Measuring comparative eco-efficiency in the Eurasian Economic Union using MaxDEA X 12.2 software
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Abstract
In recent years, Data Envelopment Analysis (DEA) has gained popularity as a robust approach for assessing the eco-efficiency of economic units of different scales. This paper demonstrates the capabilities of the latest standalone version of the open-access MaxDEA X 12.2 software to measure comparative eco-efficiency, using the countries of the Eurasian Economic Union (EAEU) as a case study for the period 2015-2023. The study uses a traditional "black box" DEA model with atmospheric emissions, waste generation, and water consumption as inputs, and GDP along with population as outputs, allowing for a structural eco-efficiency assessment focused on resource use and economic structure. Calculation results obtained using the window method show that Belarus and Kyrgyzstan have the highest eco-efficiency over the entire observation window, while Kazakhstan and Russia lag behind, correlating with their natural resource-dependent economies. The analysis also provides target reductions in emissions and resource use for inefficient countries to improve eco-efficiency. In addition, the paper highlights how the MaxDEA X 12.2 software simplifies data handling and model configuration for eco-efficiency assessments by supporting different model orientations and returns to scale assumptions. Finally, it discusses potential extensions to more complex two-stage DEA models for comprehensive eco-efficiency assessments, subject to data availability. This work highlights the usefulness of MaxDEA X 12.2 as an accessible tool for eco-efficiency benchmarking and managerial decision support in the context of regional economic integration.
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References
Angulo-Meza, L., González-Araya, M., Iriarte, A., Rebolledo-Leiva, R., & de Mello, J. C. S. (2019). A multiobjective DEA model to assess the eco-efficiency of agricultural practices within the CF+ DEA method. Computers and Electronics in Agriculture, 161, 151-161. https://doi.org/10.1016/j.compag.2018.05.037
Avadí, Á., Vázquez-Rowe, I., & Fréon, P. (2014). Eco-efficiency assessment of the Peruvian anchoveta steel and wooden fleets using the LCA+ DEA framework. Journal of Cleaner Production, 70, 118-131. https://doi.org/10.1016/j.jclepro.2014.01.047
Eurasian Economic Commission. (2025). Environment. Eurasian Economic Union. https://eec.eaeunion.org/comission/department/ dep_stat/union_stat/publications/newsletters_collections_booklets/environment.php
Ezici, B., Eğilmez, G., & Gedik, R. (2020). Assessing the eco-efficiency of US manufacturing industries with a focus on renewable vs. non-renewable energy use: An integrated time series MRIO and DEA approach. Journal of Cleaner Production, 253, 119630. https://doi.org/10.1016/j.jclepro.2019.119630
Gastaldi, M., Lombardi, G., Rapposelli, A., & Romano, G. (2020). The efficiency of waste sector in italy: An application by data envelopment analysis. Environmental and Climate Technologies, 24(3), 225-238. https://doi.org/10.2478/rtuect-2020-0099
He, K., & Jie, L. (2025). Eco-efficiency assessment of regional industrial systems in China considering scale heterogeneity. Sustainable Futures, 9, 100568. https://doi.org/10.1016/j.sftr.2025.100568
Henriques, C. O., Gouveia, C. M., Tenente, M., & da Silva, P. P. (2022). Employing value-based DEA in the eco-efficiency assessment of the electricity sector. Economic Analysis and Policy, 73, 826-844. https://doi.org/10.1016/j.eap.2022.01.010
Huang, M., Ding, R., & Xin, C. (2020). Impact of technological innovation and industrial‐structure upgrades on ecological efficiency in China in terms of spatial spillover and the threshold effect. Integrated Environmental Assessment and Management, 17(4), 852-865. https://doi.org/10.1002/ieam.4381
Li, Z., Wei, Y., Li, Y., Wang, Z., & Zhang, J. (2020). China’s provincial eco-efficiency and its driving factors - Based on network DEA and PLS-SEM method. International journal of environmental research and public health, 17(22), 8702. https://doi.org/10.3390/ijerph17228702
Liu, J., Zhang, J., & Fu, Z. (2017). Tourism eco-efficiency of Chinese coastal cities–Analysis based on the DEA-Tobit model. Ocean & coastal management, 148, 164-170. https://doi.org/10.1016/j.ocecoaman.2017.08.003
Llanquileo-Melgarejo, P., & Molinos-Senante, M. (2021). Evaluation of economies of scale in eco-efficiency of municipal waste management: an empirical approach for Chile. Environmental Science and Pollution Research, 28, 28337-28348. https://doi.org/10.1007/s11356-021-12529-1
