Skip to main content
SpringerLink
Log in

Studying the Evolution of the ‘Circular Economy’ Concept Using Topic Modelling

  • Conference paper
  • First Online:
Intelligent Data Engineering and Automated Learning – IDEAL 2019 (IDEAL 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11872))

Abstract

Circular Economy has gained immense popularity for its perceived capacity to operationalise sustainable development. However, a comprehensive long-term understanding of the concept, characterising its evolution in academic literature, has not yet been provided. As a first step, we apply unsupervised topic models on academic articles to identify patterns in concept evolution. We generate topics using LDA, and investigate topic prevalence over time. We determine the optimal number of topics for the model (k) through coherence scorings and evaluate the topic model results by expert judgement. Specifying k as 20, we find topics in the literature focussing on resources, business models, process modelling, conceptual research and policies. We identify a shift in the research focus of contemporary literature, moving away from the Chinese pre-dominance to a European perspective, along with a shift towards micro level interventions, e.g., circular design, business models, around 2014–2015.

Supported by The University of Manchester.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://www.scopus.com/search/form.uri?display=basic.

References

  1. Blei, D.M., Lafferty, J.D.: Dynamic topic models. In: Proceedings of the 23rd International Conference on Machine learning, pp. 113–120. ACM, 2006 June

    Google Scholar 

  2. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)

    MATH  Google Scholar 

  3. Blomsma, F., Brennan, G.: The emergence of circular economy: a new framing around prolonging resource productivity. J. Ind. Ecol. 21(3), 603–614 (2017)

    Article  Google Scholar 

  4. Bocken, N.M., De Pauw, I., Bakker, C., van der Grinten, B.: Product design and business model strategies for a circular economy. J. Ind. Prod. Eng. 33(5), 308–320 (2016)

    Google Scholar 

  5. Bolelli, L., Ertekin, Ş., Giles, C.L.: Topic and trend detection in text collections using Latent Dirichlet Allocation. In: Boughanem, M., Berrut, C., Mothe, J., Soule-Dupuy, C. (eds.) ECIR 2009. LNCS, vol. 5478, pp. 776–780. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-00958-7_84

    Chapter  Google Scholar 

  6. Boons, F., Spekkink, W., Jiao, W.: A process perspective on industrial symbiosis: theory, methodology, and application. J. Ind. Ecol. 18(3), 341–355 (2014)

    Article  Google Scholar 

  7. Boons, F., Spekkink, W., Mouzakitis, Y.: The dynamics of industrial symbiosis: a proposal for a conceptual framework based upon a comprehensive literature review. J. Clean. Prod. 19(9–10), 905–911 (2011)

    Article  Google Scholar 

  8. Bradie, M.: Assessing evolutionary epistemology. Biol. Philos. 1(4), 401–459 (1986)

    Article  Google Scholar 

  9. Chang, J.: lda: Collapsed Gibbs Sampling Methods for Topic Models. R package version 1.2.3 (2010). http://CRAN.R-project.org/package=lda

  10. Chang, J., Gerrish, S., Wang, C., Boyd-Graber, J. L., Blei, D.M.: Reading tea leaves: how humans interpret topic models. In: Advances in Neural Information Processing Systems, pp. 288–296 (2009)

    Google Scholar 

  11. Chen, T.H., Thomas, S.W., Hassan, A.E.: A survey on the use of topic models when mining software repositories. Empir. Softw. Eng. 21(5), 1843–1919 (2016)

    Article  Google Scholar 

  12. Feinerer, I.: Introduction to the tm Package. Text Mining in R (2015). ftp://videolan.cs.pu.edu.tw/network/CRAN/web/packages/tm/vignettes/tm.pdf

  13. Geisendorf, S., Pietrulla, F.: The circular economy and circular economic concepts—a literature analysis and redefinition. Thunderbird Int. Bus. Rev. 60(5), 771–782 (2018)

    Article  Google Scholar 

  14. Geissdoerfer, M., Savaget, P., Bocken, N.M., Hultink, E.J.: The circular economy–a new sustainability paradigm? J. Clean. Prod. 143, 757–768 (2017)

    Article  Google Scholar 

  15. Ghisellini, P., Cialani, C., Ulgiati, S.: A review on circular economy: the expected transition to a balanced interplay of environmental and economic systems. J. Clean. Prod. 114, 11–32 (2016)

