Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Methodological PLS-PM Framework for SDGs System

  • 55 Accesses


Sustainability is the biggest challenge of our generation, because civilization has reached a point where natural resources are in rapid decline. It’s a complex multidimensional phenomenon, which was studied for couple decades already. Nowadays different social concepts, such as sustainability, but also quality of life, satisfaction, are difficult and complex to define. The main problem for researchers is to find appropriate tools to obtain a composite indicator able to synthesize and represent these phenomena. The work focuses on building a system of composite indicators of Sustainability through to Structural Equation Modeling, specifically with the use of Partial Least Squares-Path Modeling. In recent years many advances have been developed, in the context of these models to solve some problems related to the role that the composite indicators play within that system; in particular on the aspects linked to the high level of abstraction, when a composite indicator is manifold, lacks its own manifest variables and is described by various underlying blocks. The aim of this paper is to demonstrate how these recent developments in Partial Least Square-Path Modeling could help you to build a SDGs system and to provide a better measure of this complex social phenomenon.

This is a preview of subscription content, log in to check access.

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


  1. 1.

    The concept of sustainable development is quite different from that of sustainability in that the word “development” clearly points to the idea of change, of directional and progressive change (Gallopín 2003).

  2. 2.

    The MDGs were derived from the United Nations Millennium Declaration, adopted by 189 nations in 2000. Most of the goals and targets were set to be achieved by 2015 on the basis of the global situation during the 1990s. The baseline for the assessment of progress is therefore 1990 for most of the MDGs targets.

  3. 3.

    One of the oldest and most famous formative composite indices is the HDI by United Nations Development Programme (United Nations Development Programme 2010). It s a composite measure of human development that include three theoretical dimensions (Health, Education and Income) (Mazziotta and Pareto 2019).

  4. 4.

    For a detailed description of the individual Goals, refer to the site “Sustainable Development Goals” (

  5. 5.

    The latest changes reflect the decisions made during the IAEG-SDG WebEx Meeting in January 2019. The tier classification of many indicators is expected to change as methodologies are developed and data availability increases. The review of reclassification requests by the IAEG-SDGs will occur when requirements are met at the two physical meetings and via WebEx meetings throughout the year, based on a calendar developed by the Group.

  6. 6.

  7. 7.

    Data presented in this study were mined in September 2018.


  1. Anand, S., & Sen, A. (2000). Human development and economic sustainability. World Development, 28, 2029–2049.

  2. Bali Swain, R., & Wallentin, F.-Y. (2017). The sustainable development quagmire: Quantifying the sustainable development goals. Unpublished draft, Södertörn University & Stockholm School of Economics.

  3. Bettencourt, L. M.-A., & Kaur, J. (2011). Evolution and structure of sustainability science. Proceedings of the National Academy of Sciences, 108(49), 19540–19545.

  4. Bilbao-Ubillos, J. (2013). The limits of Human Development Index: The complementary role of economic and social cohesion, development strategies and sustainability. Sustainable Development, 21(6), 400–412.

  5. Bithas, K. P., & Christofakis, M. (2006). Environmentally sustainable cities. Critical review and operational conditions. Sustainable Development, 14(3), 177–189.

  6. Böhringer, C., & Jochem, P. E.-P. (2007). Measuring the immeasurable—A survey of sustainability indices. Ecological Economics, 63(1), 1–8.

  7. Cataldo, R., Grassia, M. G., Lauro, N. C., & Marino, M. (2017). Developments in higher-order PLS-PM for the building of a system of composite indicators. Quality & Quantity, 51(2), 657–674.

  8. Chin, W. W. (1998). The partial least squares approach to structural equation modeling. In G. A. Marcoulides (Ed.), Modern business research methods (pp. 295–336). Mahwah, NJ: Lawrence Erlbaum Associates.

  9. Daly, H., Cobb, Jr. J. B., & Cobb, J. B. (1994). For the common good: Redirecting the economy toward community, the environment, and a sustainable future. Boston: Beacon Press.

  10. Davino, C., Dolce, P., Taralli, S., & Vinzi Esposito, V. (2018). A quantile composite-indicator approach for the measurement of equitable and sustainable well-being: A case study of the italian provinces. Social Indicators Research, 136(3), 999–1029.

  11. De Smedt, M., Giovannini, E., & Radermacher, V. (2018). Measuring sustainability, for good measure advancing research on well-being metrics beyond GDP: Advancing research on well-being metrics beyond GDP (Vol. 241). Paris: OECD Publishing.

  12. Dunteman, G. H. (1989). Principal components analysis (Vol. 69). Sage.

  13. Estoque, R. C., & Murayama, Y. (2014). Social–ecological status index: A preliminary study of its structural composition and application. Ecological Indicators, 43, 183–194.

