. Miola, A., & Schiltz, F. (2019). Measuring sustainable development goals performance: How to monitor policy action in the 2030 agenda implementation? Ecological Economics, 164, 106373. https://doi.org/10.1016/j.ecolecon.2019.106373 open in new tab
43. Moutinho, V., & Madaleno, M. (2021a). A two-stage DEA model to evaluate the technical eco-efficiency indicator in the EU countries. International Journal of Environmental Research and Public Health, 18(6), 3038. https://doi.org/10.3390/ijerph18063038 open in new tab
44. Moutinho, V., & Madaleno, M. (2021b). Assessing eco-efficiency in Asian and African countries using stochastic frontier analysis. Energies, 14(4), 1168. https://doi.org/10.3390/en14041168 open in new tab
45. OECD. (2017). Green growth indicators 2017. OECD Publishing. https://doi.org/10.1787/9789264268586-en OECD. (2021). Data. Retrieved August 12, 2021, from https://data.oecd.org/ open in new tab
46. Pais-Magalhães, V., Moutinho, V., & Marques, A. C. (2021). Scoring method of eco-efficiency using the DEA approach: Evidence from European waste sectors. Environment, Development and Sustain- ability, 23(7), 9726-9748. https://doi.org/10.1007/s10668-020-00709-x. open in new tab
47. Park, M. S., Bleischewitz, R., Han, K. J., Jang, E. K., & Joo, J. H. (2017). Eco-innovation indices as tool for measuring eco-innovation. Sustainability, 9(12), 2206. https://doi.org/10.3390/su9122206 open in new tab
48. Rennings, K. (2000). Redefining innovation-Eco-innovation research and contribution from ecological economics. Ecological Economics, 32(2), 319-332. https://doi.org/10.1016/S0921-8009(99)00112-3 open in new tab
49. Sompolska-Rzechula, A., & Kurdyś-Kujawska, A. (2021). Towards understanding interactions between sustainable development goals: The role of climate-well-being linkages. Experience of EU countries. Energies, 14(7), 2025. https://doi.org/10.3390/en14072025 open in new tab
50. Stanković, J., Marjanović, I., & Stojković, N. (2021). DEA Assessment of socio-economic development of European countries. Management. Journal of Sustainable Business and Management Solutions in Emerging Economies, 26(1), 13-24. https://doi.org/10.7595/management.fon.2020.0012 open in new tab
51. Sustainable Development Solutions Network. (2021). Sustainable development report 2021. SDSN. Re- trieved September 2, 2021, from https://www.sdgindex.org/reports/ open in new tab
52. Tone, K. (2001). A slacks-based measure of efficiency in data envelopment analysis. European Journal of Operational Research, 130(3), 498-509. https://doi.org/10.1016/S0377-2217(99)00407-5 open in new tab
53. Tone, K. (2004). Malmquist productivity index: Efficiency change over time. In W. W. Cooper, L. M. Seiford, & J. Zhu (Eds.), International series in operations research & management science: Vol. 71. Handbook on data envelopment analysis (pp. 203-227). Springer. https://doi.org/10.1007/1-4020-7798-X_8 open in new tab
54. Tone, K., & Tsutsui, M. (2009). Network DEA: A slacks-base measure approach. European Journal of Operational Research, 197(1), 243-252. https://doi.org/10.1016/j.ejor.2008.05.027 open in new tab
55. Tone, K., & Tsutsui, M. (2010). Dynamic DEA: A slacks-based measure approach. Omega, 38(3-4), 145-156. https://doi.org/10.1016/j.omega.2009.07.003 open in new tab
56. Tone, K., & Tsutsui, M. (2014). Dynamic DEA with network structure: A slacks-based measure ap- proach. Omega, 42(1), 124-131. https://doi.org/10.1016/j.omega.2013.04.002 open in new tab
57. Tone, K., & Tsutsui, M. (2017). The dynamic network DEA model. In K. Tone (Ed.), Advances in DEA theory and applications with extensions to forecasting models (pp. 74-84). John Wiley & Sons. https://doi.org/10.1002/9781118946688.ch9 open in new tab
58. Tsaples, G., & Papathanasiou, J. (2021). Data envelopment analysis and the concept of sustainability: A review and analysis of the literature. Renewable and Sustainable Energy Reviews, 138, 110664. https://doi.org/10.1016/j.rser.2020.110664 open in new tab
