Energy-Environmental Efficiency in South America: A Comparative Analysis with DEA under Different Returns to Scale
DOI:
https://doi.org/10.29327/2565368.3.1-9Keywords:
Environmental Energy Efficiency, Data Envelopment Analysis (DEA), Sustainability in South AmericaAbstract
Given the challenges posed by climate change and the energy transition, assessing the efficiency in the use of environmental and energy resources has become essential to support sustainable public policies. This study analyzes the energy-environmental efficiency of 11 South American countries, considering inputs such as energy consumption, CO₂ emissions and particulate matter (PM2.5), and outputs such as life expectancy and participation of renewable sources. The methodology adopted was Data Envelopment Analysis (DEA), in the CRS (constant returns to scale) and VRS (variable returns to scale) models, with input orientation. A computational tool was also developed in Python, with the PuLP library, to automate the calculations and facilitate replication. The 2023 data were obtained from the Our World in Data database. Preliminary results indicate that Uruguay, Ecuador, Bolivia and Paraguay presented high efficiency in both models, while Venezuela and Chile obtained lower performance. The comparison between the CRS and VRS models highlights the importance of considering the effects of scale to correctly assess efficiency, revealing that part of the inefficiency is related to structural limitations. The findings reinforce the usefulness of DEA as a decision-making tool in sustainability, as well as its potential to guide more equitable and effective energy strategies in South America.
References
Abbood, K., Mészáros, F., & Alatawneh, A. (2025). Analysis of sustainable efficiency of freight transport in major European economies: An integrated multi-region input-output and DEA approach. Periodica Polytechnica Transportation Engineering, 53(1), 31–49. https://doi.org/10.3311/PPtr.38266
Alves, E. R. A., & Rocha, D. P. (2015). Eficiência técnica na agricultura brasileira: uma aplicação do modelo DEA com retornos variáveis de escala. Revista de Economia e Sociologia Rural, 53(2), 231–250.
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.
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
Garmatz, A., Vieira, G. B. B., & Sirena, S. A. (2021). Avaliação da eficiência técnica dos hospitais de ensino do Brasil utilizando a análise envoltória de dados. Ciência & Saúde Coletiva, 26, 3447–3457.
Hadi-Vencheh, A., Khodadadipour, M., Tan, Y., Arman, H., & Roubaud, D. (2024). Cross-efficiency analysis of energy sector using stochastic DEA: Considering pollutant emissions. Journal of Environmental Management, 364, 121319. https://doi.org/10.1016/j.jenvman.2024.121319
Ji, A., Wei, B., & Ma, Y. (2024). Incremental data envelopment analysis model and applications in sustainable efficiency evaluation. Computational Economics, 64, 461–486. https://doi.org/10.1007/s10614-023-10447-7
Liu, J. S., Lu, W. M., & Wang, W. K. (2020). DEA and machine learning for performance prediction: A case of US hospitals. Omega, 94, 102055.
Marinho, A., & Façanha, L. O. (2003). Análise de eficiência técnica em hospitais brasileiros utilizando DEA com retornos variáveis de escala. Revista de Administração Pública, 37(5), 1023–1046.
Organization Meteorológica Mundial. (n.d.). Estado provisório do clima global 2023. Recuperado em 11 de maio de 2025, de https://wmo.int/files/provisional-state-of-global-climate-2023
Picazo-Tadeo, A. J., Reig-Martínez, E., & Hernández-Sancho, F. (2011). Assessing eco-efficiency with directional distance functions. Environmental and Resource Economics, 50(2), 163–174.
Simionato, V. E., & Cassel, R. A. (2019). Avaliação da eficiência de agências de crédito no processo de concessão por meio da análise envoltória de dados (DEA). In XXXIX Encontro Nacional de Engenharia de Produção (ENEGEP) (pp. 1–17). Santos, SP.
Tran, M. N. (2025). Energy efficiency assessment in CPTPP countries through the three-stage SBM-DEA model. International Journal of Energy Sector Management, ahead-of-print. https://doi.org/10.1108/IJESM-09-2024-0046
Wanke, P., Barros, C. P., & Falcão, R. P. Q. (2016). Predicting efficiency in Brazilian port terminals using data envelopment analysis and artificial neural networks. International Journal of Shipping and Transport Logistics, 8(5), 594–612.
Yin, L., Alnafrah, I., & Zhou, Y. (2024). A systemic efficiency measurement of resource management and sustainable practices: A network bias-corrected DEA assessment of OECD countries. Resources Policy, 90, 104771. https://doi.org/10.1016/j.resourpol.2024.104771
Zhou, P., Ang, B. W., & Poh, K. L. (2018). A non-radial DEA approach to measuring environmental performance. European Journal of Operational Research, 265(1), 243–257.
Downloads
Additional Files
Published
Issue
Section
License
Copyright (c) 2025 Nehemias Anastácio Santos da Silva, Hícaro Raffael Dionízio Silva, Susane de Farias Gomes, Grazielle Anastácia Santos da Silva, Tulio Fidel Orrego Rodriguez

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors who publish with Socioeconomic Analytics retain the copyright of their work and agree to license it under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) license. This means that the work can be shared, copied, and redistributed in any medium or format, as long as it is not used for commercial purposes, and the original work is properly cited. The work cannot be changed in any way or used to create derivative works.