  14. Evans, A., Strezov, V., & Evans, T., et al. (2015). Measuring tools for quantifying sustainable development. Rome: European Center of Sustainable Development.

  15. Fabbrizzi, S., Maggino, F., Marinelli, N., Menghini, S., Ricci, C., & Sacchelli, S. (2017). Sustainability: A quantitative discourse analysis. Rivista di studi sulla sostenibilità.

  16. Gallopín, G. C. (2003). A systems approach to sustainability and sustainable development (Vol. 64). United Nations Publications.

  17. Gan, X., Fernandez, I. C., Guo, J., Wilson, M., Zhao, Y., Zhou, B., et al. (2017). When to use what: Methods for weighting and aggregating sustainability indicators. Ecological Indicators, 81, 491–502.

  18. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2009). Multivariate data analysis. Upper Saddle River, NJ: Pearson Prentice Hall.

  19. Hair, J. F., Hult, T., Ringle, C. M., & Sarstedt, M. (2014). A primer on partial least squares structural equation modeling (PLS-SEM). Thousand Oaks, CA: Sage.

  20. Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19, 139–151.

  21. Hák, T., Janoušková, S., & Moldan, B. (2016). Sustainable development goals: A need for relevant indicators. Ecological Indicators, 60, 565–573.

  22. Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. In New challenges to international marketing (pp. 277–319). Emerald Group Publishing Limited.

  23. Hermans, E., Van den Bossche, F., & Wets, G. (2008). Combining road safety information in a performance index. Accident Analysis & Prevention, 40(4), 1337–1344.

  24. Hopwood, B., Mellor, M., & O’Brien, G. (2005). Sustainable development: Mapping different approaches. Sustainable Development, 13(1), 38–52.

  25. ICSU, ISSC. (2015). Review of the sustainable development goals: The science perspective. Paris: International Council for Science (ICSU).

  26. International Union for Conservation of Nature and World Wildlife Fund. (1980). World conservation strategy: Living resource conservation for sustainable development. Gland: IUCN.

  27. Joint Research Centre-European Commission, et al. (2008). Handbook on constructing composite indicators: Methodology and user guide. Paris: OECD Publishing.

  28. Jarvis, D., MacKenzie, S., & Podsakoff, P. (2003). A critical review of construct indicators and measurement model misspecification in marketing and consumer research. Journal of Consumer Research, 30(3), 199–218.

  29. Johnson, R. E., Rosen, C. C., Chang, C. H., Djurdjevic, E., & Taing, M. U. (2012). Recommendations for improving the construct clarity of higher-order multidimensional constructs. Human Resource Management Review, 22(2), 67–72.

  30. Kates, R. W. (2011). What kind of a science is sustainability science? Proceedings of the National Academy of Sciences, 108(49), 19449–19450.

  31. Kyoto Protocol. (1997). United Nations framework convention on climate change (vol. 19). Kyoto Protocol, Kyoto.

  32. Lauro, C. N., Grassia, M. G., & Cataldo, R. (2018). Model based composite indicators: New developments in partial least squares-path modeling for the building of different types of composite indicators. Social Indicators Research, 135(2), 421–455.

  33. Lohmöller, J. B. (2013). Latent variable path modeling with partial least squares. Berlin: Springer.

  34. Mazziotta, M., & Pareto, A. (2019). Use and misuse of PCA for measuring well-being. Social Indicators Research, 142(2), 451–476.

  35. Michalos, A. C. (2014). Encyclopedia of quality of life and well-being research. Dordrecht: Springer.

  36. Moran, D. D., Wackernagel, M., Kitzes, J. A., Goldfinger, S. H., & Boutaud, A. (2008). Measuring sustainable development—Nation by nation. Ecological Economics, 64(3), 470–474.

  37. Mori, K., & Christodoulou, A. (2012). Review of sustainability indices and indicators: Towards a new City Sustainability Index (CSI). Environmental Impact Assessment Review, 32(1), 94–106.

  38. Nardo, M., & Saisana, M. (2008). OECD/JRC handbook on constructing composite indicators. Putting theory into practice. Brussels: European Commission—Joint Research Centre; Institute for the Protection and Security of the Citizen Unit of Econometrics and Applied Statistics.

  39. Neumayer, E. (2010). Weak versus strong sustainability: Exploring the limits of two opposing paradigms (3rd ed.). Cheltenham: Edward Elgar Publishing.

  40. Neumayer, E. (2007). Sustainability and well-being indicators. In Human well-being (pp. 193–213). London: Palgrave Macmillan.

  41. Nicolai, S., Hoy, C., Berliner, T., & Aedy, T. (2015). Projecting progress: Reaching the SDGs by 2030. London: Overseas Development Institute.

