Skip to main content

Advertisement

SpringerLink
Log in

A Machine Learning Analysis of the Recent Environmental and Resource Economics Literature

  • Published:
Environmental and Resource Economics Aims and scope Submit manuscript

Abstract

We use topic modeling to study research articles in environmental and resource economics journals in the period 2000–2019. Topic modeling based on machine learning allows us to identify and track latent topics in the literature over time and across journals, and further to study the role of different journals in different topics and the changing emphasis on topics in different journals. The most prevalent topics in environmental and resource economics research in this period are growth and sustainable development and theory and methodology. Topics on climate change and energy economics have emerged with the strongest upward trends. When we look at our results across journals, we see that journals have different topical profiles and that many topics mainly appear in one or a few selected journals. Further investigation reveal latent semantic structures across research themes that only the insider would be aware.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

References

  • Aletras, N., M. Stevenson (2013). Evaluating topic coherence using distributional semantics. In Proceedings of the 10th International Conference on Computational Semantics (IWCS 2+13)—Long papers, pp. 13–22.

  • Belaia M, Funke M, Glanemann N (2017) Global warming and a potential tipping point in the Atlantic thermohaline circulation: the role of risk aversion. Environ Resource Econ 67:93–125

    Article  Google Scholar 

  • Bird S, Klein E, Loper E (2009) Natural language processing with Python: analyzing text with the natural language toolkit. O’Reilly Media Inc, Sebastopol, CA

    Google Scholar 

  • Blei DM (2012) Probabilistic topic models. Commun ACM 55(4):77–84

    Article  Google Scholar 

  • Blei DM, Lafferty JD (2007) A correlated topic model of science. Annal Appl Stat 1(1):17–35

    Google Scholar 

  • Blei DM, Ng AY, Jordan MI (2003) Latent Dirichlet allocation. J Mach Learn Res 3:992–1022

    Google Scholar 

  • Bowles S, Carlin W (2019) What students learn in Economics 101: time for a change. J Econ Lit 58:176–214

    Article  Google Scholar 

  • Bowles S, Carlin W, Halliday SD, Subramanyam S (2020) What do we think an economist should know? A machine learning investigation of research and intermediate-level textbooks, Manuscript

    Google Scholar 

  • Chang, J., Gerrish S, Wang C, Boyd-Graber J, Blei D (2009) Reading tea leaves: how humans interpret topic modles. In: Bengio Y, Schuurmans D, Lafferty J, Williams C, Culotta A (eds) Advances in neural information processing systems. Curran Associates Inc., NY, pp 288–296

  • Haab TC (2003) Temporal correlation in recreation demand models with limited data. J Environ Econ Manag 45(2):195–212

    Article  Google Scholar 

  • Hauber AB, Parsons GR (2000) The effect of nesting structure specification on welfare estimation in a random utility model of recreation demand: an application to the demand for recreational fishing. Am J Agr Econ 82(3):501–514

    Article  Google Scholar 

  • Hofman T (2001) Unsupervised learning by probabilistic latent semantic analysis. Mach Learn 42(1–2):177–196

    Article  Google Scholar 

  • Jelodar H, Wang Y, Yuan C, Feng X, Jiang X, Li Y, Zhao L (2019) Latent Dirichlet Allocation (LDA) and topic modeling: models, applications, a survey. Multim Tools Appl 78(11):15169–15211

    Article  Google Scholar 

  • Just DR, Just RE (2016) Empirical identification of behavioral choice models under risk. Am J Agr Econ 98(4):1181–1194

    Article  Google Scholar 

  • Kube R, Löschel A, Mertens H, Requate T (2018) Research trends in environmental and resource economics: insights from four decades of JEEM. J Environ Econ Manag 92:433–464

    Article  Google Scholar 

  • Kuriyama K, Hanemann WM, Hilger JR (2010) A latent segmentation approach to a Kuhn-Tucker model: an application to recreation demand. J Environ Econ Manag 60(3):209–220

    Article  Google Scholar 

  • Loper, E., S. Bird (2002). NLTK: the natural language toolkit. arXiv preprint cs/0205028.

