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Income-based variation in Sustainable Development Goal interaction networks

Nature Sustainability volume 2, pages 242–247 (2019)Cite this article

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Abstract

The 17 United Nations Sustainable Development Goals (SDGs) are set to change the way we live, and aim to create, by 2030, a sustainable future balancing equitable prosperity within planetary boundaries. Human, economic and natural resources must be used in tandem to achieve the SDGs; therefore, acting to resolve one SDG can impair or improve our ability to meet others that may need these resources to be used in different ways. Trade-offs arising from these SDG interactions are a key hurdle for SDG implementation. We estimate the network of SDG interactions—the sustainome—using global time series of SDG indicators for countries with different income levels. We analyse the network architecture to determine the hurdles and opportunities to maximize SDG implementation through their interactions. The relative contributions of SDGs to global sustainable success differ by country income. They also differ depending on whether we consider SDG goals or targets. However, limiting climate change, reducing inequalities and responsible consumption are key hurdles to achieving 2030 goals across countries. Focusing on poverty alleviation and reducing inequalities will have compound positive effects on all SDGs.

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Fig. 1: SDG and target sustainome including all countries.
Fig. 2: Topological centrality of SDGs depending on the sum of their positive (positive strength) and negative (negative strength) associations.
Fig. 3: Sustainome, SDG contributions to reactivity, and fates of each SDG for high-income countries.
Fig. 4: Sustainome, SDG contributions to reactivity, and fates of each SDG for low-income countries.

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Code availability

Code is available at http://www.github.com/dlusseau/sdg/.

Data availability

All of the data used for these analyses are freely available from the World Bank SDG indicators via the website (https://datacatalog.worldbank.org/dataset/sustainable-development-goals) or the World Bank API.

References

  1. Liu, J. et al. Complexity of coupled human and natural systems. Science 317, 1513–1516 (2007).

    Article  CAS  Google Scholar 

  2. Nilsson, M., Griggs, D. & Visbeck, M. Policy: map the interactions between Sustainable Development Goals. Nature 534, 320–322 (2016).

    Article  Google Scholar 

  3. Transforming Our World: the 2030 Agenda for Sustainable Development (United Nations General Assembly, 2015).

  4. Le Blanc, D. Towards integration at last? The Sustainable Development Goals as a network of targets. Sustain. Dev. 23, 176–187 (2015).

    Article  Google Scholar 

  5. Bastos Lima, M. G., Kissinger, G., Visseren-Hamakers, I. J., Braña-Varela, J. & Gupta, A. The Sustainable Development Goals and REDD+: assessing institutional interactions and the pursuit of synergies. Int. Environ. Agreem. 17, 589–606 (2017).

    Article  Google Scholar 

  6. Pradhan, P., Costa, L., Rybski, D., Lucht, W. & Kropp, J. P. A systematic study of Sustainable Development Goal (SDG) interactions. Earths Future 5, 1169–1179 (2017).

    Article  Google Scholar 

  7. Chapin, F. S. III et al. Consequences of changing biodiversity. Nature 405, 234–242 (2000).

    Article  CAS  Google Scholar 

  8. Nilsson, M. et al. Mapping interactions between the Sustainable Development Goals: lessons learned and ways forward. Sustain. Sci. 13, 1489–1503 (2018).

    Article  Google Scholar 

  9. Weitz, N., Carlsen, H., Nilsson, M. & Skånberg, K. Towards systemic and contextual priority setting for implementing the 2030 Agenda. Sustain. Sci. 13, 531–548 (2018).

    Article  Google Scholar 

  10. Haines, A. et al. Short-lived climate pollutant mitigation and the Sustainable Development Goals. Nat. Clim. Change 7, 863–869 (2017).

    Article  Google Scholar 

  11. Costanza, R. et al. Development: time to leave GDP behind. Nature 505, 283–285 (2014).

    Article  Google Scholar 

  12. Marino, E. & Ribot, J. Special issue introduction: adding insult to injury: climate change and the inequities of climate intervention. Glob. Environ. Change 22, 323–328 (2012).

    Article  Google Scholar 

  13. Biermann, F., Pattberg, P., Van Asselt, H. & Zelli, F. The fragmentation of global governance architectures: a framework for analysis. Glob. Environ. Polit. 9, 14–40 (2009).

    Article  Google Scholar 

  14. Oberthür, S. & Gehring, T. Institutional Interaction in Global Environmental Governance: Synergy and Conflict Among International and EU Policies (MIT Press, Cambridge, 2006).

  15. Tosun, J. & Leininger, J. Governing the interlinkages between the Sustainable Development Goals: approaches to attain policy integration. Glob. Challenges 1, 1700036 (2017).

    Article  Google Scholar 

  16. Barabási, A.-L., Gulbahce, N. & Loscalzo, J. Network medicine: a network-based approach to human disease. Nat. Rev. Genet. 12, 56–68 (2011).

