Greater gains for Australia by tackling all SDGs but the last steps will be the most challenging

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Abstract

The Sustainable Development Goals (SDGs) combine complex interlinkages, future uncertainty and transformational change. Recent studies highlight that trade-offs between SDG targets may undermine achievement of the goals. Significant gaps remain in scenario frameworks and modelling capabilities. We develop a novel approach nesting national SDG scenario modelling within the global Shared Socioeconomic Pathways, selecting Australia as a use case. The integrated SDG–Australia model is used to project four alternative scenarios that adopt different development approaches. Although we find that Australia is off-track to achieve the SDGs by 2030, considerable progress is possible by altering Australia’s development trajectory. A ‘Sustainability Transition’ scenario comprising a coherent set of policies and investments delivers rapid and balanced progress of 70% towards SDG targets by 2030, well ahead of the business-as-usual scenario (40%). A focus on economic growth, social inclusion or green economy in isolation foregoes opportunities for greater gains. However, future uncertainty and cascading risks could undermine progress, and closing the gap to 100% SDG achievement will be very challenging. This will require a shift from ‘transition’ to ‘transformation’.

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Fig. 1: The nested approach used to develop national scenarios, assumptions and settings.
Fig. 2: Aggregate performance on the SDGs across all Australian scenarios.
Fig. 3: Performance of each scenario on economic, social and environmental SDG targets.
Fig. 4: The proportion of SDG targets assessed as ‘achieved’ under different scenarios.
Fig. 5: Sensitivity analysis of SDG performance results: boxplots of frequency distributions from Monte Carlo simulations.

Data availability

The datasets collected and analysed during the current study are available from the corresponding author on reasonable request.

Code availability

The iSDG simulation model can be made available from the Millennium Institute on reasonable request.

References

  1. 1.

    Spaiser, V., Ranganathan, S., Swain, R. B. & Sumpter, D. J. The sustainable development oxymoron: quantifying and modelling the incompatibility of Sustainable Development Goals. Int. J. Sustain. Dev. World Ecol. 24, 457–470 (2017).

  2. 2.

    Spangenberg, J. H. Hot air or comprehensive progress? A critical assessment of the SDGs. Sustain. Dev. 25, 311–321 (2017).

  3. 3.

    Hall, N. et al. Achieving the UN Sustainable Development Goals for Water and Beyond (Global Change Institute, Univ. of Queensland, 2016).

  4. 4.

    Zhang, Q., Prouty, C., Zimmerman, J. B. & Mihelcic, J. R. More than Target 6.3: a systems approach to rethinking sustainable development goals in a resource-scarce world. Engineering 2, 481–489 (2016).

  5. 5.

    Allen, C., Metternicht, G. & Wiedmann, T. Prioritising SDG targets: assessing baselines, gaps and interlinkages. Sustain. Sci. 14, 421–438 (2019).

  6. 6.

    Zhou, X., Moinuddin, M. & Xu, M. Sustainable Development Goals Interlinkages and Network Analysis: A Practical Tool for SDG Integration and Policy Coherence (IGES, 2017).

  7. 7.

    A Guide to SDG Interactions: From Science to Implementation (ICS, 2017).

  8. 8.

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

  9. 9.

    Lusseau, D. & Mancini, F. Income-based variation in Sustainable Development Goal interaction networks. Nat. Sustain. 2, 242–247 (2019).

  10. 10.

    Mcgowan, P. J., Stewart, G. B., Long, G. & Grainger, M. J. An imperfect vision of indivisibility in the Sustainable Development Goals. Nat. Sustain. 2, 43–45 (2019).

  11. 11.

    Weitz, N., Carlsen, H., Nilsson, M. & Skånberg, K. Towards systemic and contextual priority setting for implementing the 2030 Agenda. Sustain. Sci. 13, 531–548 (2017).

  12. 12.

    Campagnolo, L. et al. The ex-ante evaluation of achieving Sustainable Development Goals. Soc. Indic. Res. 136, 73–116 (2018).

  13. 13.

    Gao, L. & Bryan, B. A. Finding pathways to national-scale land-sector sustainability. Nature 544, 217–222 (2017).

  14. 14.

    Moyer, J. D. & Bohl, D. K. Alternative pathways to human development: assessing trade-offs and synergies in achieving the Sustainable Development Goals. Futures 105, 199–210 (2019).

  15. 15.

    Pedercini, M., Zuellich, G., Dianati, K. & Arquitt, S. Toward achieving Sustainable Development Goals in Ivory Coast: simulating pathways to sustainable development. Sustain. Devel. https://doi.org/10.1002/sd.1721 (2018).

  16. 16.

