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The economic interaction between climate change mitigation, climate migration and poverty

Nature Climate Change volume 10, pages 518–525 (2020)Cite this article

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Abstract

Mitigation of anthropogenic climate change takes place against the backdrop of poor countries being most affected by climate change impacts; climate-induced migration is expected to increase in the future. However, the interaction between mitigation, climate migration and poverty has not been investigated explicitly. Here, we represent simultaneous poverty- and climate-induced migration in a laboratory setting, within the collective-risk social dilemma that arises from attempts to avert dangerous climate change. The relatively rich participants try to prevent migration by the relatively poor but in the long run these attempts are unsuccessful because of free-riding among the rich. The rich are willing to increase their effort at averting dangerous climate change when the poor are hit by a climate extreme event exacerbating their poverty. Conversely, the poor are willing to compensate some weaker effort by the rich, as long as the effort by the rich lies above a threshold emerging within the experiment.

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Fig. 1: Migration between rich and poor countries.
Fig. 2: The course of a climate event.
Fig. 3: Investment in climate mitigation and payoff.
Fig. 4: Investment in climate mitigation during climate event.
Fig. 5: Net effect of climate event during poverty migration.
Fig. 6: Relation between poor and rich player contributions.

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Data availability

The dataset generated and analysed during the current study is available at https://pure.mpg.de/pubman/faces/ViewItemFullPage.jsp?itemId=item_3217143 and http://hdl.handle.net/21.11116/0000-0006-5492-6. Source Data for Figs. 1–6 and Extended Data Figs. 1–4 are provided with the paper.

Code availability

The data analysis code is available from the corresponding author on request.

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Acknowledgements

We thank the students from the universities of Hamburg and Kiel for their participation. We also thank H. Brendelberger and S. Dobler for logistic support. M.M. acknowledges discussion at the NIMBioS sustainability conference at Knoxville. This study was supported by the Max Planck Society for the Advancement of Science.

Author information

Authors and Affiliations

  1. Department ‘The Ocean in the Earth System’, Max Planck Institute for Meteorology, Hamburg, Germany

    Jochem Marotzke

  2. Department of Evolutionary Ecology, Max-Planck Institute of Evolutionary Biology, Plön, Germany

    Dirk Semmann & Manfred Milinski

  3. Johann-Friedrich-Blumenbach Institut für Zoologie und Anthropologie, Georg-August-Universität Göttingen, Göttingen, Germany

    Dirk Semmann

Authors
  1. Jochem Marotzke
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  2. Dirk Semmann
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  3. Manfred Milinski
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Contributions

M.M. conceived the study. M.M. and J.M. designed the study. D.S. wrote the z-Tree program. D.S. and M.M. performed the research. M.M. analysed the data. M.M. and J.M. wrote the paper and all authors revised the manuscript.

Corresponding authors

Correspondence to Jochem Marotzke or Manfred Milinski.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Climate Change thanks Reuben Kline, Mathew Hauer and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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

Extended data

Extended Data Fig. 1 Harvest per inhabitant per round (€).

in the rich country and the poor country dependent on the number of inhabitants. (a) with low overall harvest in T0 and T1, (b) with high overall harvest in T2.

Source data

Extended Data Fig. 2 Number of complete blockades.

Mean + s.e.m. per group in T0, T1 and T2. P after Mann–Whitney U-test, n1 = 14, n2 = 14, z = −2.955. Effective blockings (mean ± s.e.m.) per group: 2.5 ± 0.7 in T0, 1.8 ± 0.2 in T1, and 7.1 ± 1.7 in T2, respectively.

Source data

Extended Data Fig. 3 Risk of climate event.

Group size per group (mean ±s.e.m.) in the last 5 rounds per round in the poor country in T0, in T1 and T2 dependent on the risk of a climate event to occur of either 0%, n = 13, 10%, n = 7, or 20%, n = 7, per round. There are fewer inhabitants in the poor country during the last five rounds with increasing risk of climate events (n = 3 treatments, P = 0.005, h = 10.534, Kruskal–Wallis test).

Source data

Extended Data Fig. 4 Absolute Spearman rank correlation coefficients.

rho, mean + s.e.m., between contributions of one class of players to contributions of the other class of players in previous round over 19 rounds per group. Poor player reacts to rich player’s contribution in previous round, rich player reacts to poor player’s contribution in previous round; a. T0, n = 13 groups of 10 players each, b. T1, n = 14 groups of 10 players each, c. T2, n = 14 groups of 10 players each; d. T0, 7 groups of 10 players each, where rich players contributed at least €300 total per group, e. T1, 8 groups of 10 players each, where rich players contributed at least €300 total per group. P calculated from Fisher combination test41, a. combining P values from each of 13 (T0), b, c. of 14 (T1, T2), d. of 7 (T0), e. of 8 (T1) independent groups of 10 players each. For each group the Spearman correlation coefficient rho was calculated from 19 rounds, providing a single p-value.

Source data

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Marotzke, J., Semmann, D. & Milinski, M. The economic interaction between climate change mitigation, climate migration and poverty. Nat. Clim. Chang. 10, 518–525 (2020). https://doi.org/10.1038/s41558-020-0783-3

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