    Article  Google Scholar 

  16. Goertz, G.: Social Science Concepts: A User’s Guide. Princeton University Press, Princeton (2006)

    Google Scholar 

  17. Griffiths, T.L., Steyvers, M.: Finding scientific topics. Proc. Natl. Acad. Sci. 101(Suppl. 1), 5228–5235 (2004)

    Article  Google Scholar 

  18. Grun, B., Hornik, K.: Topicmodels: an R package for fitting topic models. J. Stat. Softw. 40(13), 1–30 (2011). https://www.jstatsoft.org/v040/i13, https://doi.org/10.18637/jss.v040.i13. ISSN 1548-7660

  19. Hall, D., Jurafsky, D., Manning, C.D.: Studying the history of ideas using topic models. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 363–371. Association for Computational Linguistics, 2008 October

    Google Scholar 

  20. Hindle, A., Godfrey, M.W., Holt, R.C.: What’s hot and what’s not: windowed developer topic analysis. In: 2009 IEEE International Conference on Software Maintenance, pp. 339–348. IEEE, 2009 September

    Google Scholar 

  21. Hull, D.L.: Central subjects and historical narratives. Hist. Theory 14(3), 253–274 (1975)

    Article  Google Scholar 

  22. Hull, D.L.: Science as a Process: An Evolutionary Account of the Social and Conceptual Development of Science. University of Chicago Press, Chicago (2010)

    Google Scholar 

  23. Jiao, W., Boons, F.: Policy durability of Circular Economy in China: a process analysis of policy translation. Resour. Conserv. Recycl. 117, 12–24 (2017)

    Article  Google Scholar 

  24. Kao, A., Poteet, S.R. (eds.): Natural language processing and text mining. Springer, London (2007). https://doi.org/10.1007/978-1-84628-754-1

    Book  MATH  Google Scholar 

  25. Kirchherr, J., Reike, D., Hekkert, M.: Conceptualizing the circular economy: an analysis of 114 definitions. Resour. Conserv. Recycl. 127, 221–232 (2017)

    Article  Google Scholar 

  26. Kuhn, T.S.: The road since structure. In: PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association, vol. 1990, no. 2, pp. 3–13. Philosophy of Science Association, 1990 January

    Google Scholar 

  27. Lazarevic, D., Valve, H.: Narrating expectations for the circular economy: towards a common and contested European transition. Energy Res. Soc. Sci. 31, 60–69 (2017)

    Article  Google Scholar 

  28. Margolis, E., Laurence, S.: Concepts. In: Zalta, E.N. (ed.) The Stanford Encyclopedia of Philosophy. https://plato.stanford.edu/archives/sum2019/entries/concepts/. (Summer 2019 Edition)

  29. Masi, D., Kumar, V., Garza-Reyes, J.A., Godsell, J.: Towards a more circular economy: exploring the awareness, practices, and barriers from a focal firm perspective. Prod. Plan. Control. 29(6), 539–550 (2018)

    Article  Google Scholar 

  30. Merli, R., Preziosi, M., Acampora, A.: How do scholars approach the circular economy? A systematic literature review. J. Clean. Prod. 178, 703–722 (2018)

    Article  Google Scholar 

  31. Mimno, D., Wallach, H.M., Talley, E., Leenders, M., McCallum, A.: Optimizing semantic coherence in topic models. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 262–272. Association for Computational Linguistics, 2011 July

    Google Scholar 

  32. Newman, D., Noh, Y., Talley, E., Karimi, S., Baldwin, T.: Evaluating topic models for digital libraries. In: Proceedings of the 10th Annual Joint Conference on Digital Libraries, pp. 215–224. ACM, 2010 June

    Google Scholar 

  33. Nobre, G.C., Tavares, E.: Scientific literature analysis on big data and internet of things applications on circular economy: a bibliometric study. Scientometrics 111(1), 463–492 (2017)

    Article  Google Scholar 

  34. Prendeville, S., Cherim, E., Bocken, N.: Circular cities: mapping six cities in transition. Environ. Innov. Soc. Transit. 26, 171–194 (2018)

