Skip to main content

Variational Autoencoders for Assessing Sustainability

  • 56 Accesses

Part of the Lecture Notes in Electrical Engineering book series (LNEE,volume 846)


Reliable, impartial, and timely data models are urgently needed to inform global sustainability measures. As a response, implicit and explicit fuzzy methods that evaluate qualitative and quantitative variables have been proposed. Meanwhile, decision-makers are often compelled to address immediate over long-term risks. In the era of big data, artificial intelligence, and the internet we need to leverage the power of statistical models to support effective leadership. This article introduces a new technique for evaluating sustainability based on the Sustainability Assessment by Fuzzy Evaluation (SAFE) model: Variational Autoencoder plus graphical analysis (VAE&GA). This approach produces SAFE-like rankings backed by a dual axis chart that groups countries according to their most important unique indicators. VAE&GA is thus a more objective alternative to current fuzzy methods.


  • Artificial neural network
  • Sustainability indicators
  • Graphical sustainability ranking

This is a preview of subscription content, access via your institution.

Buying options

USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-93718-8_5
  • Chapter length: 16 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
USD   169.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-93718-8
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Hardcover Book
USD   219.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.


  1. Waas, T., Hugé, J., Verbruggen, A., Wright, T.: Sustainable development: a bird’s eye view. Sustainability 3(10), 1637–1661 (2011)

    CrossRef  Google Scholar 

  2. Sachs, J., Schmidt-Traub, G., Kroll, C., Durand-Declare, D., Teksoz, K.: SDG index and dashboards - global report. Bertelsmann Stiftung and Sustainable Development Solutions Network (SDSN), New York.

  3. Deeplizard: Machine Learning & Deep Learning Fundamentals, 21 November 2017

    Google Scholar 

  4. Zhu, Q., Zhang, R.: A classification supervised auto-encoder based on predefined evenly-distributed class centroids. arXiv:1902.00220v3 [cs.CV] (2020)

  5. Fu, H., Lei, P., Tao, H., Zhao, L., Yang, J.: Improved semi-supervised autoencoder for deception detection. PLoS One 14(10).

  6. Fu, X., et al.: Semi-supervised aspect-level sentiment classification model based on variational autoencoder. Knowl.-Based Syst. 171, 81–92 (2019).

    CrossRef  Google Scholar 

  7. Zhu, Q., Li, T.: Semi-supervised learning method based on predefined evenly-distributed class centroids. Appl. Intell. 50, 2770–2778 (2020).

    CrossRef  Google Scholar 

  8. Chorowski, J., Weiss, R.J., Bengio, S., van den Oord, A.: Unsupervised speech representation learning using WaveNet autoencoders. IEEE/ACM Trans. Audio Speech Lang. Process. 27(12), 2041–2053 (2019).

    CrossRef  Google Scholar 

  9. Yusiong, J.P.T., Naval, P.C.: AsiANet: autoencoders in autoencoder for unsupervised monocular depth estimation. In: Proceedings - 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019, pp. 443–451 (2019).

  10. Bengio, Y.: Learning deep architectures for AI. Found. Trends Mach. Learn. 2(1), 1–27 (2009).

    MathSciNet  CrossRef  MATH  Google Scholar 

  11. Sarkar, D., Bali, R., Sharma, T.: Practical Machine Learning with Python. Apress, Berkeley (2018).

    CrossRef  Google Scholar 

  12. Flach, P.: Machine Learning: The Art and Science of Algorithms that Make Sense of Data (2012).

  13. Rocca, J.: Understanding Variational Autoencoders (VAEs). Building, step by step, the reasoning that leads to VAEs. Towards Data Science (2019).

  14. Zadeh, L.A.: Similarity relations and fuzzy orderings. Inf. Sci. 3(2), 177–200 (1971)

    MathSciNet  CrossRef  Google Scholar 

  15. Grigoroudis, E., Kouikoglou, V.S., Phillis, Y.A.: SAFE 2013: Sustainability of countries updated. Ecol. Ind. 38, 61–66 (2014)

    CrossRef  Google Scholar 

  16. Grigoroudis, E., Kouikoglou, V., Phillis, Y.: SAFE 2019: Updates and new sustainability findings worldwide. Ecol. Ind. 121, 107072 (2019)

    CrossRef  Google Scholar 

  17. Phillis, Y.A., Grigoroudis, E., Kouikoglou, V.S.: Sustainability ranking and improvement of countries. Ecol. Econ. 70(3), 542–553 (2011)

    CrossRef  Google Scholar 

  18. Tan, Y., Shuai, C., Jiao, L., Shen, L.: Adaptive neuro-fuzzy inference system approach for urban sustainability assessment: a China case study. Sustain. Dev. 26(6), 749–764 (2018).

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to José Fernando Romero-Cañizares .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Romero-Cañizares, J.F., Vicente-Galindo, P. (2022). Variational Autoencoders for Assessing Sustainability. In: Berrezueta, S., Abad, K. (eds) Doctoral Symposium on Information and Communication Technologies - DSICT. Lecture Notes in Electrical Engineering, vol 846. Springer, Cham.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-93717-1

  • Online ISBN: 978-3-030-93718-8

  • eBook Packages: EngineeringEngineering (R0)