    Costanza, R. et al. Scenarios for Australia in 2050: a synthesis and proposed survey. J. Futures Stud. 19, 49–76 (2015).

  17. 17.

    O’Connell, D. et al. Navigating Sustainability: Measurement, Evaluation and Action (CSIRO, 2013).

  18. 18.

    Allen, C., Metternicht, G. & Wiedmann, T. National pathways to the Sustainable Development Goals (SDGs): a comparative review of scenario modelling tools. Environ. Sci. Policy 66, 199–207 (2016).

  19. 19.

    Allen, C., Metternicht, G. & Wiedmann, T. An iterative framework for national scenario modelling for the Sustainable Development Goals (SDGs). Sustain. Dev. 25, 372–385 (2017).

  20. 20.

    Bauer, N. et al. Shared socio-economic pathways of the energy sector—quantifying the narratives. Glob. Environ. Change 42, 316–330 (2017).

  21. 21.

    O’Neill, B. C. et al. The roads ahead: narratives for shared socioeconomic pathways describing world futures in the 21st century. Glob. Environ. Change 42, 169–180 (2017).

  22. 22.

    O’Neill, B. C. et al. A new scenario framework for climate change research: the concept of shared socioeconomic pathways. Clim. Change 122, 387–400 (2014).

  23. 23.

    Riahi, K. et al. The shared socioeconomic pathways and their energy, land use, and greenhouse gas emissions implications: an overview. Glob. Environ. Change 42, 153–168 (2017).

  24. 24.

    Arnell, N. W. & Lloyd-Hughes, B. The global-scale impacts of climate change on water resources and flooding under new climate and socio-economic scenarios. Clim. Change 122, 127–140 (2014).

  25. 25.

    Hasegawa, T., Fujimori, S., Takahashi, K. & Masui, T. Scenarios for the risk of hunger in the twenty-first century using Shared Socioeconomic Pathways. Environ. Res. Lett. 10, 1 (2015).

  26. 26.

    Frame, B., Lawrence, J., Ausseil, A.-G., Reisinger, A. & Daigneault, A. Adapting global shared socio-economic pathways for national and local scenarios. Clim. Risk Manag. 21, 39–51 (2018).

  27. 27.

    König, M., Loibl, W., Haas, W. & Kranzl, L. in Economic Evaluation of Climate Change Impacts (eds Steininger, K. et al.) 75–99 (Springer, 2015).

  28. 28.

    Steininger, K. W., Bednar-Friedl, B., Formayer, H. & König, M. Consistent economic cross-sectoral climate change impact scenario analysis: method and application to Austria. Clim. Serv. 1, 39–52 (2016).

  29. 29.

    Zimm, C., Sperling, F. & Busch, S. Identifying sustainability and knowledge gaps in socio-economic pathways vis-à-vis the Sustainable Development Goals. Economies 6, 20 (2018).

  30. 30.

    Allen, C., Reid, M., Thwaites, J., Glover, R. & Kestin, T. Assessing national progress and priorities for the Sustainable Development Goals (SDGs): experience from Australia. Sustain. Sci. https://doi.org/10.1007/s11625-019-00711-x (2019).

  31. 31.

    Hatfield-Dodds, S. et al. Australia is ‘free to choose’ economic growth and falling environmental pressures. Nature 527, 49–53 (2015).

  32. 32.

    United Nations Sustainable Development Goals: Guidelines for the Use of the SDG Logo (United Nations, 2016); https://www.un.org/sustainabledevelopment/news/communications-material/

  33. 33.

    Bliemel, F. Theil’s forecast accuracy coefficient: a clarification. J. Mark. Res. 10, 444–446.

  34. 34.

    Theil, H. Applied Economic Forecasting (North-Holland, 1966).

  35. 35.

    Spangenberg, J. H. The growth discourse, growth policy and sustainable development: two thought experiments. Int. J. Technol. Pol. Manag. 18, 561–566 (2010).

  36. 36.

    Kwakkel, J. H., Walker, W. E. & Marchau, V. A. Classifying and communicating uncertainties in model-based policy analysis. Int. J. Technol. Manag. 10, 299–315 (2010).

  37. 37.

    Maier, H. R. et al. An uncertain future, deep uncertainty, scenarios, robustness and adaptation: how do they fit together? Environ. Model. Softw. 81, 154–164 (2016).

  38. 38.

    National Resilience Taskforce Profiling Australia’s Vulnerability: The Interconnected Causes and Cascading Effects of Systemic Disaster Risk (Australian Government Department of Home Affairs, 2018).

  39. 39.

    Díaz, S. et al. Summary for Policymakers of the Global Assessment Report on Biodiversity and Ecosystem Services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES Secretariat, 2019).

  40. 40.

