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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Code is available at http://www.github.com/dlusseau/sdg/.
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.
Liu, J. et al. Complexity of coupled human and natural systems. Science 317, 1513–1516 (2007).
Nilsson, M., Griggs, D. & Visbeck, M. Policy: map the interactions between Sustainable Development Goals. Nature 534, 320–322 (2016).
Transforming Our World: the 2030 Agenda for Sustainable Development (United Nations General Assembly, 2015).
Le Blanc, D. Towards integration at last? The Sustainable Development Goals as a network of targets. Sustain. Dev. 23, 176–187 (2015).
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).
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).
Chapin, F. S. III et al. Consequences of changing biodiversity. Nature 405, 234–242 (2000).
Nilsson, M. et al. Mapping interactions between the Sustainable Development Goals: lessons learned and ways forward. Sustain. Sci. 13, 1489–1503 (2018).
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).
Haines, A. et al. Short-lived climate pollutant mitigation and the Sustainable Development Goals. Nat. Clim. Change 7, 863–869 (2017).
Costanza, R. et al. Development: time to leave GDP behind. Nature 505, 283–285 (2014).
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).
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).
Oberthür, S. & Gehring, T. Institutional Interaction in Global Environmental Governance: Synergy and Conflict Among International and EU Policies (MIT Press, Cambridge, 2006).
Tosun, J. & Leininger, J. Governing the interlinkages between the Sustainable Development Goals: approaches to attain policy integration. Glob. Challenges 1, 1700036 (2017).
Barabási, A.-L., Gulbahce, N. & Loscalzo, J. Network medicine: a network-based approach to human disease. Nat. Rev. Genet. 12, 56–68 (2011).
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).
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).
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).
Bond, R. Complex networks: network healing after loss. Nat. Hum. Behav. 1, 0087 (2017).
Newman, M. E. J. Networks (Oxford Press, Oxford, 2018).
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).
DataBank: Sustainable Development Goals (World Bank, 2017); http://databank.worldbank.org/sdgs
Bronski, J. & DeVille, L. Spectral theory for dynamics on graphs containing attractive and repulsive interactions. SIAM J. Appl. Math. 74, 83–105 (2014).
Galor, O. & Zeira, J. Income distribution and macroeconomics. Rev. Econ. Stud. 60, 35–52 (1993).
Pearce, D. W., Atkinson, G. D. & Dubourg, W. R. The economics of sustainable development. Annu. Rev. Energy Environ. 19, 457–474 (1994).
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
Meinshausen, N. et al. Methods for causal inference from gene perturbation experiments and validation. Proc. Natl Acad. Sci. USA 113, 7361–7368 (2016).
Neil Adger, W., Arnell, N. W. & Tompkins, E. L. Successful adaptation to climate change across scales. Glob. Environ. Change 15, 77–86 (2005).
Vosti, S. A. & Reardon, T. A. Sustainability, Growth, and Poverty Alleviation: a Policy and Agroecological Perspective (Johns Hopkins Univ. Press, Baltimore and London, 1997).
Head, B. W. & Alford, J. Wicked problems. Adm. Soc. 47, 711–739 (2015).
EM-DAT: The International Disasters Database (Centre for Research on the Epidemiology of Disasters, accessed 13 March 2018); http://emdat.be/
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
Viechtbauer, W. Conducting meta-analyses in R with the metafor package. J. Stat. Softw. 36, 1–48 (2010).
Neubert, M. G. & Caswell, H. Alternatives to resilience for measuring the responses of ecological systems to perturbations. Ecology 78, 653–665 (1997).
Lusseau, D., Whitehead, H. & Gero, S. Incorporating uncertainty into the study of animal social networks. Anim. Behav. 75, 1809–1815 (2008).
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.
The authors declare no competing interests.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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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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