A Comparative Analysis of Countries' Performance According to SDG Indicators based on Machine Learning

  • Guilherme Souza UFRJ
  • Julian Santos UFRJ
  • Gabriel SantClair UFRJ
  • Janaina Gomide UFRJ
  • Luan Santos UFRJ

Resumo


The Sustainable Development Goals (SDGs) are part of a global effort to reduce the impacts of climate change, promoting social justice and economic growth. The United Nations provides a database with hundreds of indicators to track the SDGs since 2016 for a total of 302 regions. This work aims to assess which countries are in a similar situation regarding sustainable development. Principal Component Analysis was used to reduce the dimension of the dataset and k-means algorithm was used to cluster countries according to their SDGs indicators. For the years of 2016, 2017 and 2018 were obtained 11, 13 and 11 groups, respectively. This paper also analyses clusters changes throughout the years.

Referências

Bishop, C. M. (2006). Pattern recognition and Machine Learning. Springer.

Booth, L. (2010). Targets as a policy tool. [link]. Accessed: 2021-08-03.

Davis, K. E., Kingsbury, B., and Merry, S. E. (2012). Indicators as a technology of global governance. Law & Society Review, 46(1):71–104.

Ferreira, B., Iten, M., and Silva, R. (2020). Monitoring sustainable development by means of earth observation data and machine learning: a review. Environ Sci Eur, 32(120).

Géron, A. (2019). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems. O’Reilly Media.

in China, U. N. (2015). Sustainable development goals officially adopted by 193 countries. http://www.un.org.cn/info/6/620.html. Accessed: 2021-07-13.

Jabbari, M., Motlagh, M. S., Ashrafi, K., and Abdoli, G. (2019). Differentiating countries based on the sustainable development proximities using the sdg indicators. Environment, Development and Sustainability, pages 1–19.

Kijewska, A. and Bluszcz, A. (2016). Research of varying levels of greenhouse gas emissions in european countries using the k-means method. Atmospheric Pollution Research, 7(5):935–944.

Lim, S. S., Allen, K., Bhutta, Z. A., Dandona, L., Forouzanfar, M. H., Fullman, N., Gething, P. W., Goldberg, E. M., Hay, S. I., Holmberg, M., et al. (2016). Measuring the health-related sustainable development goals in 188 countries: a baseline analysis from the global burden of disease study 2015. The Lancet, 388(10053):1813–1850.

Madley-Dowd, P., Hughes, R., Tilling, K., and Heron, J. (2019). The proportion of missing data should not be used to guide decisions on multiple imputation. Journal of clinical epidemiology, 110:63–73.

Raszkowski, A. and Bartniczak, B. (2019). On the road to sustainability: Implementation of the 2030 agenda sustainable development goals (sdg) in poland. Sustainability, 11(2).

Rousseeuw, P. J. (1987). Silhouettes: a graphical aid to the interpretation an validation of cluster analysis. Journal of Computational and Applied Mathematics, pages 53–65.

Santos, L. and Santos, T. (2017). Os ods e seus indicadores: novas classes gramaticais, uma mesma morfologia. Pontes, 13:13–16.

Shahbaz, M., Sharma, R., Sinha, A., and Jiao, Z. (2021). Analyzing nonlinear impact of economic growth drivers on co2 emissions: Designing an sdg framework for india. Energy Policy, 148:111965.
Publicado
29/11/2021
Como Citar

Selecione um Formato
SOUZA, Guilherme; SANTOS, Julian; SANTCLAIR, Gabriel; GOMIDE, Janaina; SANTOS, Luan. A Comparative Analysis of Countries' Performance According to SDG Indicators based on Machine Learning. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 18. , 2021, Evento Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2021 . p. 141-152. DOI: https://doi.org/10.5753/eniac.2021.18248.

Artigos mais lidos do(s) mesmo(s) autor(es)