    IPCC Global Warming of 1.5°C (eds Masson-Delmotte, V. et al.) (Cambridge Univ. Press, 2018).

  41. 41.

    O’Connell, D. et al. Approach, Methods and Results for Co-Producing a Systems Understanding of Disaster (CSIRO, 2018).

  42. 42.

    The Global Risks Report 2019 (WEF, 2019); http://www3.weforum.org/docs/WEF_Global_Risks_Report_2019.pdf

  43. 43.

    Walker, W. E., Lempert, R. J. & Kwakkel, J. H. Deep uncertainty. Encycl. Oper. Res. Manage. Sci. https://doi.org/10.1007/978-1-4419-1153-7_1140 (2016).

  44. 44.

    Weaver, C. P. et al. Improving the contribution of climate model information to decision making: the value and demands of robust decision frameworks. Wiley Interdiscip. Rev. Clim. Change 4, 39–60 (2013).

  45. 45.

    Gao, L. et al. Robust global sensitivity analysis under deep uncertainty via scenario analysis. Environ. Model. Softw. 76, 154–166 (2016).

  46. 46.

    Ralston, B. & Wilson, I. The Scenario Planning Handbook (Thomson, 2006).

  47. 47.

    Swanson, D. & Bhadwal, S. Creating Adaptive Policies—A Guide for Policy-Making in an Uncertain World (IISD, 2009).

  48. 48.

    Van Vuuren, D. P. et al. The representative concentration pathways: an overview. Clim. Change 109, 5–31 (2011).

  49. 49.

    Van Vuuren, D. P. et al. A new scenario framework for climate change research: scenario matrix architecture. Clim. Change 122, 373–386 (2014).

  50. 50.

    Hatfield-Dodds, S. et al. Australian National Outlook 2015: Economic Activity, Resource Use, Environmental Performance and Living Standards, 1970–2050 (CSIRO, 2015).

  51. 51.

    Raupach, M. R., Mcmichael, T., Finnigan, J. J., Manderson, L. & Walker, B. H. Negotiating Our Futures: Living Scenarios for Australia to 2050 (AAS, 2012).

  52. 52.

    Chambers, I. et al. A public opinion survey of four future scenarios for Australia in 2050. Futures 107, 119–132 (2018).

  53. 53.

    Commission on Growth and Development The Growth Report: Strategies for Sustained Growth and Inclusive Development (World Bank, 2008).

  54. 54.

    Gupta, J., Pouw, N. R. & Ros-Tonen, M. A. Towards an elaborated theory of inclusive development. Eur. J. Dev. Res. 27, 541–559 (2015).

  55. 55.

    Towards a Green Economy: Pathways to Sustainable Development and Poverty Eradication (UNEP, 2011).

  56. 56.

    United Nations General Assembly United Nations Millennium Declaration A/RES/55/2 (United Nations, 2000).

  57. 57.

    Collste, D., Pedercini, M. & Cornell, S. E. Policy coherence to achieve the SDGs: using integrated simulation models to assess effective policies. Sustain. Sci. 12, 921–931 (2017).

  58. 58.

    Vensim DSS v.7.3 (Ventana Systems Inc., 2018); https://vensim.com/vensim-software/

  59. 59.

    Stella Architect (ISEE Systems, 2018); https://www.iseesystems.com

  60. 60.

    iSDG Model Documentation (Millennium Institute, 2017); https://www.millennium-institute.org/documentation

  61. 61.

    Bassi, A. M. A context-inclusive approach to support energy policy formulation and evaluation. Reg. Environ. Change 11, 285–295 (2011).

  62. 62.

    Bassi, A. M., Tan, Z. & Goss, S. An integrated assessment of investments towards global water sustainability. Water 2, 726–741 (2010).

  63. 63.

    Kopainsky, B., Pedercini, M., Davidsen, P. I. & Alessi, S. M. A blend of planning and learning: simplifying a simulation model of national development. Simul. Gaming 41, 641–662 (2010).

  64. 64.

    Pedercini, M. in International Conference on Software Engineering and Formal Methods Vol. 7041 (eds Barthe G., Pardo A. & Schneider G.) 447–463 (Springer, 2011).

  65. 65.

    Pedercini, M., Kleemann, H., Dlamini, N., Dlamini, V. & Kopainsky, B. Integrated simulation for national development planning. Kybernetes 48, 208–223 (2019).

  66. 66.

    Partnership for Action on Green Economy The Integrated Green Economy Modelling Framework (UNEP, 2017).

  67. 67.

    Green Economy Assessment Report: Kenya (UNEP, 2014).

  68. 68.

    Green Economy Assessment Study: Burkina Faso (UNEP, 2014).

  69. 69.

    Green Economy Assessment Study: Senegal (UNEP, 2014).