    Article  Google Scholar 

  17. Christakis, N. A. & Fowler, J. H. The spread of obesity in a large social network over 32 years. N. Engl. J. Med. 357, 370–379 (2007).

    Article  CAS  Google Scholar 

  18. Saavedra, S., Stouffer, D. B., Uzzi, B. & Bascompte, J. Strong contributors to network persistence are the most vulnerable to extinction. Nature 478, 233–235 (2011).

    Article  CAS  Google Scholar 

  19. Battiston, S., Mandel, A., Monasterolo, I., Schütze, F. & Visentin, G. A climate stress-test of the financial system. Nat. Clim. Change 7, 283–288 (2017).

    Article  Google Scholar 

  20. Bond, R. Complex networks: network healing after loss. Nat. Hum. Behav. 1, 0087 (2017).

    Article  Google Scholar 

  21. Newman, M. E. J. Networks (Oxford Press, Oxford, 2018).

  22. Islam, S. M. N., Munasinghe, M. & Clarke, M. Making long-term economic growth more sustainable: evaluating the costs and benefits. Ecol. Econ. 47, 149–166 (2003).

    Article  Google Scholar 

  23. DataBank: Sustainable Development Goals (World Bank, 2017); http://databank.worldbank.org/sdgs

  24. Bronski, J. & DeVille, L. Spectral theory for dynamics on graphs containing attractive and repulsive interactions. SIAM J. Appl. Math. 74, 83–105 (2014).

    Article  Google Scholar 

  25. Galor, O. & Zeira, J. Income distribution and macroeconomics. Rev. Econ. Stud. 60, 35–52 (1993).

    Article  Google Scholar 

  26. Pearce, D. W., Atkinson, G. D. & Dubourg, W. R. The economics of sustainable development. Annu. Rev. Energy Environ. 19, 457–474 (1994).

    Article  Google Scholar 

  27. How are the Income Group Thresholds Determined? (World Data Bank Help Desk, World Bank, accessed 13 March 2018); https://datahelpdesk.worldbank.org/knowledgebase/articles/378833-how-are-the-income-group-thresholds-determined

  28. Meinshausen, N. et al. Methods for causal inference from gene perturbation experiments and validation. Proc. Natl Acad. Sci. USA 113, 7361–7368 (2016).

    Article  CAS  Google Scholar 

  29. Neil Adger, W., Arnell, N. W. & Tompkins, E. L. Successful adaptation to climate change across scales. Glob. Environ. Change 15, 77–86 (2005).

    Article  Google Scholar 

  30. Vosti, S. A. & Reardon, T. A. Sustainability, Growth, and Poverty Alleviation: a Policy and Agroecological Perspective (Johns Hopkins Univ. Press, Baltimore and London, 1997).

  31. Head, B. W. & Alford, J. Wicked problems. Adm. Soc. 47, 711–739 (2015).

    Article  Google Scholar 

  32. EM-DAT: The International Disasters Database (Centre for Research on the Epidemiology of Disasters, accessed 13 March 2018); http://emdat.be/

  33. Pinheiro, J., Bates, D. M., DebRoy, S. S. & Sarkar, D. Nlme: Linear and Nonlinear Mixed Effects Models v.3.1-137 (2018); https://CRAN.R-project.org/package=nlme

  34. Viechtbauer, W. Conducting meta-analyses in R with the metafor package. J. Stat. Softw. 36, 1–48 (2010).

    Article  Google Scholar 

  35. Neubert, M. G. & Caswell, H. Alternatives to resilience for measuring the responses of ecological systems to perturbations. Ecology 78, 653–665 (1997).

    Article  Google Scholar 

  36. Lusseau, D., Whitehead, H. & Gero, S. Incorporating uncertainty into the study of animal social networks. Anim. Behav. 75, 1809–1815 (2008).

    Article  Google Scholar 

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Acknowledgements

We thank the United Nations Department of Public Information for making the 17 SDG icons available, the World Bank for curating and collating the SDG indicator data and making them easily accessible, and A. Douglas for fruitful analytical discussions.

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Author notes

  1. Francesca Mancini

    Present address: Centre for Ecology & Hydrology, Wallingford, OX10 8BB, England

Authors and Affiliations

  1. School of Biological Sciences, University of Aberdeen, Aberdeen, UK

    David Lusseau & Francesca Mancini

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  1. David Lusseau
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  2. Francesca Mancini
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Contributions

D.L. conceived the study. D.L. and F.M. designed the statistical models. D.L. carried out the analyses. D.L. and F.M. wrote the manuscript.

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Correspondence to David Lusseau.

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The authors declare no competing interests.

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Supplementary Information

Supplementary Table 1, Supplementary Figures 1–10

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Lusseau, D., Mancini, F. Income-based variation in Sustainable Development Goal interaction networks. Nat Sustain 2, 242–247 (2019). https://doi.org/10.1038/s41893-019-0231-4

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