  42. Pradhan, P., Costa, L., Rybski, D., Lucht, W., & Kropp, J. P. (2017). A systematic study of sustainable development Earth’s future. Wiley Online Library, 5(11), 1169–1179.

  43. Rajala, R., & Westerlund, M. (2010). Antecedents to consumers’ acceptance of mobile advertisements: A hierarchical construct PLS structural equation model. In XLIIIth Hawaii International Conference on Systems Sciences (HICSS).

  44. Rodriguez-Takeuchi, L. (2014). The ‘X-file’ and the need for civil registration and vital statistics systems post-2015. London: ODI.

  45. Sachs, J. D. (2012). From millennium development goals to sustainable development goals. The Lancet, 379(9832), 2206–2211.

  46. Sachs, J., Schmidt-Traub, G., Kroll, C., Durand-Delacre, D., & Teksoz, K. (2016). SDG index & dashboards: A global report. Gütersloh: Bertelsmann Stiftung.

  47. Salzman, J. (2003). Methodological choices encountered in the construction of composite indices of economic and social well-being. Ottawa, ON: Ottawa Center for the Study of Living Standards.

  48. Sanchez, G. (2013). PLS path modeling with R (Vol. 383). Berkeley: Trowchez Editions.

  49. Sanchez, G., & Trinchera, L. (2012). plspm: Partial Least Squares data analysis methods. R package version 0.2-2.

  50. Smith, L. I. (2002). A tutorial on principal components analysis.

  51. Solow, R. M. (1994). Perspectives on growth theory. Journal of Economic Perspectives, 8(1), 45–54.

  52. Spaiser, V., Ranganathan, S., Swain, R. B., & Sumpter, D. J. T. (2017). The sustainable development oxymoron: Quantifying and modelling the incompatibility of sustainable development goals. International Journal of Sustainable Development & World Ecology, 24(6), 457–470.

  53. Stiglitz, J. E., Sen, A., & Fitoussi, J. P. (2017). Report by the commission on the measurement of economic performance and social progress.

  54. Strezov, V., Evans, A., & Evans, T. J. (2017). Assessment of the economic, social and environmental dimensions of the indicators for sustainable development. Sustainable Development, 25(3), 242–253.

  55. Stuart, E., Samman, E., Avis, W., & Berliner, T. (2015). The data revolution: Finding the missing millions. London: Overseas Development Institute.

  56. Swain, R. B. (2018). A critical analysis of the sustainable development goals. In Handbook of sustainability science and research (pp. 341–355). Cham: Springer.

  57. Tenenhaus, M. (1998). La régression PLS: théorie et pratique. Editions technip.

  58. Tenenhaus, M., Esposito Vinzi, V., Chatelin, Y. M., & Lauro, C. N. (2005). PLS path modeling. Computational Statistics and Data Analysis, 48(1), 159–205.

  59. Trinchera, L., Russolillo, G., & Lauro, C. N. (2008). Using categorical variables in PLS path modeling to build system of composite indicators. Statistica Applicata, 20(2), 309–330.

  60. United Nations. (2009). Millennium Development Goals Report 2009 (Includes the 2009 Progress Chart). United Nations Publications.

  61. United Nations. (2015a). Transforming our world: The 2030 agenda for sustainable development. General Assembly 70 session.

  62. United Nations. (2015b). United Nations Conference on Sustainable Development, Rio+20.

  63. United Nations Development Programme. (1992). Earth Summit 1992: The United Nations Conference on Environment and Development, Rio de Janeiro, 1992.

  64. United Nations Development Programme. (2010). Human development report 2010. Palgrave Macmillan.

  65. United Nations Statistical Commission, et al. (2017). Global indicator framework for the Sustainable Development Goals and targets of the 2030 Agenda for Sustainable Development. UN Resolution A/RES/71/313.

  66. WCED, SPECIAL WORKING SESSION. (1987). World commission on environment and development. Our common future (vol. 17, pp. 1–91). Oxford University Press, London.

  67. Wetzels, M., Odekerken-Schröder, G., & van Oppen, C. (2009). Using PLS path modeling for assessing hierarchical construct models: Guidelines and empirical illustration. MIS Quarterly, 33(1), 177–195.

  68. Wilson, B. (2009). Using PLS to investigate interaction effects between higher order brand constructs. In Esposito, Vinzi, V., Chin, W. W., Henseler, J., Wang, H. (Eds) Handbook of partial least squares: Concepts, methods and applications in marketing and related fields. Berlin: Springer.

Download references

Author information

Correspondence to Rosanna Cataldo.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Cataldo, R., Crocetta, C., Grassia, M.G. et al. Methodological PLS-PM Framework for SDGs System. Soc Indic Res (2020).

Download citation


  • Composite indicators
  • Higher-order construct
  • PLS-path modeling
  • Sustainable development goals