Lorenzo-Toja, Y., Vázquez-Rowe, I., Chenel, S., Marín-Navarro, D., Moreira, M. T., & Feijoo, G. (2015). Eco-efficiency analysis of Spanish WWTPs using the LCA+ DEA method. Water research, 68, 651-666. https://doi.org/10.1016/j.watres.2014.10.040
MaxDEA. (2025). Download Data. MaxDEA. https://www.maxdea.com/Download.html
Moutinho, V., Fuinhas, J. A., Marques, A. C., & Santiago, R. (2018). Assessing eco-efficiency through the DEA analysis and decoupling index in the Latin America countries. Journal of Cleaner Production, 205, 512-524. https://doi.org/10.1016/j.jclepro.2018.08.322
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 Sustainability, 23, 9726-9748. https://doi.org/10.1007/s10668-020-00709-x
Picazo-Tadeo, A. J., Beltrán-Esteve, M., & Gómez-Limón, J. A. (2012). Assessing eco-efficiency with directional distance functions. European Journal of Operational Research, 220(3), 798-809. https://doi.org/10.1016/j.ejor.2012.02.025
Ratner, S. V., Shaposhnikov, A. M., Lychev, A. V. (2023) Network DEA and its applications (2017–2022): A systematic literature review. Mathematics, 11(9), 2141. https://doi.org/10.3390/math11092141
Ratner, S., Lychev, A., Rozhnov, A., Lobanov, I. (2021) Evaluation of regional environmental management systems in russia using data envelopment analysis. Mathematics, 9(18):2210. https://doi.org/10.3390/math9182210
Ratner, S., Ratner, P. (2017) DEA-based dynamic assessment of regional environmental efficiency. Applied Computer Science, 13(2): 48–60. https://doi.org/10.23743/acs-2017-13
Ren, W., Zhang, Z., Wang, Y., Xue, B., & Chen, X. (2020). Measuring regional eco-efficiency in China (2003–2016): A “Full World” perspective and network data envelopment analysis. International journal of environmental research and public health, 17(10), 3456. https://doi.org/10.3390/ijerph17103456
Romano, G., & Molinos-Senante, M. (2020). Factors affecting eco-efficiency of municipal waste services in Tuscan municipalities: An empirical investigation of different management models. Waste Management, 105, 384-394. https://doi.org/10.1016/j.wasman.2020.02.028
Romano, G., Molinos-Senante, M., Carosi, L., Llanquileo-Melgarejo, P., Sala-Garrido, R., & Mocholi-Arce, M. (2021). Assessing the dynamic eco-efficiency of Italian municipalities by accounting for the ownership of the entrusted waste utilities. Utilities Policy, 73, 101311. https://doi.org/10.1016/j.jup.2021.101311
Shao, L., Yu, X., & Feng, C. (2019). Evaluating the eco-efficiency of China's industrial sectors: A two-stage network data envelopment analysis. Journal of environmental management, 247, 551-560. https://doi.org/10.1016/j.jenvman.2019.06.099
Song, Y. Y., Li, J. J., Wang, J. L., Yang, G. L., & Chen, Z. (2022). Eco-efficiency of Chinese transportation industry: A DEA approach with non-discretionary input. Socio-Economic Planning Sciences, 84, 101383. https://doi.org/10.1016/j.seps.2022.101383
Tsai, W. H., Lee, H. L., Yang, C. H., Huang, C. C., (2016). Input-output analysis for sustainability by using DEA method: A comparison study between European and Asian countries. Sustainability, 8(12), 1230. http://dx.doi.org/10.3390/su8121230
Wang, Q., Tang, J., & Choi, G. (2021). A two-stage eco-efficiency evaluation of China’s industrial sectors: A dynamic network data envelopment analysis (DNDEA) approach. Process Safety and Environmental Protection, 148, 879-892. https://doi.org/10.1016/j.psep.2021.02.005
Wu, Y., Chen, Z., & Xia, P. (2018). An extended DEA-based measurement for eco-efficiency from the viewpoint of limited preparation. Journal of Cleaner Production, 195, 721-733. https://doi.org/10.1016/j.jclepro.2018.05.200
Yang, L., Chen, S., Chiu, Y. H., Chang, T. H., & Wang, Y. (2024). Reassessment of industrial eco-efficiency in China under the sustainable development goals: A meta two-stage parallel entropy dynamic DDF-DEA model. Journal of Cleaner Production, 447, 141275. https://doi.org/10.1016/j.jclepro.2024.141275
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
Zhang, X., & Xu, D. (2022). Assessing the eco-efficiency of complex forestry enterprises using LCA/time-series DEA methodology. Ecological Indicators, 142, 109166. https://doi.org/10.1016/j.ecolind.2022.109166
Zurano-Cervelló, P., Pozo, C., Mateo-Sanz, J. M., Jiménez, L., Guillén-Gosálbez, G. (2019). Sustainability efficiency assessment of the electricity mix of the 28 EU member countries combining data envelopment analysis and optimized projections. Energy Policy, 134, 110921. https://doi.org/10.1016/j.enpol.2019.110921
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