    Article  Google Scholar 

  35. Röder, M., Both, A., Hinneburg, A.: Exploring the space of topic coherence measures. In: Proceedings of the Eighth ACM International Conference on Web Search and Data Mining, pp. 399–408. ACM, 2015 February

    Google Scholar 

  36. Rosner, F., Hinneburg, A., Röder, M., Nettling, M., Both, A.: Evaluating topic coherence measures. arXiv preprint arXiv:1403.6397 (2014)

  37. Shin, S.H., Kwon, O., Ruan, X., Chhetri, P., Lee, P., Shahparvari, S.: Analyzing sustainability literature in maritime studies with text mining. Sustainability 10(10), 3522 (2018)

    Article  Google Scholar 

  38. Spekkink, W.: Institutional capacity building for industrial symbiosis in the Canal Zone of Zeeland in the Netherlands: a process analysis. J. Clean. Prod. 52, 342–355 (2013)

    Article  Google Scholar 

  39. Stevens, K., Kegelmeyer, P., Andrzejewski, D., Buttler, D.: Exploring topic coherence over many models and many topics. In: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp. 952–961. Association for Computational Linguistics, 2012 July

    Google Scholar 

  40. Su, B., Heshmati, A., Geng, Y., Yu, X.: A review of the circular economy in China: moving from rhetoric to implementation. J. Clean. Prod. 42, 215–227 (2013)

    Article  Google Scholar 

  41. Sun, L., Yin, Y.: Discovering themes and trends in transportation research using topic modeling. Transp. Res. Part C Emerg. Technol. 77, 49–66 (2017)

    Article  Google Scholar 

  42. Thomas, S.W., Adams, B., Hassan, A.E., Blostein, D.: Modeling the evolution of topics in source code histories. In: Proceedings of the 8th Working Conference on Mining Software Repositories, pp. 173–182. ACM, 2011 May

    Google Scholar 

  43. Thomas, S.W., Adams, B., Hassan, A.E., Blostein, D.: Studying software evolution using topic models. Sci. Comput. Program. 80, 457–479 (2014)

    Article  Google Scholar 

  44. Wang, X., McCallum, A.: Topics over time: a non-Markov continuous-time model of topical trends. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 424–433. ACM, 2006 August

    Google Scholar 

  45. Zhao, W., et al.: A heuristic approach to determine an appropriate number of topics in topic modeling. In: BMC Bioinformatics, vol. 16, no. 13, p. S8. BioMed Central, 2015 December

    Google Scholar 

Download references

Acknowledgements

The authors are grateful to Helen Holmes, Wouter Spekkink, Maria Sharmina, Malte Roedl, and Carly Fletcher for serving as the experts to evaluate the topic model results and providing their valuable feedback.

Sampriti Mahanty acknowledges the support from Alliance Manchester Business School.

Author information

Authors and Affiliations

  1. Alliance Manchester Business School, The University of Manchester, Manchester, UK

    Sampriti Mahanty, Frank Boons & Julia Handl

  2. School of Computer Science, The University of Manchester, Manchester, UK

    Riza Batista-Navarro

Authors
  1. Sampriti Mahanty
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Frank Boons
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Julia Handl
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Riza Batista-Navarro
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sampriti Mahanty .

Editor information

Editors and Affiliations

  1. University of Manchester, Manchester, UK

    Hujun Yin

  2. Technical University of Madrid, Madrid, Spain

    David Camacho

  3. University of Birmingham, Birmingham, UK

    Peter Tino

  4. University of Huelva, Huelva, Spain

    Antonio J. Tallón-Ballesteros

  5. University of Exeter, Exeter, UK

    Ronaldo Menezes

  6. University of Manchester, Manchester, UK

    Richard Allmendinger

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Mahanty, S., Boons, F., Handl, J., Batista-Navarro, R. (2019). Studying the Evolution of the ‘Circular Economy’ Concept Using Topic Modelling. In: Yin, H., Camacho, D., Tino, P., Tallón-Ballesteros, A., Menezes, R., Allmendinger, R. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2019. IDEAL 2019. Lecture Notes in Computer Science(), vol 11872. Springer, Cham. https://doi.org/10.1007/978-3-030-33617-2_27

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-33617-2_27

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33616-5

  • Online ISBN: 978-3-030-33617-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation