Skip to main content

A topic network analysis of the system turn in the environmental sciences


The concept of Earth system science denotes a shift in the scientific discourse from disciplinary accounts of isolated components of the global environment towards the holistic and interdisciplinary treatment of their complex, functional interactions. We measure to what extent the environmental scientific literature of the past three decades reflects this system turn. Our initial dataset consists of 133,670 articles published in 95 relevant journals since 1990. We apply a combination of topic modelling and network analysis. Correlated Topic Models identify latent themes (“topics”) in the scientific discourse as well as intertopic correlations. This allows the generation of topic networks and thus the application of network-analytic techniques. We generate and analyze 2 topic networks. The first network focuses on climate linkages in a subset of our corpus consisting only of environmental journals without a climate-specific orientation: as the climate system is constitutional for the notion of Earth system science, a system turn should reflect itself in strong linkages between climate- and non-climate environmental topics. The second network, based on the full dataset, applies community detection to identify broader topical clusters. Here, we expect the system turn to manifest itself in communities with high topical diversity regarding the components of the global environment as well as types of human-nature interactions, rather than reflecting the boundaries between broader research fields. Our results show that climate topics are comparatively weakly connected and less integrated into broader thematic packages than other topics; that linkages frequently reflect conceptual debates rather than functional interactions between substantive environmental components; and that the scientific discourse splits into 4 broader domains, two of those being topically homogeneous and the other two comprising only marginally diverse topics. We conclude that the concept of Earth system science is primarily aspirational in nature rather than reflecting an empirical shift in the structure of the scientific discourse.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4


  • Barrat, A., Barthelemy, M., Pastor-Satorras, R., & Vespignani, A. (2004). The architecture of complex weighted networks. Proceedings of the National Academy of Sciences, 101(11), 3747–3752.

    Article  Google Scholar 

  • Beyer, A., Mackay, D., Matthies, M., Wania, F., & Webster, E. (2000). Assessing long-range transport potential of persistent organic pollutants. Environmental Science and Technology, 34(4), 699–703.

    Article  Google Scholar 

  • Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3(Jan), 993–1022.

  • Blei, D. M. and Lafferty, J. D. (2006). Correlated topic models. In: Weiss, Y., Schölkopf, B., and Platt, J. (eds.) Advances in neural information processing systems 18.

  • Chang, J., Gerrish, S., Wang, C., Boyd-Graber, J. L., & Blei, D. M. (2009). Reading tea leaves: How humans interpret topic models. In: Bengio, Y., Schuurmans, D., Lafferty, J. D., Williams, C. K. I., & Culotta, A. (eds.), Advances in neural information processing systems 22.

  • Dudley, N., & Alexander, S. (2017). Agriculture and biodiversity: A review. Biodiversity, 18(2–3), 45–49.

    Article  Google Scholar 

  • Friis, C., Nielsen, J. Ø., Otero, I., Haberl, H., Niewöhner, J., & Hostert, P. (2016). From teleconnection to telecoupling: Taking stock of an emerging framework in land system science. Journal of Land Use Science, 11(2), 131–153.

    Article  Google Scholar 

  • Griffiths, T. L., & Steyvers, M. (2004). Finding scientific topics. Proceedings of the National Academy of Sciences, 101(s1), 5228–5235.

    Article  Google Scholar 

  • Gruber, N., & Galloway, J. N. (2008). An Earth-system perspective of the global nitrogen cycle. Nature, 451(7176), 293–296.

    Article  Google Scholar 

  • Holling, C. S. (1998). Two cultures of ecology. Conservation Ecology, 2(2), 4.

    Article  Google Scholar 

  • IPCC (2014) Climate change (2014). Synthesis report. Geneva: Intergovernmental Panel on Climate Change.

  • Johnson, J. M. F., Franzluebbers, A. J., Weyers, S. L., & Reicosky, D. C. (2007). Agricultural opportunities to mitigate greenhouse gas emissions. Environmental pollution, 150(1), 107–124.

    Article  Google Scholar 

  • Kuhn, T. (1970). The structure of scientific revolutions. London / Chicago: Chicago University Press.

    Google Scholar 

  • Lawton, J. (2001). Earth system science. Science, 292(5524), 1965.

    Article  Google Scholar 

  • Lenton, T. M., Rockström, J., Gaffney, O., Rahmstorf, S., Richardson, K., Steffen, W., & Schellnhuber, H. J. (2019). Climate tipping points—too risky to bet against. Nature, 575, 592–595.

    Article  Google Scholar 

  • Lövbrand, E., Stripple, J., & Wiman, B. (2009). Earth system governmentality: Reflections on science in the anthropocene. Global Environmental Change, 19(1), 7–13.

    Article  Google Scholar 

  • Opsahl, T., Agneessens, F., & Skvoretz, J. (2010). Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks, 32(3), 245–251.

    Article  Google Scholar 

  • Reid, W. V., Chen, D., Goldfarb, L., Hackmann, H., Lee, Y. T., Mokhele, K., et al. (2010). Earth system science for global sustainability: Grand challenges. Science, 330(6006), 916–917.

    Article  Google Scholar 

  • Roberts, M. E., Stewart, B. M., & Tingley, D. (2014). stm: R package for structural topic models. Journal of Statistical Software, 10(2), 1–40.

    Google Scholar 

  • Schellnhuber, H. J. (1999). ‘Earth system’ analysis and the second Copernican revolution. Nature, 402(6761), C19–C23.

    Article  Google Scholar 

  • Shiva, V. (2016). The violence of the green revolution: Third world agriculture, ecology, and politics. University Press of Kentucky.

  • Steffen, W., Sanderson, R. A., Tyson, P. D., Jäger, J., Matson, P. A., Moore III, B., Oldfield, F., Richardson, K., Schellnhuber, H.-J., Turner, B.L., & Wasson, R. J. (2006). Global change and the earth system: a planet under pressure. Springer Science and Business Media.

  • Turner, D. P. (2018). The green marble. Earth system science and global sustainability. New York: Columbia University Press.

    Book  Google Scholar 

  • Yang, Z., Algesheimer, R., & Tessone, C. J. (2016). A comparative analysis of community detection algorithms on artificial networks. Scientific Reports, 6, 30750.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Florian Rabitz.



Part 1: journal selection

We start with an initial list of the 100 top-ranked journals from Web of Science (WOS), subject area “environmental science”, subject category “management, monitoring, policy and law”, for the year 2018. At the time of writing, this is the most-recent ranking. Table 1 lists those journals and indicates which of them are included in our two respective models. For 5 of those journals, no abstract data is available through WOS.

Table 1 Journals in data set.

Part 2: topic labels and classification

Topic labels describe and summarize the overall semantic content of a topic, based on the respective topic-term associations. We label topics according to substantive knowledge, with a preference for shorter over longer labels. For model 1, we subsequently classify each of our topics as “climate” or “non-climate”, again based on associated top terms. We consider a topic to be climate-related if its highest-probability terms or most-frequent exclusive (FREX) terms include expressions referring to immediate causes of climate change (i.e. “greenhouse” or “methane”), its consequences (i.e. “sealevels”) or response measures (i.e. “adaptation”, “mitigation” or “renewables”). In addition, we also classify the topics in model 1 by type, distinguishing whether they primarily deal with substantive components of the biosphere, abstract objects or methodological issues (see Table 2).

Table 2 topics, labels, classifications and top-probability and FREX terms for model 1. c = conceptual, s = substantive, m = methodological, o = other.
Table 3 topics, labels, top-probability and FREX terms for model 2. Table 3 Below presents top-probability and FREX terms, as well as labels, for model 2

Part 3: community detection

The overall results of community detection hold up independent of the chosen algorithm. Table 4 shows the outputs from the Cluster Fast Greedy and Walktrap methods, in addition to the Louvain method used in the text.

Table 4 Community structure

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Rabitz, F., Olteanu, A., Jurkevičienė, J. et al. A topic network analysis of the system turn in the environmental sciences. Scientometrics 126, 2107–2140 (2021).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:


  • Earth system science
  • Topic models
  • Network analysis
  • Environmental science