59. United Nations. (1992a). United Nations sustainable development. Agenda 21. Retrieved September 7, 2021, from https://sustainabledevelopment.un.org/outcomedocuments/agenda21 open in new tab
60. United Nations. (1992b). United Nations framework convention on climate change. FCCC/INFOR- MAL/84 GE.05-62220 (E) 200705. Retrieved September 10, 2021, from https://unfccc.int/resource/ docs/convkp/conveng.pdf open in new tab
61. United Nations. (2015a). Transforming our world: The 2030 agenda for sustainable development. Geneva. open in new tab
62. United Nations. (2015b). Paris Agreement. Retrieved September 12, 2021, from https://unfccc.int/sites/ default/files/english_paris_agreement.pdf open in new tab
63. United Nations. (2015c). Indicators and a monitoring framework for the sustainable development goals. Launching a data revolution for the SDGs. Retrieved September 17, 2021, from https://resources.un- sdsn.org/indicators-and-a-monitoring-framework-for-sustainable-development-goals-launching-a- data-revolution-for-the-sdgs open in new tab
64. Vanhercke, B., Spasova, S., & Fronteddu, B. (2021). Social policy in the European Union: State of play 2020. Facing the pandemic. European Trade Union Institute (ETUI) and European Social Observa- tory (OSE). Retrieved September 20, 2021, from https://www.etui.org/publications/social-policy- european-union-state-play-2020 open in new tab
65. World Bank. (2021). Indicators. Retrieved August 10, 2021, from https://data.worldbank.org/indicator open in new tab
66. World Business Council for Sustainable Development. (2006). Eco-efficiency learning module. WBCSD. Retrieved September 12, 2021, from https://www.wbcsd.org/Projects/Education/Resources/Eco- efficiency-Learning-Module open in new tab
67. Yang, W.-C., Lu, W.-M., & Ramasamy, A. P. (2021). International environmental efficiency trends and the impact of the Paris Agreement. Energies, 14(15), 4503. https://doi.org/10.3390/en14154503 open in new tab
68. Yu, S., Liu, J., & Li, L. (2020). Evaluating provincial eco-efficiency in China: An improved network data envelopment analysis model with undesirable output. Environmental Science and Pollution Research, 27, 6886-6903. https://doi.org/10.1007/s11356-019-06958-2 open in new tab
69. Zalasiewicz, J., Waters, C., & Head, M. J. (2017). Anthropocene: Its straigraphic basis. Nature, 541, 289. https://doi.org/10.1038/541289b open in new tab
70. Zhang, Y., Mao, Y., Jiao, L., Shuai, C., & Zhang, H. (2021). Eco-efficiency, eco-technology innovation and eco-well-being performance to improve global sustainable development. Environmental Impact Assessment Review, 89, 106580. https://doi.org/10.1016/j.eiar.2021.106580 open in new tab
71. Zhou, H., Yang, Y., Chen, Y., Zhu, J., & Shi, Y. (2021). DEA application in sustainability 1996-2019: The origins, development, and future directions. In C. Chen, Y. Chen, & V. Jayaraman (Eds.), Interna- tional series in operations research & management science: Vol. 301. Pursuing sustainability. OR/MS applications in sustainable design, manufacturing, logistics, and resource management (pp. 71-109). open in new tab
72. Springer. https://doi.org/10.1007/978-3-030-58023-0_4 open in new tab
73. Kiani Mavi et al. (2019), Yang et al. (2021), Moutinho and Madaleno (2021a), Zhang et al. (2021), Li et al. (2021)
74. Energy consumption Guo et al. (2017), Kiani Mavi et al. (2019), Łozowicka (2020), Yang et al. (2021), Li et al. (2021)
75. Indicators of Eco-innovation Park et al. (2017), Kiani Mavi and Kiani Mavi (2021) SDG Grochová and Litzman (2021), Miola and Schiltz (2019), Sompolska- Rzechula and Kurdyś-Kujawska (2021), Cheba and Bąk (2021) Czechia 1.18 1.63 0.95 1.62 1.23 1.01 1.24 0.86 0.80 1.23 1.56 0.70 1.04 1.20 1.00 1.00 1.04 1.20
76. G. m. 1.09 1.23 0.97 1.27 1.13 0.97 1.17 0.98 0.88 1.09 1.33 0.91 1.03 1.10 1.00 1.00 1.03 1.10 open in new tab
77. Note: MI -Malmquist Index; FS -Frontier-shift effect; CU -Catch-up effect; G. m. -Geometric mean; open in new tab
78. > 8 -2017->2018; 8 > 9 -2018->2019. open in new tab

show (78) hide

Verified by:

No verification