A Support Vector Machine approach for predicting progress toward environmental sustainability from information and communication technology and human development

  • Milena Lipovina-Božović
  • Ljiljana KašćelanEmail author
  • Vladimir Kašćelan


Human activities are increasingly affecting the planet and its sustainability by degrading and damaging the environment. The literature on this topic has demonstrated that Information and Communications Technology (ICT) and human development (HD) are important promoters of progress towards environmental sustainability. The impact of these factors is most often examined by using standard regression analysis which suffers from the problems of multicollinearity and non-linear dependency. In order to resolve this problem, a non-parametric method is proposed. To be specific, a Support Vector Machine (SVM) model for predicting environmental performance growth has been developed, based on various predictors- ICT and HD indexes, population growth, and an economic development indicator. The prediction is made at the macro level using a sample of 139 countries. The model was created by a prediction procedure consisting of the optimization of the SVM learner parameters using the grid-search method, as well as k-fold cross-validation. A predictive accuracy of the SVR model of 80.4% was achieved. The model predicts growth in environmental performance of 1.5% for each 1% increase in the ICT index, while an increase of the HD index of 1% produces an environmental performance increase of 4.3%. The results of the sensitivity analysis confirm that the effects of both predictors are enhanced when they operate in interaction. This the first study to apply the predictive machine learning method to the analysis of the impact of ICT and HD on environmental performance and empirically confirmed its efficiency. The obtained results contribute to the existing literature and could be beneficial to policy makers working in sustainable development.


Environmental performance Human development ICT Sensitivity analysis Support vector regression 


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Authors and Affiliations

  1. 1.Faculty of EconomicsUniversity of MontenegroPodgoricaMontenegro

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