  • MacKay DJC (2009) Sustainable energy. UIT Cambridge Ltd, Cambridge, England

    Google Scholar 

  • Pycroft J, Abrell J, Ciscar J-C (2016) Response to “comment on ‘the global impacts of extreme sea-level rise: a comprehensive economic assessment”’ (R. Tol et al.). Environ Resour Econ 64:345–347

    Article  Google Scholar 

  • Rehurek, R., P. Sojka (2010). Software framework for topic modelling with large corpora. In Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks.

  • Röder, M., A. Both, A. Hinneburg (2015). Exploring the space of topic coherence measures. In Proceedings of the Eight ACM International Conference on Web Search and Data Mining, pp. 399–408.

  • Sievert, C., K.E. Shirley (2014). LDAvis: A method for visualizing and interpreting topics. Proceedings of the Workshop on Interactive Language Learning, Visualization, and Interfaces, pp. 63–70.

  • Steyvers M, Griffiths T (2007) Probabilistic topic models. Handbook Latent Semantic Anal 427(7):424–440

    Google Scholar 

  • Syed, S., M. Spruit (2017). Full-text or abstract? Examining topic coherence scores using latent Dirichlet allocation. In 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp. 165–174.

  • Von Haefen RH, Phaneuf DJ (2008) Identifying demand parameters in the presence of unobservables: a combined revealed and stated preference approach. J Environ Econ Manag 56(1):19–32

    Article  Google Scholar 

  • Wang X, Bendle NT, Mai F, Cotte J (2015) The journal of consumer research at 40: a historical analysis. J Consum Res 42(1):5–18

    Article  Google Scholar 

  • Weitzman ML (2013) A precautionary tale of uncertain tail fattening. Environ Resour Econ 55:159–173

    Article  Google Scholar 

  • Weyant J (2017) Some contributions of integrated assessment models of global climate change. Rev Environ Econ Policy 11(1):115–137

    Article  Google Scholar 

  • Zenghelis D, Stern N (2009) Principles for a global deal for limiting the risks from climate change. Environ Resour Econ 43:307–311

    Article  Google Scholar 

Download references

Acknowledgements

We acknowledge funding support from the Research Council of Norway, grant numbers 257630 and 302197.

Author information

Authors and Affiliations

  1. SNF - Centre for Applied Research at NHH, Helleveien 30, N-5045, Bergen, Norway

    Sturla F. Kvamsdal

  2. NHH Norwegian School of Economics, Helleveien 30, N-5045, Bergen, Norway

    Ivan Belik, Arnt Ove Hopland & Yuanhao Li

Authors
  1. Sturla F. Kvamsdal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ivan Belik
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Arnt Ove Hopland
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yuanhao Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sturla F. Kvamsdal.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (PDF 6651 KB)

Appendix

Appendix

See Figs. 10, 11, 12, 13

Fig. 10
figure 10

Correlations between topic weights, \(corr(\theta )\). Values along the diagonal are not plotted

Full size image
Fig. 11
figure 11

Mean topic weights over all articles in JEEM for four sub-periods; \({n}_{jy}\) report the number of articles in the given sub-period, shaded columns indicate mean topic weights above the uniform threshold (1/30)

Full size image
Fig. 12
figure 12

Difference in average per paper topic weights, 2000–2018. Error bars show two standard errors. Shaded columns indicate that the maximum average per paper topic weight occurred later than the minimum

Full size image
Fig. 13
figure 13

Topic visualization with topic 19 (‘Discounting, social institutions’) and token ‘game theory’ highlighted (see Sievert and Shirley 2014)

Full size image

.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kvamsdal, S.F., Belik, I., Hopland, A.O. et al. A Machine Learning Analysis of the Recent Environmental and Resource Economics Literature. Environ Resource Econ 79, 93–115 (2021). https://doi.org/10.1007/s10640-021-00554-0

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10640-021-00554-0

Keywords

Search

Navigation