  70. 70.

    Sterman, J. Business Dynamics: Systems Thinking and Modeling for a Complex World (Irwin McGraw-Hill, 2000).

  71. 71.

    Saltelli, A. et al. Global Sensitivity Analysis: The Primer (John Wiley & Sons, 2008).

  72. 72.

    Elsawah, S. et al. An overview of the system dynamics process for integrated modelling of socio-ecological systems: lessons on good modelling practice from five case studies. Environ. Model. Softw. 93, 127–145 (2017).

  73. 73.

    Mirchi, A., Madani, K., Watkins, D. & Ahmad, S. Synthesis of system dynamics tools for holistic conceptualization of water resources problems. Water Resour. Manag. 26, 2421–2442 (2012).

  74. 74.

    Moon, Y. B. Simulation modelling for sustainability: a review of the literature. Int. J. Sustain. Eng. 10, 2–19 (2017).

  75. 75.

    Barlas, Y. Formal aspects of model validity and validation in system dynamics. Syst. Dyn. Rev. 12, 183–210 (1996).

  76. 76.

    Forrester, J. W. & Senge, P. M. Tests for building confidence in system dynamics models. TIMS Stud. Manag. Sci. 14, 209–228 (1980).

  77. 77.

    Qudrat-Ullah, H. On the validation of system dynamics type simulation models. Telecommun. Syst. 51, 159–166 (2012).

  78. 78.

    Homer, J. B. Partial‐model testing as a validation tool for system dynamics (1983). Syst. Dyn. Rev. 28, 281–294 (2012).

  79. 79.

    Bennett, N. D. et al. Characterising performance of environmental models. Environ. Model. Softw. 40, 1–20 (2013).

  80. 80.

    Pianosi, F. et al. Sensitivity analysis of environmental models: a systematic review with practical workflow. Environ. Model. Softw. 79, 214–232 (2016).

  81. 81.

    R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2013).

  82. 82.

    Migrant Intake to Australia—Productivity Commission Inquiry Report (Australian Government Productivity Commission, 2016).

  83. 83.

    Climateworks Australia, ANU, CSIRO & CoPS Pathways to Deep Decarbonisation: How Australia can Prosper in a Low Carbon World (ClimateWorks Australia, 2014).

  84. 84.

    CSIRO Australian National Outlook 2019: Securing our Nation’s Future Prosperity (CSIRO, 2019); https://www.csiro.au/en/Showcase/ANO

  85. 85.

    Mcdonald, P. F. & Temple, J. Immigration, Labour Supply and Per Capita Gross Domestic Product: Australia 2010–2050 (Australian Government Department of Immigration and Citizenship, 2010) .

  86. 86.

    Schandl, H. et al. Decoupling global environmental pressure and economic growth: scenarios for energy use, materials use and carbon emissions. J. Clean. Prod. 132, 45–56 (2016).

  87. 87.

    Sobels, J. et al. Research into the Long-Term Physical Implications of Net Overseas Migration (National Institute of Labour Studies, Flinders University School of the Environment, CSIRO Sustainable Ecosystems, 2010).

  88. 88.

    Syed, A., Melanie, J., Thorpe, S. & Penney, K. Australian Energy Projections to 2029–30 ABARE research report 10.02 (ABARES, 2010).

  89. 89.

    Turner, G. M., Elliston, B. & Diesendorf, M. Impacts on the biophysical economy and environment of a transition to 100% renewable electricity in Australia. Energy Policy 54, 288–299 (2013).

  90. 90.

    Sachs, J., Schmidt-Traub, G., Kroll, C., Lafortune, G. & Fuller, G. SDG Index and Dashboards Report 2018 (Bertelsmann Stiftung, Sustainable Development Solutions Network, 2018).

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Acknowledgements

The authors thank J. West, H. Schandl and M. Stafford-Smith at the CSIRO for their informal advice, as well as the provision of data to support this study. We acknowledge the National Sustainable Development Council for their previous work in assessing Australia’s progress on the SDGs, which provided an important baseline for this study.

Author information

C.A. led the research and undertook data collection, model calibration for iSDG–Australia, model adjustments, scenario development and simulations. M.P. developed the iSDG base model and provided advice and guidance data, model calibration and adjustment. G.M. and T.W. provided overall study supervision, advice and guidance regarding research framing, scenario development, methods and data sources. C.A. wrote the paper with inputs from G.M., T.W. and M.P.

Correspondence to Cameron Allen.

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Allen, C., Metternicht, G., Wiedmann, T. et al. Greater gains for Australia by tackling all SDGs but the last steps will be the most challenging. Nat Sustain 2, 1041–1050 (2019) doi:10.1038/s41893-019-0409-9

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